Efficient and Reproducible Temperature Forecasting Using CNN-BiGRU Based Architectures

Introduction

Weather forecasting has always been a very critical aspect to human society being the determinant of decisions that may be on a scale varying between the ordeal and the life-saving. People have been weather watching by choosing times to plant or harvest crops and travel during a fair weather or during a time of low winds. The significance of the weather prediction has become even more critical in the contemporary world since it has an immediate impact on agriculture and transport, disaster management and urban planning. With forecasts farmers decide on irrigation schedules, choice of crop management practices. They are critical to airlines and shipping industries in terms of planning safe and expedient paths. Local governments and first responders are counting on the early warnings to plan ahead of hurricanes, floods and heat-waves and urban planners are factoring in the forecasts into building designs, infrastructure design and building of more resilient structures. Absence of precise forecasts would leave societies exposed to environmental uncertainty, which leads to losses in economies, interference of routine, and human lives in danger [1], [2], [3].

Numerical Weather Prediction (NWP) has been the basis of modern forecasting over decades. In NWP, complex mathematical equations that describe the processes inside the atmosphere are solved: in other words, it is precisely the processes of temperature changes, wind movability and moisture distribution which are mathematically described. This solution has been termed as a revolution of silence since it has brought tremendous changes to meteorology over the years. Advances in forecast modelling, ensemble forecasting and data assimilation procedures have continued to advance the accuracy and reliability of forecasts, such that a significant improvement has been made with predictability over time unlike what was previously achievable [1], [2]. An example is ensemble forecasting that uses multiple simulations to take into account the uncertainties and provide more reliable forecasts; another example is data assimilation which takes massive observational data combined with model results to increase accuracy. Yet, despite these advances, NWP has significant limitations. It is computationally costly in that it demands a massive volume of processing and specialized super-computers before it can be executed with ease. What is more, these models tend to fail at the reproduction of small-scale processes, e.g. localized thunderstorm activity, cloud microphysics, or changes in wind direction that occur abruptly. This fact has led NWP to continue to play a major role in meteorology, even though there has been a surge to seek additional methods [1], [3].

The fast moving development of Artificial Intelligence (AI) and Deep Learning (DL) has brought opportunities to form solutions to these challenges. Contrary to customary approaches that rely on physics incorporated in the definition of equations, AI is data-based. It can train on large and varied data that comprise satellite imagery, ground-based sensors, radar measurements, and long-term history data. Based on this information, the AI systems can discover patterns that are not necessarily expressed in physical equations yet necessary to make accurate predictions. The move of equation-based modeling to data-driven modeling makes AI an appealing replacement or supplement to more conventional forecasting [3], [19]. Another advantage of AI approaches is that it is more computationally efficient, since they usually take less time and fewer resources but they still provide valid results. It has even been demonstrated in some situations that they have higher accuracy, and faster turnaround times than their analogues, the conventional NWP systems [1], [3].

The application of deep learning architectures to the improved accuracy of weather and climate forecast has grown in considerable numbers in recent research. For example, Furizal et al. [4] presented a thorough comparison of LSTM and GRU predictions for temperature time series with a focus on the ability of the gated recurrent method of these models to learn nonlinear temporal dependencies between the elements of the time series forecast. Convolutional Neural Networks (CNNs) have also gained interest due to their ability to extract spatial features from meteorological datasets that can be used for weather forecasting. Li et al. [5] demonstrated that CNN-based models are especially effective at detecting localized atmospheric patterns that are particularly important for short-term weather analyses. These recent studies also provide evidence to suggest that combined learning of residual temporal and spatial features will prove to be critical for accurate weather predictions.

Hybrid deep learning models that are designed to capture both temporal and spatial features of upper-air atmospheric data have been developed from the above advancements. For example, Zhang et al. [6] proposed a novel hybrid architecture of CNN-LSTM for predicting monthly climate with evidence that models of this nature exceed the predictive skill of individual (standalone) architectures. Similarly, Wang et al. [7] performed a thorough evaluation of CNN-LSTM models and confirmed the efficacy of using these hybrid models for modelling the characteristics of the complex atmosphere. Recently, transformer-based architectures have become available that offer a strong alternative to the recurrent model of architecture discussed above. The Evidence from Lam et al. [10] shows how attention-based transformer models excel in predicting mid-term weather patterns, while significantly Lowering computational load requirements compared to other techniques. This supports further research into Hybrid and advanced deep learning models for Weather Predictability.

As can be seen from the variety of deep learning architectures applied in weather predictions. Convolutional Neural Networks CNNs are known to be highly efficient when it comes to spatial patterns recognition on maps, satellite images, etc., and are used to count cloud cover or map precipitation.

Atmospheric data consists of time series that can be modelled by recurrent neural networks RNNs are efficient tools to model time series and consider the time dependency in data. Traditional RNNs are not able to represent long-term critical dependencies[17] and therefore more sophisticated versions based on LSTM networks[13] and GRUs[18] have been developed. The proposed architecture addresses the problem of vanishing gradients and can indeed well represent intricate, long-range temporal dependencies in weather data.

In recent experiments scientists have tried a combination of techniques in what have become known as the hybrid models.For example, CNN–LSTM hybrid [6], [7] and CNN–BiGRU approach [6], [7] integrate spatial and temporal learning and therefore are able to consider spatial and temporal features in atmosphere data. They have shown outstanding promise in predicting highly dynamic weather patterns.

Artificial intelligence that finds uses in meteorology already achieved remarkable results in many fields. In some of them, as short-term nowcasting, practitioners train automated models to read high-resolution radar and satellite information to predict rainfall, storms, motion of clouds within a few hours to provide useful information in urban flood control and traffic management[8] . Within the field of climate monitoring, deep learning algorithms also found use in detecting anomalies, such as sudden warming or sudden, unexpected changes in precipitation as well as extreme droughting. It also shows promise in long term/decadal prediction, where it has been used to reduce systematic errors and improve reliability of climate models. In numerous of them, AI-based models have proven their success over even high-resolution convective-permitting weather models, especially in the fields of precipitation forecasting, weather downscaling and anomaly detection[2,3,4].

The growing interest in hybrid approaches that combine AI and physics-based NWP is arguably the most exciting one. Many researchers see AI as a powerful complement to, rather than replacement of, conventional forecasting. AI can be applied as a post-processing layer to increase resolution and/or reduce biases of NWP outputs. As an alternative, AI can be directly embedded into physical models as a parameterization method to help represent atmospheric processes at small scales that are notoriously hard to model, such as cloud microphysics or turbulence[1,10,11]. Multiple benefits of the two approaches that these hybrid strategies seize are interpretable and theoretic grounds of physics-based models as well as flexibility and efficiency in learning using AI. Such a balance is not only increasing accuracy, but also develops trust among the scientists and the policymakers who still use forecasts to base their decisions on.

This paper extends the existing body of research by evaluating the predictive performance of several deep learning models for weather forecasting. Figure 1 illustrates the workflow of the proposed weather forecasting model. With the Seattle weather data set which includes characteristics of weather such as precipitation, maximum and minimum temperature, and wind speed we want to predict weather phenomena such as drizzle, rain, sun, snow and fog. The models we will be examining are Vanilla RNN, CNN, GRU, LSTM, GRU-LSTM Combinations, BiGRU-RNN, BiGRU-LSTM, and CNN-BiGRU. The architectures we have chosen cover a wide range of deep learning solutions from the most simple recurrent solution to the more hybrid solution of learning in space and time. We use standard error performance measurements: Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). These measurements will allow us to quantitatively determine the relative accuracy levels of the different models, whereas the visualization of the validation and test data sets will give us additional information on the existence of the differences in accuracy.

The results of this research have shown that among all models examined CNN–BiGRU is the most efficient in terms of prediction. CNN's ability to deal with spatial as well as temporal learning enables it to better simulate the complexity of the atmospheric process than other models examined. This result is in accordance with the field, where hybrid deep models are being identified as state-of-the-art models in the field of weather forecast [6,7,15]. The results of CNN-BiGRU in the present study also show that artificial intelligence can lead to the enhancement of conventional methods of forecast and perhaps to usurp.

The results of the study show that innovations through the use of AI are changing the face of meteorology. With the ability to provide efficient, accurate, and scalable alternatives to physics-based models, AI enables us to find new ways of forecasting in the long term, medium term, and short term. Although NWP is likely to remain a cornerstone of meteorology in the coming years, the advent of AI, especially in hybrid methods, is an important step on the way to revolutionise weather forecasting [1,3,10,11]. Together, these innovations enable more accurate and reliable forecasts while providing deeper insights into the atmospheric systems that govern climate behaviour. Figure 2 presents the architecture of the proposed deep learning-based weather forecasting system.

Comparative Review of Existing Methods

Recent findings in weather forecasting research have indicated that there is a paradigm shift in modelling from traditional physics-inspired models to data-intensive models, especially with more sophisticated approaches in deep learning. The existing weather models are based on deep academic knowledge in physics but are computationally intensive and unscaleable regarding processing high-dimensional spatial and temporal information [1], [2]. Deep learning models have presented potential alternatives in modeling weather forecasts because of their potential in automating knowledge acquisition in high dimensions from big datasets [3], [15]. Gated recurrent models have presented potential in modeling weather time series forecasting because of their effectiveness in modeling dynamics in weather phenomenon time series [4], [13], [17]. The convolutional models have also presented potential in modeling spatial patterns in weather observations, especially with spatial images [5], [8]. Current models and findings have indicated that modeling in weather forecasting with both spatial and temporal models presents superior performance to models that are either solely spatial or purely temporal in nature [6], [7], [14]. Recent models in weather forecasting also indicate that new models such as transformers and probability foundation models are redefining weather forecasting in medium-range forecasting with high accuracy and efficiency [10], [11], [12].

1. Traditional Approaches to Weather Forecasting

Predicting the weather was traditionally a physics-based process known as Numerical Weather Prediction (NWP), which attempts to solve a group of partial differential equations (PDEs) describing the underlying physics in the atmosphere [3,16]. The essential physical processes (fluid motion, thermodynamics and radiative transfer) are taken into account in these equations that determine the evolution of atmospheric variables with time. In practice, NWP systems divide up the atmosphere into a 3-dimensional grid, solve the governing equations numerically over their millions of grid points to estimate how the atmosphere will be at future model time steps. This approach has been the root of operational weather forecasting for decades and emerges as the basis of most global and regional weather centers [1,2]. However, the inherent complexity of these models makes them computationally demanding, requiring access to supercomputers and high-performance computing resources [10,12]. Even with such resources, forecasts are often restricted by limited temporal resolution, making it harder to generate real-time prediction in such a short span of time. A more fundamental limitation of NWP arises from its sensitivity to initial conditions. Because the atmosphere is so complex in nature that even very small errors or uncertainties occurring at the very beginning of the measurement phase can result in massive error which may make the outcome so undesirable [17]. After a few days it becomes less feasible and trustworthiness of the data will be reduced unexpectedly. Additionally, the grid-based discretization introduces scale limitations, which in simple terms means the chances of localised weather phenomenon thunderstorms, cyclones, tornadoes, and flash floods detection is completely missed or the percentage is reduced to great extent if their scale is smaller than the model’s resolution [2,3]. This leads to the more serious outcome for disaster readiness and risk management as many of the most destructive weather events occur at precisely these smaller scales. Before the rise of modern machine learning techniques, traditional statistical methods were also widely used in weather forecasting. Examples include autoregressive integrated moving average (ARIMA) models, regression-based approaches, and Markov models [16]. This classical method tries to capture the pattern in past time series data to probably guess and accurately encounter the future values. Though computationally less demanding and simpler to apply than NWP, they had a poor track record in representing the atmospheric dynamics that are nonlinear, highly chaotic, and high dimensional [2,15]. As an example, climate variables that include temperature, precipitation and wind speed depend not only on previous values but also complicated interactions with other variables over space as well as time. Classical models, being adaptively linear or low-dimensional in their root form, lacked the flexibility to capture these multiple small parts of interrelated complicated pattern dependencies [3]. This meant that they could only be effectively applied in short-term or single-variable forecasts and often to lower accuracy in multi-variable or long-range predictions. These shortcomings of both physics-based and statistical approaches paved the way to the creation of data-driven models, especially those that use deep learning [2,15]. Unlike classical approaches that require human supervision to set the rules to estimate the effect, leveraging of large-scale historical datasets such as satellite imagery, radar observations or climate reanalysis products enables deep learning models to learn the hidden spatiotemporal patterns in the data automatically [3,5]. The architectures of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) enable the consideration of both spatial characteristics (e.g., pressure systems, cloud formation) as well as temporal ones (e.g., daily cycle, seasonal patterns) [4,5]. Adding more to these models significantly reduces computational overhead compared to NWP, enabling faster forecasts while maintaining high accuracy [10,12]. More relevantly, that deep learning methods also raise the prospect of probabilistic and ensemble-like forecasting, and thus come to provide not only predictions, but also estimates of uncertainty [11]. Beyond this, it is a significant advance in the direction of more powerful, real time and localized weather forecasting which is more important in the context of rising climate variability and extreme weather events [1,15].

2. Recurrent Architectures: LSTM and GRU

Recurrent Neural Networks (RNNs) pose the first major paradigm shift in the use of artificial intelligence to make weather predictions as RNNs do provide capabilities in areas that the previous statistical models did not [16]. In contrast to feedforward neural networks, in which the inputs are considered independent of one another, RNNs are explicitly sequential in that they have internal hidden states that constitute a form of memory. This characteristic enables the RNNs to memorize patterns in either previous time periods and project them into future predictions, making RNNs highly appropriate in time-series weather data like temperature, humidity, wind velocity and atmospheric pressure [17]. For example, the prediction of tomorrow’s temperature can be informed not just by today’s conditions, but by how the sequence of weather variables has evolved over the past several days. Despite their initial promise, classical RNNs encountered significant limitations, most notably the issues of vanishing and exploding gradients during training [17]. When attempting to capture long-term dependencies in weather records, the gradients required for updating network weights often became either too small (vanishing) or excessively large (exploding), which caused the training process to become unstable and hindered the model’s ability to learn long sequences effectively [17]. To overcome these challenges, more advanced RNN variants were developed, the most notable being the Long Short-Term Memory (LSTM) [13] and the Gated Recurrent Unit (GRU) [18].

Convolutional Neural Networks and Hybrid Models

RNNs have proved to be very effective at learning temporal dynamics; yet, they are fundamentally constrained in simulating spatial features that are no less important in weather forecasting. Atmospheric systems often entail complex spatial patterns such as cloud formations, the eye walls of cyclones, jet streams, and pressure systems. These structures change over time and have an impact on regional and global climate outcomes. On the other hand, by applying convolutional filters to multi-dimensional data, Convolutional Neural Networks (CNNs), which were first made popular in the field of computer vision, are well suited to extracting spatial dependencies. This renders CNNs especially useful in meteorological applications where data are often represented in multi-channel images or maps retrieved with satellite-derived data, radar systems or through reanalysis data. CNNs can interpret the localised features of a spatial feature; i.e. clouds clusters in region, cyclone spirals bands, and frontal boundaries with temperature gradients can be identified as a localised feature [5]. While being good at pattern recognition, convolutional neural networks (CNNs) are static and not able to capture temporal dynamics of atmospheric phenomena. For example, while a CNN can identify an incipient storm cell, it cannot single-handedly predict the movement and strengthening of the same over a period of time. To overcome this shortcoming, researchers came up with hybrid architectures combining CNNs with temporal modeling paradigms like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU). These models both leverage the spatial feature extraction prowess of CNNs and effectively combine the temporal memory benefit of recurrent neural network (RNN) variants to enable modeling of meteorological data both in the spatial and temporal realms. Zhang et al. [6] demonstrated that a CNN-LSTM model performed better than both single CNN and LSTM configurations in the domain of monthly climate prediction for Jinan City, specifically in predicting extreme temperature fluctuations where spatial and temporal correlations were both of critical significance.. Similar to this, Wang et al. [7] compared CNN-LSTM architectures for dynamic weather prediction, showing how adaptable they are in better capturing short-term variability, wind patterns, and precipitation trends than conventional techniques. These findings suggest that hybrid models enhance robustness in forecasts of a wide variety of meteorological variables besides enhancing their predictive accuracy. These mixed models have been particularly applied to the prediction of weather over shorter to medium ranges, and of regional climate trends that combine in a time-varying pattern with the local nature of the atmosphere. As an example, in areas prone to floods, CNN-LSTM architectures have been used to process a sequence of satellite imagery in prediction of rainfall accumulation, and in renewable energy, CNN-GRU models have been readily used to help predict wind speed that would aid in terms of generation of wind power. The increased popularity of these architectures is part of a larger shift within meteorology toward integrative deep learning which mixes the strengths of different model families to overcome the individual weaknesses. Through simultaneous learning of the location at which atmospheric features exist and their changes over time, hybrid CNN-RNN systems enable the addition of an effective basis to produce sophisticated forecast technologies to account to the spatio-temporal complexity of weather systems. Figure 3 shows the structural design of the convolutional neural network (CNN) model used for extracting local temporal features from the temperature time series.

Advanced Architectures: ConvLSTM, Transformers, and Capsule Networks

Based on the representational advancements of hybrid structures, which consist of convolutional and recurrent networks, researchers have led towards further neural net designs with the ability to model the complete time-space domain of weather forecasting. A powerful innovation is the Convolutional Long Short-Term Memory (ConvLSTM) network, that advances the conventional LSTM, substituting the multiplications of matrices by convolutional operations.

This change preserves temporal dependencies while enabling ConvLSTM to process spatial data sequences directly, such as satellite imagery or radar precipitation maps. ConvLSTM preserves the two-dimensional relationships within meteorological maps, which makes it especially well-suited for precipitation nowcasting and storm tracking. This is in contrast to standard LSTMs, which flatten spatial structures into one-dimensional sequences. Shi et al. [8] presented one of the first studies of ConvLSTM, which showed that this model performed better than LSTM models in predicting short-term rainfall, proving that it could be used practically in flood management operations and to prepare a community in the event of a disaster. Along with ConvLSTM, researchers also experimented with the use of Capsule Networks, an architecture first proposed to address the limitations that CNNs have in identifying hierarchical patterns. Capsule Networks: proposed by Sabour et al., can introduce what is called capsules, which are groups of neurons, able to incorporate not only the existence of features, but their spatial orientation and association.

This renders them especially well-suited to capture the multi-scale, hierarchical interdependencies of atmospheric systems. Capsule networks in meteorology have been applied to the downscaling of coarse-resolution forecasts, essentially translating predictions made at a global scale to high-resolution local weather maps [9]. This functionality is of high value across a range of applications, such as urban microclimate modeling, agriculture planning, and renewable energy forecasting, where high-precision prediction is essential. Perhaps the most groundbreaking development of the past few years has been the application of Transformer-based architectures to the domain of weather forecasting. As opposed to recurrent models, transformers avoid sequential constraints through the use of self-attention mechanisms, allowing them to learn long-range temporal dependencies without suffering from the problems of vanishing gradients or incremental processing. This architecture has been shown to be exceptionally powerful in dealing with large, multi-variable climate data over a period of several decades. Lam et al. [10] showed not only that transformer-based models could surpass the accuracy of the traditional ECMWF deterministic forecasts when applied to 7-14 days predictions, but also that models could provide a much-improved computation time.

5. Generative Models and Foundation Approaches

Generative and foundation models have the potential to radically transform AI weather forecasting from deterministic single trajectories to stochastic fields capturing uncertainty in the atmosphere more accurately. Instead of predicting one path, they produce multiple plausible scenarios.

Google DeepMind’s GenCast for instance, ran in minutes compared to hours used by ECMWF ensemble forecasts for which it outperformed in 97% of the cases [11]. FourCastNet, a Fourier neural operator, can do week-long global forecasts in seconds with the accuracy of NWP forecasts [12].

Foundation models pretrained on large datasets can be adapted to a variety of tasks from cyclone tracking to precipitation nowcasting to climate trend prediction. Their flexibility can be applied in other forecast-intensive sectors such as disaster management, aviation, energy, and agriculture which are losing a lot of money and even lives yearly. However, to do it, foundation models need to become validated, interpretable and trust-worthy. These risks are overfitting to historical extremes, failure to respond to unprecedented extremes and poor generalization in areas that have little observations. To achieve long-term success we should benchmark, solve physical knowledge and partner across national boundaries.

6. Interpretability, Uncertainty, and Limitations

However, the application of deep learning models in operational forecasts of weather is still hampered by several critically challenging issues. Interpretability is perhaps one of the most critical issues of the black box neural networks with their opaque internal decision-making process. Meteorologists, policy makers and emergency planners often need to explain their predictions especially when they are responsible for making decisions to allocate resources or when it comes to putting people’s lives and safety into danger. Lack of transparency may lead to consumers questioning highly accurate predictions especially when results vary significantly from those from Numerical Weather Prediction models (NWP). Another challenge, which researchers overcame by incorporating explainable AI (XAI) methods into the meteorological forecasting workflow, is that people also cannot explain why the forecast was made. Tools like SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), or Grad-CAM (Gradient-weighted Class Activation Mapping) have been used to provide information on what has influenced the predictions. These methods highlight certain variables, i.e. sea surface temperature anomalies, relative humidity, or pressure gradients, thus providing some interpretability and so experts can trust and verify the outputs from the neural networks further. Another critical issue that we still need to address is uncertainty determination, especially for the extreme weather conditions such as cyclones, hurricanes, or flash floods. These phenomena are largely rare and underrepresented in the available training datasets, which is also a common phenomenon that the deep learning model cannot perform well when applied to predict these kinds of phenomena. Since they are one of the most severe extreme weather events in terms of the need to make decisions in disaster preparedness and climate adaptation, they can have disastrous consequences due to their possible under-estimation or mis-prediction.

The other pitfalls are geographical generalization: manufactured with regards to training in Europe or North America may degenerate with low coverage in the tropics and poles. Opportunities include transfer learning, domain adaptation, large-scale global pretraining, and open-access international repositories.

In summary, deep learning achieves both accuracy and efficiency, but addressing interpretability, uncertainty, and regional biases to build reliable and scalable operational forecasting systems remains an opportunity

Methodology

1. Dataset Description

The data used in the study is the Seattle Weather Data which contains information on the daily meteorological observation used between January 2012 and December 2015. The data consists of about 1461 daily records that have five main attributes representing the amount of precipitation in millimeters, maximum daily temperature in different degrees Celsius, minimum daily temperature in different degrees Celsius, wind speed, and a numeric attribute indicating the weather forecast such as drizzle, rain, sun, snow, or fog.

Since this records the variation characteristic of a typical mid-latitude coastal climate, it would be expected to have the best application in short-term forecasting. The study appraises the indicative variable of maximum daily temperature (temp_max). Maximum daily temperature is widely applicable in climate change studies and energy consumption forecast modeling and has important health aspects in modeling urban heat waves and cold stress [13, 14]. While precipitation and wind did not enter explicitly, they have an auxiliary role in the exploratory analysis and might have a multivariate role in future work.

Before using the dataset, preprocessing was performed on it. There were no missing values; hence, the consistency of the series is preserved. Duplicated observations exist if records were not maintained properly, which could be eliminated. After the filtration process, the dataset needs to be arranged chronologically to preserve the time-related aspect of the data. This is an essential step since one record moved about can falsify the temporal distribution internal to deep learning training algorithms.

The second important feature is the distribution of extreme events.The data has several days of the snowfall which tends to cause remarkable inconsistency. It is also characterized by abnormally warm summer temperatures greater than 30 °C, which challenge the forecasting process of models. Including both classes of anomalies, the data set makes the models fit the averages of the seasonal behavior as well as the outliers that can make up most of the practical applications. Figure 4 depicts the time-series visualization of meteorological data collected from January 2012 to December 2015. Figure 5 presents the actual daily maximum temperature along with a 30-day rolling mean, highlighting seasonal trends and short-term variations.

2. Data Preparation

The predictive was defined as the supervised sequence learning problem. In that, the model is conditioned to make predictions of future outputs of a process on the basis of past observations. To convert the raw time series to supervised learning instances, the sliding-window mechanism was used. Explicitly, the previous ten days of the highest temperature were considered as the input variable and the highest temperature on the next day was the output variable. This approach can thus strike a good balance between not wanting to miss the capturing of the temporal dependencies but at the same time not having an excessively complex model [15]. Figure 6 illustrates the dataset cleaning, normalization, and preparation stages applied prior to model training.

In symbols, we use the time series as {x 1, x 2, ..., x r}, where x r is the top temperature of day,t. Taking the same example of a window size of 10, X t is taken as [x t 10, x t 9,... x t 1 and the target output is y t = x t. This conversion led to production of 1461 labeled samples in the dataset.

• Training set: the first 800 samples, in which the models could be fitted.

• Validation set: the following 200 samples, when used to optimize hyperparameters and watch out overfitting.

• Test set: final 461 samples, used in final evaluation.

This time-based partition is favoured in fitting time series models because real-data forecasts are unfamiliar in these applications. Random partitioning can potentially cause the leakage of data, which improperly increases the performance evaluation by the provision of knowledge about the future.

All input sequences were transformed to three-dimensional tensors of shape (samples, timesteps, features).

3. Model Architectures

To evaluate the capability of different neural network architectures in forecasting temperature, eight models were implemented. These models range from simple recurrent structures to more advanced hybrid architectures that combine convolutional and recurrent networks.

The baseline model was a Vanilla RNN that we tried stacking SimpleRNN layers to create a deeper model. RNNs can model sequential dependencies, since they use an internal state that passes information forward in time. They have not yet found practical use, however, due to the problem of vanishing gradients that does not allow them to reflect long-term dependencies [16].

The LSTM model employed four stacked layers of Long Short-Term Memory units. LSTMs address the vanishing gradient issue by introducing gates that regulate the flow of information, allowing the network to retain long-term dependencies in the data [17], [18]. Similarly, the GRU model, implemented with three layers, also employs gating mechanisms but with fewer parameters than LSTMs, making it computationally more efficient [8].

A 1D CNN model was added to provide short-term local patterns in the data. Causal padding on the convolutional filters ensures that information from future time steps is not used when predicting the next value. The application of CNNs to the detection of sudden changes is especially useful in locating sharp drop-offs (e.g. temperature due to rainstorms) [5], [8].

In order to investigate possible synergies, hybrid systems were designed. The GRU-LSTM hybrid applied LSTMs and GRUs in series, so that the limitations of using GRUs alone could be offset by the superior memory performance offered by LSTMs. The BiGRU-RNN-hybrid model consisted of bidirectional GRU and a SimpleRNN to train the model to interpret data both forward and backward. BiGRU-LSTM is a combination of a bidirectional GRU and an LSTM that improves the bidirectional context capturing with long-term memory.

The most sophisticated model was CNN-BiGRU hybrid. In this architecture, the local temporal features were extracted through convolutional layers and these local features were then passed into a bidirectional GRU to learn the dependency in both directions. The rationale behind the choice of this hybrid was that previous studies had shown that CNN-RNN hybrids are more effective than standalone models in spatiotemporal forecasting [6], [7], [14].

All the architectures utilized dropout layers with a dropout rate of 20 percent following each major block to avoid over-fitting. Finally, dense regression layers were used at the final layer and used the learned representation to predict the maximum temperature as a scalar.

4. Experimental Setup

The experiments were carried out on Keras with TensorFlow as the backend. Every model was trained until 100 epochs with a batch size of 32. Adam optimizer was used because its adaptive learning rate is helpful in providing consistent training across architectures of various complexities. The loss function adopted was mean squared error (MSE) as it is in line with the regression nature of the task.

Early stopping with a patience of ten epochs was used to prevent overfitting of the model and unnecessary computations. This mechanism tracked the loss during validation and stopped training when no further improvement was made in a predetermined patience interval, and reverted the model to the best set of weights recorded since that check proved to be optimal.

As the measurement criteria, three standard metrics of performance were consulted The MSE penalizes large deviations and is thus responsive to extreme weather conditions such that it works well with climate data. MAE provides us an average error in the value of degrees Celsius which can readily be translated into practical applicability of predicting energy consumption. MAPE normalizes errors using percentages, making it possible to compare performance across datasets of different scales.

Training and testing of the models were carried out under the identical circumstances to ensure a fair comparison. This research design controls the performance implications of architecture and not the difference in training set up.

5. Methodology Architecture

The general approach taken in the study is based on the specific pipeline that aims at meeting the criteria of reproducibility, objectivity in the comparison, and robustness of the forecasting task. The meteorological data is collected and preprocessed in the first phase of workflow in which the raw records are checked on missing entries, insert cases and temporal discrepancies. This step prevents the loss of the integrity of the input series since time-series predicted can be very receptive to slight data separation.

After preprocessing, the dataset is converted into a supervised learning format by means of a sliding window. In this research, the window width used was ten-days, meaning that the previous 10 days with maximum temperatures occurred are used to predict the next day's temperature. The scheme enables the models to utilize the dependencies in time in the data, combined with a trade-off between complexity and computational efficiency.

The data are then chronologically divided into three parts, training, validation, and testing. Splitting in the chronological order does not leak information regarding future values, an important fact in time-series prediction. Training set-The training subset will be used to train the models, validation set-To tune the hyperparameters and early stopping of the model, test set-To provide an unbiased measure of the model generalization. Figure 7 presents the proposed hybrid architectures designed for spatiotemporal feature extraction and temperature forecasting.

The next step is based on the deployment of several models of deep learning. The eight model designs were: Vanilla RNN, LSTM, GRU, CNN, GRU-LSTM, BiGRU-RNN, BiGRU-LSTM and CNN-BiGRU. All models were implemented with Keras and TensorFlow as backend, hyperparameters were kept constant across the models to permit fair comparison.

6. How the Models Work

All of the tested models have their own styles of sequence modelling. A closer appreciation of the operational intuition that led to these architectures sheds a more detailed light on their performance divergence

The Vanilla RNN is a machine that sequences data- it maintains a hidden state that changes with each new timestep. Although it allows it to model dependencies between adjacent observations, its simple recurrent formulation falls victim to the vanishing gradient [17] problem, and this causes it to lose effectiveness when learning long-term dependencies. Consequently, Vanilla RNNs tend to perform poorly at such tasks that need seasonal or long-term memory.

LSTM rescues this shorthood by a gating mechanism that controls the information flow. In particular, the information that is maintained, revised, or eliminated depends on the forget, input, and output gates [17]. By adopting this structure, the LSTM can be used to keep the relevant information over a longer timeframe and therefore used when performing weather forecasting where the short-term variation and the long-term periodicity both exist.

The GRU allows a simpler version of the LSTMs in which forget and input gates are merged into an update gate. This makes models both simpler and able to model long-term dependencies [8]. GRUs tend to be more optimistic than LSTMs; they tend to converge more often and the models need far fewer parameters, making them computationally inexpensive.

The CNN treats the task differently with the help of one-dimensional convolutional filters. As opposed to sequential memory, the CNNs represent local temporal features by using filters over sliding windows. This is why CNNs are very useful to capture short time variations like changes in temperature due to precipitation or decreases in temperature caused by a sudden fall in temperature. They are not as good at recording the seasons by themselves however because they cannot remember longer contextual memory [5], [8].

The hybrid architectures have the advantages of two or more architectures. The GRU-LSTM dual model uses both the efficiency of GRU and the long-term memory of LSTM, and the bidirectional models including BiGRU-RNN and BiGRU-LSTM cover temporal effects in both directions. These hybrids have come to standardly outperform the single models, using more effective representations of the sequence.

Particularly, the CNN-BiGRU hybrid was the most successful. In this architecture, CNN layers, equipped with the local feature extraction capability, will be used to identify short-term fluctuations and filter noise. The identified features are next forwarded to BiGRU layers that consider the sequence of the features in both directions to find longer-term trends and dependencies. This two-stage technique enables the model to combine the discovery of local and seasonal features at the same time. Figure 8 presents the proposed CNN–BiGRU hybrid architecture designed for spatiotemporal feature extraction and temperature forecasting.

7. Comparative Analysis

Each model was tested systematically on the validation and test sets after training by comparing it to the ground truth. The evaluation was performed according to the two-pronged approach: (i) quantitative benchmark based on statistical measures of errors, and (ii) qualitative analysis based on graphical diagnostics.

A leaderboard was generated in terms of validation RMSE as this is the most reliable indicator of a model generalizing during development. Such rankings were then tested for robustness by cross checking the results (RMSE, MAE, and MAPE) on the test set.

The findings indicated that there was a definite performance ranking held. Although the Vanilla RNN worked as a baseline it was the worst performer. The memory capacity and the vanishing gradient issue did not allow it to capture the seasonal dependencies longer than a couple of steps. The predictions were also over-smoothed, which underestimates winter downturns or summer surges-diagnostics that was consistent with previous findings that simple recurrent architectures tend to fail at long-term dependencies [16].

Both Sub-networks showed significant gains. Their gated mechanisms were able to maintain information in the longer sequences, which allowed them to model seasonal cycles better. GRU performed better between the two as it converged a little faster and took less computational time, as evidenced in previous studies that consider GRUs an alternative and effective version of LSTM [8]. Both of these, however, are also less responsive to sudden changes, such as cold snaps or sneaky heatwaves, and more towards smoothed temporal dynamics.

The CNN model had opposite strengths as it demonstrated. Convolutional filters performed best at detecting short-term variations as it tracked sudden changes like rainstorms, or a snow event. However, because the CNNs did not include any internal memory mechanism, they performed poorly on longer seasonal cycles, and failed in cases where longer term memory was necessary - as was observed in other sequence learning tasks [5], [16].

The hybrid architectures demonstrated a strong performance superiority including synergistic capabilities. As an example, the GRU-LSTM hybrid used the efficiency of GRU in short terms assigned dependencies and LSTM memory in long-term trends. The BiGRU with RNN and LSTM hybrids also had the advantage of bidirectional processing, thereby promoting better contextualization. When compared across metrics, these hybrids performed better than their standalone counterparts across the metrics.

The CNN BiGRU hybrid model was the outstanding one with the lowest RMSE, MAE and MAPE during validation and in test sets. This performance was explicable by its two-stage design as convolutional layers learned local features and detected abrupt short-term fluctuations, whereas the BiGRU modeled long-term seasonal relations bidirectionally. This enabled the accurate forecasting of the daily variability as well as the seasonal cycles. Importantly, validation and test curves showed a smooth fit without much divergence, showing high robustness and the abilities to resist overfitting.

In general, hybrid models, especially CNN and BiGRU, appeared most effective to deal with both short-term variability and long-term environments in weather forecasting. The outcomes are consistent with those of the previous research, which notes the success of the combination of convolutional feature extraction and recurrent memory networks in spatiotemporal forecasting [6], [7], [14].

Performance Comparison of Deep Learning Models

Best Model Summary

Conclusion

This research establishes a rigorously controlled and reproducible pipeline for daily maximum temperature forecasting using deep learning–based time-series modelling. The methodology integrates robust data preprocessing procedures including imputation of missing observations, correction of temporal inconsistencies, and validation of anomalous entries to ensure high-fidelity input signals. The supervised learning formulation using a 10-day sliding window effectively preserves temporal dependencies while maintaining computational efficiency.

Chronologically segmented training, validation, and testing sets eliminate forward-looking bias, enabling an unbiased assessment of model generalization. A comprehensive suite of architectures Vanilla RNN, LSTM, GRU, CNN, GRU-LSTM, BiGRU-RNN, BiGRU-LSTM, and CNN-BiGRU implemented with uniform hyperparameter configurations, supports a controlled comparative analysis of model behavior and predictive capability. The findings underscore the effectiveness of a standardized evaluation protocol and provide a scalable, transparent framework for future advancements in temperature forecasting and broader time-series predictive modeling applications.