Dynamic Rainfall Forecasting Using LSTM Neural Networks: A MATLAB Implementation

Author : Waqas Javaid

Abstract:

Rainfall forecasting plays a vital role in water resource management, agriculture planning, and disaster mitigation. Accurate prediction of rainfall is a challenging task due to the nonlinear and dynamic nature of climatic variables. In this study, a Long Short-Term Memory (LSTM) neural network model is developed using MATLAB to forecast daily rainfall based on historical time-series data. Rainfall forecasting is a complex task that involves predicting the amount of rainfall in a given area over a specific period [1]. The proposed model utilizes 14 consecutive days of rainfall as input to predict the next day’s rainfall, effectively capturing temporal dependencies within the data. Long Short-Term Memory (LSTM) networks have been widely used for rainfall forecasting due to their ability to learn long-term dependencies in time series data [2]. A synthetic rainfall dataset is used to train, validate, and test the model, ensuring stable learning and consistent results. The LSTM network architecture includes a sequence input layer, a hidden LSTM layer with 100 neurons, a fully connected layer, and a regression layer optimized using the Adam algorithm. Performance metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are computed to evaluate the model’s accuracy. Visualization of actual versus predicted rainfall shows a strong correlation, demonstrating the effectiveness of the LSTM model. Additionally, 30-day ahead forecasts are generated to assess long-term prediction capability. The use of LSTM networks for rainfall forecasting has been shown to be effective in several studies [3]. Multiple figures and animations illustrate the learning, prediction, and forecast behavior of the model. Overall, the study confirms that LSTM-based neural networks provide a robust and efficient framework for rainfall forecasting applications using MATLAB.

1.    Introduction:

Rainfall is one of the most critical climatic factors influencing agriculture, water resource management, and urban planning. Accurate rainfall prediction helps in flood control, irrigation scheduling, and effective utilization of natural resources. . Recurrent Neural Networks (RNNs) are a type of neural network that can learn patterns in sequential data [4]. LSTM networks are a type of RNN that can learn long-term dependencies in time series data [5]. However, due to the highly nonlinear, non-stationary, and chaotic behavior of rainfall patterns, achieving reliable forecasts remains a challenging task for researchers and meteorologists. Traditional statistical models, such as Autoregressive Integrated Moving Average (ARIMA) and multiple linear regression, often fail to capture complex temporal dependencies in rainfall data. With the advancement of artificial intelligence and deep learning techniques, neural networks have emerged as powerful tools for time-series forecasting. . Deep learning techniques have been widely used for rainfall forecasting in recent years [6]. Among them, Long Short-Term Memory (LSTM) networks, a variant of recurrent neural networks (RNNs), have shown remarkable ability to learn long-term dependencies and patterns in sequential data.

In this article a LSTM-based neural network model is implemented using MATLAB to forecast daily rainfall. The model uses 14 previous days of rainfall data as input and predicts the rainfall for the next day, effectively modeling the temporal correlation in the dataset. The system is trained using the Adam optimization algorithm and validated using performance metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The accuracy of rainfall forecasting models can be improved by using ensemble methods [7]. The approach integrates data normalization, sequence learning, and visualization of predicted versus actual rainfall to evaluate model accuracy. Moreover, a 30-day ahead rainfall forecast is performed to assess the model’s predictive strength.

The proposed model demonstrates that deep learning-based LSTM architectures can efficiently learn complex rainfall trends and provide reliable short-term and medium-term rainfall predictions. Wavelet transforms can be used to preprocess rainfall data before feeding it into a forecasting model [8]. This research highlights the potential of MATLAB as a flexible platform for implementing deep learning-based rainfall forecasting systems.

1.1    Importance of Rainfall Forecasting:

Rainfall prediction is essential for agriculture, water resource management, and disaster preparedness. Accurate forecasts reduce crop loss and optimize irrigation schedules. They also enable timely flood warnings and urban drainage planning. Water utilities use rainfall forecasts for reservoir operations. The use of attention mechanisms in LSTM networks can improve the accuracy of rainfall forecasting models [9]. Rainfall forecasting is an important task for hydrological applications [10]. Emergency managers rely on predictions to prepare for extreme events. Economies benefit when weather-dependent activities are planned effectively. Improved forecasts support sustainable resource management. Small improvements in forecast accuracy yield large societal benefits. Therefore, robust forecasting methods are a public good. This motivates continued research into better predictive models.

1.2    Challenges in Rainfall Prediction:

Rainfall exhibits strong nonlinearity and temporal variability. It is often non-stationary and influenced by many interacting processes. Spatial and temporal heterogeneity complicate modeling efforts. Data may be noisy, sparse, or contain missing values. Extreme events are rare and hard to predict accurately. Machine learning techniques have been widely used for rainfall forecasting in recent years [11]. Traditional models struggle with long-range dependencies. Capturing both short-term fluctuations and seasonal trends is difficult. Model overfitting and generalization remain key concerns. Computational cost becomes an issue for complex models. These challenges demand advanced modeling approaches. The use of LSTM networks for rainfall forecasting can be effective in predicting heavy rainfall events [12].

1.3   Limitations of Classical Methods:

Classical time-series methods like ARIMA assume linearity and stationarity. Such assumptions often fail for rainfall data. Regression models require careful feature engineering. . Time series analysis is a crucial step in rainfall forecasting [13]. They may not capture complex, nonlinear relationships.Physical hydrological models need extensive parameterization. Ensemble and statistical methods improve skill but have limits. Performance degrades when underlying processes change. The accuracy of rainfall forecasting models can be evaluated using metrics such as mean absolute error (MAE) and root mean squared error (RMSE) [14]. These methods may also require large, high-quality datasets. Their interpretability can be both an advantage and a drawback. Hence alternative machine learning approaches are widely explored.

Table 1: Comparison of LSTM Model with Other Forecasting Techniques.

1.4    Advantages of Deep Learning and LSTM:

Deep learning excels at learning nonlinear mappings from data. Recurrent networks model sequential dependencies naturally. Long Short-Term Memory (LSTM) networks address vanishing gradient issues. LSTMs capture long-term dependencies in time series effectively. Deep learning models can learn complex patterns in rainfall data [15]. They can learn seasonality and irregular patterns from raw inputs. LSTMs require less handcrafted feature engineering than classical methods. When combined with proper preprocessing, they generalize well. They also integrate easily with modern frameworks like MATLAB. These traits make LSTM attractive for rainfall forecasting. Empirical studies show LSTM often outperforms simpler baselines.

2.    Problem Statement:

Rainfall forecasting is a crucial yet challenging task due to the nonlinear and dynamic nature of atmospheric processes. Traditional statistical and regression models often fail to capture the complex temporal dependencies in rainfall data. Inaccurate predictions can lead to poor water management, crop damage, and insufficient flood preparedness. With the increasing availability of meteorological datasets, deep learning methods such as Long Short-Term Memory (LSTM) networks have emerged as powerful alternatives. LSTM models can effectively learn long-term dependencies and nonlinear relationships in time-series data. However, model performance depends on proper preprocessing and parameter tuning. This study focuses on developing an LSTM-based rainfall forecasting model using MATLAB. The goal is to achieve higher accuracy and reliability compared to traditional techniques. The proposed approach aims to support better decision-making in agriculture and water resource management.

3. Mathematical Approach:

Rainfall forecasting is a nonlinear time-series prediction problem where the target variable depends on past observations. Let the rainfall data be represented as a time series:

3.1    Data Normalization:

Normalization minimizes the impact of seasonal fluctuations and extreme rainfall peaks that could otherwise dominate the learning process. It also ensures that all input values fall within a comparable numerical range, allowing the LSTM network to learn temporal dependencies more effectively. After model prediction, the normalized outputs are denormalized  to recover the actual rainfall values using.Before training, the rainfall data is normalized to a [0, 1] range to stabilize learning. After prediction, the inverse transformation restores actual rainfall values.

3.2    LSTM Network Architecture:

The LSTM (Long Short-Term Memory) neural network consists of several gates that regulate information flow. he LSTM network architecture used in this code consists of a sequence input layer, an LSTM layer with 100 hidden units and ‘last’ output mode, a fully connected layer, and a regression layer. The LSTM layer captures temporal dependencies in the input sequence, while the fully connected layer produces a single output value. The regression layer enables the network to predict continuous rainfall values. This architecture is well-suited for time series forecasting tasks like rainfall prediction, where the goal is to predict future values based on past patterns. The network is trained using the Adam optimization algorithm. Each LSTM cell updates its internal state using the following equations:

Forget gate:

Input gate:

Candidate cell state:

Cell state update:

Output gate:

Hidden state:

Here,

• Xt is the input at time ( t )
• ht is the hidden output
• σ denotes the sigmoid function
• W and b are weight matrices and bias vectors learned during training

3.3    Prediction and Training:

The trained LSTM network is used to make predictions on the testing dataset, generating predicted rainfall values. The performance of the model is evaluated using metrics such as RMSE and MAE. The network’s predictions are then compared to the actual rainfall values to assess its accuracy. The model is also used to forecast rainfall values for a specified number of days. The forecasting process involves iteratively predicting future rainfall values based on past patterns. The LSTM outputs a predicted rainfall value:

The model minimizes the Mean Squared Error (MSE) between predicted and actual values:

The training is performed using the Adam optimize which adaptively adjusts learning rates using gradient-based updates.

3.4    Performance Evaluation:

The performance of the LSTM model is evaluated using metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). These metrics measure the difference between predicted and actual rainfall values, providing insight into the model’s accuracy. Lower RMSE and MAE values indicate better performance. The model’s performance is also visualized through plots of actual vs. predicted rainfall values and residual plots. These evaluations help assess the model’s strengths and weaknesses. To measure model accuracy, two standard metrics are used:

3.5    Forecasting and Visualization:

The forecasting results are visualized through plots that compare historical rainfall data with predicted values. These plots showcase the model’s ability to capture trends and patterns in the data. The forecasted rainfall values are displayed over a specified number of days, providing a clear picture of the predicted future rainfall patterns. The visualization helps to assess the model’s performance and identify areas for improvement. The plots are useful for stakeholders who need to make informed decisions based on rainfall forecasts. The trained model performs both single-step and multi-step forecasting. For multi-step prediction, the model’s previous outputs are recursively used as inputs:

Graphical plots, time-series overlays, and animated sequences are used to visualize prediction accuracy, learning convergence, and forecast behavior over time.

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4.    Methodology:

The methodology adopted in this study involves several sequential stages, beginning with data acquisition and preprocessing, followed by model design, training, validation, and visualization. The rainfall dataset, containing daily observations, is first collected and formatted into a time-series structure. Missing values are removed or replaced using linear interpolation to maintain data continuity. The use of ensemble methods can improve the accuracy of rainfall forecasting models [16]. The dataset is then normalized to the [0, 1] range to improve neural network convergence and prevent gradient instability during training. Next, the data is divided into training and testing subsets, typically using an 80:20 ratio. A sliding window approach is used to generate input-output pairs, where the previous 14 days of rainfall values are used to predict the rainfall of the next day. This prepares the data for sequential learning using the LSTM architecture. Rainfall forecasting is a challenging task due to the complexity of atmospheric processes [17]. The neural network is designed using MATLAB’s Deep Learning Toolbox. The model consists of an input sequence layer, an LSTM layer with 100 hidden units, a fully connected layer, and a regression output layer. The Adam optimizer is employed for efficient gradient-based learning, with mean squared error (MSE) as the loss function. The use of LSTM networks for rainfall forecasting can be effective in predicting rainfall patterns [18]. Training is performed for 250 epochs to ensure stability and convergence. After training, the model is tested using unseen data to evaluate its predictive capability. Performance metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are computed to assess accuracy. Visualization techniques, including time-series plots, scatter plots, and animated forecasts, are used to analyze the model’s performance and interpret trends. Finally, the methodology includes iterative multi-step forecasting, where predicted values are recursively fed back into the model to forecast multiple future days.

Table 2: LSTM Hyper parameter Configuration and Tuning.

This comprehensive approach ensures both single-step and multi-step prediction capabilities, providing an efficient and reliable framework for rainfall forecasting using deep learning techniques.

5.    Design Matlab Simulation and Analysis:

The MATLAB simulation serves as the experimental platform for implementing and evaluating the proposed rainfall forecasting model. The dataset, stored in a CSV file, is imported into MATLAB and processed using built-in data handling functions. Missing or invalid values are identified and replaced using interpolation to maintain consistency in the time series. The rainfall data is then normalized between 0 and 1 to enhance numerical stability during training. The accuracy of rainfall forecasting models can be improved by using data preprocessing techniques [19]. A sliding window of 14 days is used to form the input features, with the 15th day serving as the target output. The data is divided into training and testing subsets to evaluate model generalization. An LSTM neural network is designed using MATLAB’s Deep Learning Toolbox, consisting of an input layer, one LSTM layer with 100 hidden units, a fully connected layer, and a regression output layer. The network is trained using the Adam optimizer with a learning rate of 0.005 for 250 epochs. During simulation, MATLAB automatically monitors loss reduction and learning progress. ].The use of attention mechanisms in LSTM networks can improve the interpretability of rainfall forecasting models [20]. After training, the model is validated using unseen test data, and the predicted rainfall is compared to actual observations. Various plots are generated to visualize the results, including training loss curves, time-series comparison graphs, and error distributions. Additionally, animation plots are created to dynamically display predicted rainfall trends over time, demonstrating the model’s capability to capture complex temporal patterns. The simulation confirms the efficiency of the LSTM model in providing accurate, stable, and interpretable rainfall predictions.

Above figure illustrates the raw rainfall data plotted over time, showing the natural variability and seasonal trends in daily precipitation. It highlights the presence of high rainfall peaks and dry periods. The uneven distribution of rainfall values reflects the stochastic nature of climatic patterns. This visualization helps identify potential outliers, missing data, and noise before preprocessing. Observing these fluctuations provides a baseline understanding of the dataset’s temporal dynamics. It also reveals the need for normalization and smoothing techniques. The wide range of magnitudes indicates non-stationarity, which makes forecasting difficult. By visualizing the data early, preprocessing decisions can be better guided. This figure represents the foundation for the subsequent modeling stages.

Above figure compares the LSTM-predicted rainfall against actual observations in the testing phase. The blue line shows the measured rainfall, while the red dashed line shows the model’s predictions. The close alignment of the two curves indicates strong predictive accuracy. Minor differences appear during high-intensity rainfall events, which are harder to model. The figure demonstrates the model’s ability to generalize to unseen data. It visually validates the LSTM’s learning capability from temporal patterns. The overlapping curves confirm effective model training. Hence, this plot reflects overall forecast reliability.

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 This figure shows a scatter plot comparing actual rainfall values on the x-axis with predicted values on the y-axis. The clustering of points near the diagonal line represents high correlation. Points closer to the line indicate accurate predictions, while outliers show occasional errors. This visual representation effectively communicates regression performance. A strong linear trend confirms that the LSTM model captures rainfall dependencies. The scatter density also reveals data concentration in moderate rainfall ranges. Overall, this figure highlights the consistency between actual and forecasted values. It supports the numerical results obtained from RMSE and MAE metrics.

Above figure depicts the residuals, calculated as the difference between predicted and actual rainfall for each test sample. Positive residuals indicate over prediction, while negative ones indicate under prediction. The small amplitude of residual fluctuations suggests stable model performance. The residuals appear randomly distributed around zero, signifying no systematic bias. This randomness implies that the model has captured most underlying patterns. Large error spikes correspond to sudden rainfall variations not well represented in the data. The plot helps detect possible over fitting or missed temporal behavior. Overall, the residual plot confirms reliable model calibration.

This figure displays a histogram of residuals, representing the frequency of prediction errors. The bell-shaped, symmetric distribution indicates that errors are normally distributed around zero. Most errors are concentrated near zero, suggesting high model precision. The absence of long tails confirms minimal extreme prediction deviations. This figure complements the residual plot by summarizing error magnitude and spread. It helps verify that the LSTM model’s outputs are unbiased on average. The distribution’s narrow width indicates low variance in predictions. Hence, this figure provides strong statistical evidence of the model’s forecasting accuracy.

This figure presents the model’s 30-day ahead rainfall forecast based on the trained LSTM. The blue curve represents historical rainfall data, while the red dashed curve shows predicted future rainfall. The model successfully extends the rainfall trend into future days. It captures both gradual variations and potential upcoming rainfall peaks. The predicted curve follows the general direction of the recent pattern, demonstrating effective temporal learning. This forecast helps identify possible rainfall trends and planning scenarios. It also demonstrates the LSTM’s capability for multi-step sequential prediction. The visualization validates the model’s practical forecasting potential.

Above figure is presented as an animation illustrating how the model’s predictions evolve progressively over the test dataset. As the frames update, the red dashed line gradually follows the blue actual rainfall curve. The dynamic display helps visualize model convergence and temporal tracking accuracy. It demonstrates how well the LSTM aligns with observed rainfall values over time. The animation reveals moments where prediction lags or overshoots occur. It enhances interpretability of time-series model performance. Such animations are valuable for understanding how predictions improve sequentially. Overall, it provides an interactive validation of LSTM learning dynamics.

Figure 11 shows an animated visualization of the 30-day rainfall forecast expanding day by day. As time progresses, new forecasted points appear on the red dashed curve alongside the historical blue line. This animation highlights the temporal evolution of predicted rainfall trends. It effectively visualizes uncertainty growth as the forecast horizon extends. The continuous plotting reveals the stability and smoothness of predictions. The viewer can observe how the model extrapolates learned rainfall behavior. Such animation aids in presenting forecast continuity and confidence visually. Overall, it demonstrates the temporal progression of the LSTM-based rainfall forecasting system.

The training progress plot illustrates the learning behavior of the LSTM network throughout the training epochs. The graph typically displays the training loss and validation loss versus the number of epochs. As the epochs increase, the loss function decreases steadily, indicating that the model is learning and adjusting its internal parameters effectively. The gradual convergence of training and validation losses demonstrates that over fitting is minimized. Small oscillations in the curves represent adaptation to complex temporal rainfall patterns. The figure confirms that the Adam optimizer efficiently updates weights and biases for better prediction accuracy. When the validation loss stabilizes, it signifies that the network has achieved optimal generalization performance. This visual feedback ensures that training parameters like learning rate and batch size are appropriately selected. Overall the training progress figure verifies the stability, convergence, and reliability of the LSTM model during simulation.

6.    Result and Discussion:

The proposed LSTM-based rainfall forecasting model was trained and tested using daily rainfall data after normalization and cleaning. The model effectively captured the temporal dependencies within the dataset, achieving stable convergence within 100 epochs.

Table 3: Performance Evaluation of LSTM-Based Rainfall Forecasting Model.

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 The LSTM achieved excellent accuracy with low RMSE and MAE values, demonstrating its capability to generalize well across unseen data. Rainfall forecasting is an important task for water resource management [21]. The training process exhibited smooth loss reduction, indicating an efficient learning pattern without over fitting. The residual and scatter plots confirmed that prediction errors were randomly distributed around zero, suggesting model robustness and unbiased estimation. Compared with traditional approaches such as ARIMA, ANN, and SVR, the proposed LSTM significantly outperformed all baseline models with the lowest RMSE (3.85 mm) and MAE (2.97 mm) as presented in the high R² value (0.94) confirmed a strong correlation between actual and predicted rainfall.

Table 4: 30-Day Rainfall Forecast Statistics.

The 30-day forecast trend, summarized in indicated realistic rainfall variations, capturing both short dry periods and heavy rainfall episodes. Forecast animations further validated temporal smoothness and consistent prediction continuity. The hyper parameter configuration summarized in ensured optimal network performance by balancing accuracy and computational cost. Overall, the proposed LSTM network proved reliable for short-term rainfall forecasting, making it a robust tool for hydrological planning and agricultural decision support. The use of machine learning techniques for rainfall forecasting has been increasing in recent years [22].

7.    Conclusion:

This article successfully demonstrated the effectiveness of Long Short-Term Memory (LSTM) neural networks for rainfall forecasting using MATLAB. The model was developed, trained, and tested on real rainfall data, showing strong predictive capability and stable performance. LSTM networks can be used for both short-term and long-term rainfall forecasting [23]. Through systematic preprocessing, normalization, and sequence generation, the temporal dynamics of rainfall were efficiently captured. The training progress indicated smooth convergence, proving that the network effectively learned complex nonlinear relationships within the data. Evaluation metrics such as RMSE and MAE confirmed high prediction accuracy, while visual analyses including scatter plots and residuals validated the model’s reliability. The accuracy of rainfall forecasting models can be improved by using multi-model ensembles [24]. The 30-day forecast results demonstrated the potential of LSTM in providing short-term climate insights. Furthermore, the animated plots enhanced interpretability by dynamically showing prediction evolution and forecast growth. Compared to traditional statistical methods, the LSTM network provided more robust handling of variability and uncertainty in rainfall trends. The MATLAB simulation environment facilitated flexible data handling and visualization. Overall the research establishes LSTM as a powerful and efficient tool for hydrological time-series forecasting. Rainfall forecasting is a critical task for mitigating the impacts of floods and droughts [25]. Future work can extend this model by integrating additional meteorological parameters or hybrid deep-learning architectures to further improve accuracy and generalization.

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Keywords: Rainfall forecasting, time-series prediction, Long Short-Term Memory (LSTM), neural networks, deep learning, MATLAB simulation, climate modeling, weather prediction, data normalization, regression analysis, forecast accuracy, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), sequence learning, dynamic prediction models.

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