Deep Learning Based Climate Forecasting Model Using LSTM (Long Short-Term Memory) of Kathmandu Valley

Explainable Deep Learning Models for Long-Term Temperature and Rainfall Forecasting

Climate impact assessment, agricultural planning, and disaster preparedness requires accurate long-term prediction of temperature and rainfall. Conventional statistical models are frequently not able to represent nonlinear and long-term relationships in climate data, and deep learning models, however powerful in prediction, cannot be interpreted. The given paper suggests an interpretable deep learning architecture, which uses Long Short-Term Memory (LSTM) networks and SHAP-based explainability to forecast multivariate climate. Sliding window sequences are used to process historical data (temperature, rainfall, humidity and atmospheric pressure) to obtain seasonal and long-term trends. The proposed model predicts both temperature and rainfall simultaneously, and it has a better performance than the ARIMA, LSTM, GRU and CNN-LSTM models in the terms of RMSE, MAE and Accuracy. Moreover, the explainability module offers insights into feature and time importance, which increases the level of transparency and trust in the prediction. Empirical evidence shows that the described method is effective in balancing predictive accuracy of 92% and understandability, which can be used in climate decision support systems.

Comprehensive Performance Evaluation of Deep Learning Algorithms for Multi-Variable Climate Prediction: RNN, LSTM, and GRU Analysis

The selection of appropriate deep learning architectures for climate prediction remains a  critical challenge in atmospheric sciences, with different algorithms showing varying performance across climate variables and geographical regions. This study presents a comprehensive comparative analysis of three prominent deep learning architectures Recurrent Neural Networks (RNN), Long   Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) for multi-variable climate prediction in the Middle East region. Using 42 years (1981-2022) of high-resolution MERRA-2 reanalysis data across nine Middle Eastern capitals, we evaluated the performance of these algorithms in predicting six key climate variables: maximum and minimum temperature, relative humidity, wind speed, solar radiation, and precipitation. Our analysis reveals significant performance differences across algorithms, with GRU demonstrating superior computational efficiency (14% faster training than LSTM) while  maintaining competitive accuracy. LSTM excelled in capturing long-term dependencies for temperature variables (R² > 0.95), while RNN showed adequate performance for simpler patterns but struggled with complex temporal relationships. The study provides detailed performance metrics, computational requirements, and practical guidelines for algorithm selection based on specific climate prediction tasks. Key findings indicate that algorithm choice should be tailored to the prediction target, with temperature  variables favoring LSTM, precipitation benefiting from GRU's efficiency, and humidity showing  comparable performance across all architectures. These results provide essential guidance for operational  weather forecasting systems and climate modeling applications, contributing to the optimization of deep learning approaches in atmospheric  sciences.

A CNN–LSTM Deep Learning Framework for Multivariate Temperature Forecasting and Extreme Weather Event Analysis

Throughout history weather predication has been a powerful and necessary tool in a number of activities that have had a major influence on human development and survival such as climate monitoring, agricultural planning and disaster management. Unlike other variables, temperature fluctuations are a major challenge for prediction due to the fact that they are nonlinear and extremely dynamic. This research paper introduces deep learning (DL) architectures for multivariate temperature forecasting using past weather data of single cities in India as samples. The DL methods such-as LSTM, GRU, Hybrid CNN-LSTM and Attention-based model were first outlined and then experimented. The meteorological variables that were considered include humidity, precipitation, wind speed and cloud cover. The quality of forecasting is quantified by the means of MAE, RMSE, MAPE and R2. Experimental results reveal that the DL methods achieve significantly better forecasting of temperature as compared to the conventional ARIMA model. Out of the deep learning models which were experimented with the CNN-LSTM model gave the best performance and has the following results: MAE (287.37), RMSE (401.60), MAPE (7.35%) and R2 (0.893). Besides that, CNN-LSTM model not only performed better in normal conditions but also in extreme temperatures and was able to achieve R2 of 0.923, which underscores its capacities to capture complex weather changes. This paper provides strong evidence of the value of combining DL methods for weather prediction. In the future, efforts will be made to enhance the accuracy of prediction by using transformer-based time-series models and incorporating larger spatiotemporal climate data.

Enhanced Short-term Temperature Prediction Through ARIMA-LSTM Hybrid Model

Short-term temperature prediction plays a vital role in agriculture, water resource management, energy planning and climate change monitoring. Nevertheless, traditional statistical methods do not account for the nonlinearities in weather patterns, whereas deep learning models may not consider the linear time series characteristics. To solve this problem, the present study proposes a hybrid Auto-Regressive Integrated Moving Average-Long Short Term Memory (ARIMA-LSTM) model for short-term temperature prediction. The ARIMA model will be used in the proposed framework to identify the linear and seasonal components of temperature patterns. The errors produced by the ARIMA model will be further analyzed by means of an LSTM neural network that would model the non-linear time series behavior. The model is tested on the Tamil Nadu and Puducherry Weather Dataset (2022), consisting of roughly 1.8 million weather data sets, including variables like temperature of the air, humidity level, wind speed, and pressure. The evaluation criteria include mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The experimental findings reveal that the developed model performs better in terms of accuracy than traditional deep learning models, thus providing an effective and reliable tool for predicting temperature in short intervals.

Deep Learning-Based Multi-Step Ahead Streamflow Forecasting under RCP 4.5 and 8.5 Scenarios

One-Step-Ahead Solar Radiation Forecasting Using Bi-LSTM and GRU Models in Semi-Desert Climates

The increasing demand for energy and the imperative to reduce greenhouse gas emissions have heightened the need for renewable energy sources. Hence, there has been a notable surge in research efforts focused on advancing solar energy forecasting. The aim of this study is to forecast solar radiation using Deep Neural Network models, including Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU), based on historical solar radiation values recorded at half-hour intervals in a semi-desert climate over a two-year period starting in January 2020. To assess and compare the accuracy of the two Deep Neural Networks (DNNs), various evaluation metrics were used, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Maximum Error, and R-squared (R²). Solar radiation predictions were carried out based on the values recorded over the previous twenty-four hours at half-hour intervals. The results obtained indicate that both forecasting models achieve exceptional accuracy in one-step-ahead solar radiation prediction, with correlation coefficients exceeding 97.2%. This highlights the strong potential of Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM) models for optimizing and estimating solar radiation. Consequently, these models can be effectively used to estimate solar energy production, thereby enhancing the control and efficiency of solar energy systems. However, the study also highlights challenges in forecasting under partial cloud cover, where sudden fluctuations in solar radiation adversely affect prediction accuracy. Addressing these complexities could further improve the robustness of forecasting models and enhance their practical applicability in semi-desert climates.