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

Abstract

Reliable temperature and precipitation forecasting is more important than ever for communities and policymakers preparing for the challenges ahead for effective adaptation and mitigation strategies in the Kathmandu Valley, which is located within a landlocked mountain valley. A deep learning-based climate forecasting model for the area is proposed in this work with the intention of converting improved forecasts into more intelligent, timely decisions for resilience and adaptation. A Long Short-Term Memory (LSTM) network, an architecture ideal for identifying minute, distant patterns in climate data, lies at the heart of the method. Before the model was taught to identify intricate correlations between climatic variables throughout time, historical climate records were thoroughly cleaned and normalized. In order to evaluate real-world reliability, performance was then compared against ARIMA (AutoRegressive Integrated Moving Average) and two LSTM variants: Bidirectional LSTM and Stacked LSTM. It was then stress-tested against harsh weather conditions and unobserved data. The outcomes were evident: LSTM-based models regularly performed far better than ARIMA, with MAE, MSE, and RMSE of 0.0217, 0.0008, and 0.0285 as opposed to ARIMA’s 1.2809, 1.5290, and 1.2365. These results support deep learning’s significant potential as a scalable and dependable method for regional climate forecasting, giving it an advantage in the valley’s climate change adaptation policy-making process.