Machine Learning-Based Prediction of Heavy Rainfall Days in the Northern Himalayas: A Case Study of Shimla, Srinagar and Kargil

In regions vulnerable to heavy rainfall, such as the north Himalayan region of India, which includes the states of Himachal Pradesh, Jammu & Kashmir and Union Territory of Ladakh, an effective disaster management and ecosystem protection are especially important. Forecasting days with heavy rainfall accurately is crucial because these occurrences not only interfere with agricultural operations, but also cause landslides and flash floods, which cause serious damage to the environment and a large loss of life and property. Although traditional and conventional numerical weather prediction (NWP) models are widely used, problems with accuracy and operational efficiency are common with such models. The present study incorporates a machine learning technique known as the Long Short-Term Memory (LSTM) networks, to analyze long-term nonlinear time series rainfall data and to encapsulate temporal dependencies in the data. The study uses daily climatological rainfall data from the Indian Meteorological Department in Pune for a period of 123 years (1901 to 2023). The extensive data set has enabled the LSTM model to be trained to forecast rainfall categories such as Rather Heavy Rain and Heavy Rain, as specified by the India Meteorological Department (IMD). This study also investigates the model's ability to forecast days with relatively rather heavy and heavy rainfall in the districts of Shimla, Srinagar and Kargil of Himachal Pradesh, Jammu & Kashmir and Ladakh respectively. With an emphasis on the model's ability to predict days with rather heavy and heavy rainfall, this study also evaluates the model's performance using a variety of statistical measures including Mean Absolute Error(MAE), Root Mean Square Error(RMSE) and Coefficient of determination(R²) error. The encouraging findings of the present study implies that LSTM may prove to be a good substitute for traditional techniques in the North Himalayan region when it comes to rainfall forecasting. The present study adds to the increasing amount of research on the use of advanced machine learning algorithms for weather forecasting and emphasizes the importance of incorporating these methods into local climate adaptation strategies.