Spatio-Temporal Rainfall Forecasting in Uttarakhand Using Ensemble Machine Learning Models - Chaudhary, Harsh Vardhan; Saxena, Parul (Journal of Neonatal Surgery, 2025)
Precise rainfall prediction is crucial for efficient agricultural planning, efficient disaster management, and efficient water resource management, particularly in ecologically fragile regions like Uttarakhand, India. The state's diversified topography and complex climatic conditions make traditional forecasting methods less accurate. In order to create an effective rainfall prediction model tailored to Uttarakhand, this paper compares and contrasts a number of machine learning algorithms, including Linear Regression, Support Vector Machine (SVM), Random Forest, Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM). Each model was trained and validated using performance metrics such as R2 score, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) based on historical weather records. With an R2 score of 0.91, an RMSE of 11.92 mm, and an MAE of 8.21 mm, the LSTM network was the most predictive model among those examined. The outcomes demonstrate how well deep learning methods—particularly LSTM—model temporal weather patterns in challenging geographic contexts. In Uttarakhand and other similar areas, this paper offers an accurate and extensible prediction model that can facilitate prompt decision-making in flood control, agriculture, and climate risk assessment.