Analysis of extreme annual rainfall in North-Eastern India using machine learning techniques

Abstract

The machine learning techniques of Multiple Linear Regression (MLR), Generalized Additive Models (GAMs), and the Random Forest (RF) Method have been used to analyze the extreme annual rainfall in the six states of Assam, Meghalaya, Tripura, Mizoram, Manipur, and Nagaland in North-Eastern (NE) India. Latitude, longitude, altitude, and temperature were the covariates that were used in this study. Ordinary Kriging was used to interpolate the predicted outcomes of each dataset. Statistical metrics like Mean Absolute Errors (MAE), Root Mean Square Error (RMSE), Coefficients of Determination (COD-R2), and Nash–Sutcliffe Efficiency (NSE) were also assessed. When compared to satellite rainfall data, all techniques performed significantly better for ground rainfall data. For prediction, GAM's predicted rainfall values triumph over MLR or RF. RF ranks a close second, while the linearity of MLR prohibits it from making precise predictions for a physical phenomenon like rainfall. The MAE and RMSE of GAM forecasts are significantly lower than those of MLR and RF in most circumstances. Additionally, the COD and NSE of GAM predictions are significantly better than both MLR and RF in most cases, showing that GAM, out of MLR, GAM, and RF, is the best model for predicting rain in our research area. HIGHLIGHTS The map for RF + OK is much more realistic.; The testing (rain gauge data) maps were much better for all the methods than their training counterparts.; Temperature inclusion has an effect on latitude, longitude, and elevation at each point as an attribute.; The use of splines in the linear model allows GAMs to get around linearity constraints.; Comparing the MAE and RMSE of GAM predictions for the various datasets.;

Keywords

• generalized additive models (gams)
• kriging
• multiple linear regression (mlr)
• north-east india
• rainfall
• random forest method (rf)