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

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....

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Vydané v:Aqua (London, England) Ročník 72; číslo 12; s. 2201 - 2215
Hlavní autori: Agarwal, Shivam, Mukherjee, Disha, Debbarma, Nilotpal
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: IWA Publishing 01.12.2023
Predmet:
generalized additive models (gams)
kriging
multiple linear regression (mlr)
north-east india
rainfall
random forest method (rf)
ISSN:2709-8028, 2709-8036
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Popis
Shrnutí: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.
ISSN:2709-8028
2709-8036
DOI:10.2166/aqua.2023.016