Integrating Regression Models and Climatological Data for Improved Precipitation Forecast in Southern India
In one sentence. Compares regression models and ensembles on NASA POWER climate data for precipitation forecasts in southern India, with ensemble E-MLR-RFR performing best.
Abstract
Modern technologies like Artificial Intelligence (AI) and Machine Learning (ML) replicate intelligent human behavior and offer solutions in all domains, especially for human protection and disaster management. Nowadays, in both rural and urban areas, flood control is a serious issue to overcome the vast disaster to life and property. The work proposes to identify an appropriate ML based precipitation forecast model for the flood-prone southern states of India namely Tamil Nadu, Karnataka, and Kerala which receive most precipitation using the climatological information obtained from the NASA POWER platform. The work investigates the effectiveness of ML forecasting models: Multiple Linear Regression (MLR), Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest Regression (RFR) and Ensemble (E) learning approaches of E-MLR-SVR, E-MLR-DTR, E-MLR-RFR, E-SVR-DTR, E-SVR-RFR, E-DTR-RFR, E-MLR-SVR-DTR, E-MLR-SVR-RFR, E-MLR-DTR-RFR and E-SVR-DTR-RFR in forecasting precipitation. The E-MLR-RFR model produces improved and most precise precipitation forecast in terms of Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and R2 values. A higher precipitation forecast can be used to provide early warning about the possible flood in any region.
Labels
- Topic
- Local & station forecasting, Extremes & hazards
- Type
- Application study
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Regional
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.14569/ijacsa.2023.0140567 OpenAlex:W4379385774
- Added
- 2026-10-22
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