A framework to assess the dynamics of climate extremes on irrigation water requirement using machine learning techniques

A methodological framework has been proposed that consists of six different modules to compute and analyze the dynamics of climate-related extremes on irrigation water requirement (IWR) using machine learning techniques. These modules interactively supply information to compute irrigation water requirement, extreme indices, selection of appropriate indices, clustering of stations, and machine learning models to demonstrate the impact of rain and temperature-based extreme indices on water requirement for crops in a region. The proposed framework was implemented for the kharif paddy crop at 206 grids in the Mahanadi basin (Catchment area: 1.41 lakh km 2 ) of India. The seasonal rainfall, daily temperature range ( DT ), maximum one-day rainfall ( RX1D ), simple daily intensity index ( SDII ), and mean daily minimum temperature ( TxM ) were found to be the most significant indices, and the whole region was categorized into three clusters. The extra tree regressor with different parameters was found to be the most suited regressor technique among all 17 models used in the analysis and able to predict IWR with more than 75% accuracy in testing and training. The proposed theoretical framework is capable of quantifying the impact of climate extremes on IWR for any crop and automation may be useful to field practitioners and policymakers to plan available water resources optimally.