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Development of Rainfall Prediction Models Using Machine Learning Approaches for Different Agro-Climatic Zones
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Author(s): Diwakar Naidu (BRSM College of Agricultural Engineering and Technology and Research Station, Chatarkhar, India & Indira Gandhi Agricultural University, Raipur, India), Babita Majhi (Guru Ghashidas Vishwavidyalaya, Central University, Bilaspur, India)and Surendra Kumar Chandniha (BRSM College of Agricultural Engineering and Technology and Research Station, Chatarkhar, India & Indira Gandhi Agricultural University, Raipur, India)
Copyright: 2021
Pages: 23
Source title:
Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science
Source Author(s)/Editor(s): Mrutyunjaya Panda (Utkal University, India)and Harekrishna Misra (Institute of Rural Management, Anand, India)
DOI: 10.4018/978-1-7998-6659-6.ch005
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Abstract
This study focuses on modelling the changes in rainfall patterns in different agro-climatic zones due to climate change through statistical downscaling of large-scale climate variables using machine learning approaches. Potential of three machine learning algorithms, multilayer artificial neural network (MLANN), radial basis function neural network (RBFNN), and least square support vector machine (LS-SVM) have been investigated. The large-scale climate variable are obtained from National Centre for Environmental Prediction (NCEP) reanalysis product and used as predictors for model development. Proposed machine learning models are applied to generate projected time series of rainfall for the period 2021-2050 using the Hadley Centre coupled model (HadCM3) B2 emission scenario data as predictors. An increasing trend in anticipated rainfall is observed during 2021-2050 in all the ACZs of Chhattisgarh State. Among the machine learning models, RBFNN found as more feasible technique for modeling of monthly rainfall in this region.
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