Open AccessProjection of Temperature and Precipitation using Multiple Linear Regression and Artificial Neural Network as a Downscaling Methodology for Upper Bhima Basin
Author(s) -
Dattatray Kisan Rajmane,
AUTHOR_ID,
Milind L. Waikar,
AUTHOR_ID
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b3266.099320
Subject(s) - downscaling , precipitation , environmental science , linear regression , climatology , artificial neural network , scale (ratio) , projection (relational algebra) , climate change , meteorology , mathematics , computer science , statistics , geography , geology , machine learning , algorithm , oceanography , cartography
Study of Climate change effect on water resources is very important for its effective management. Projection of temperature and precipitation can be performed by using General Circulation Model (GCM) outputs. GCM can make the projections of climate parameters with different emission scenarios at coarser scale. However hydrological models require climate parameters at smaller scale Downscaling technique is used for obtaining small scale climate variables from large scale variables of GCM outputs. In this study downscaling has been carried out by using Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) techniques. Performance of MLR and ANN models has been evaluated considering Coefficient of determination value (R2). It has been observed that ANN performs better against MLR Model, showed the results that rainfall distribution pattern is varied, in monsoon season rainfall decreases while it increases in post monsoon period. Due to its good evaluation performance such techniques can be applicable for downscaling purpose.