Projection of Temperature and Precipitation using Multiple Linear Regression and Artificial Neural Network as a Downscaling Methodology for Upper Bhima Basin
Dattatray Kisan Rajmane1, Milind Laxman Waikar2
1Dattatray Kisan Rajmane *, Department of Civil Engineering, Shri Guru Gobind Singhji Institute of Engg. & Tech., Nanded, India.
2Dr. Milind Laxman Waikar, Department of Civil Engineering, Shri Guru Gobind Singhji Institute of Engg. & Tech., Nanded, India.
Manuscript received on August 01, 2020. | Revised Manuscript received on August 05, 2020. | Manuscript published on September 30, 2020. | PP: 1-8 | Volume-9 Issue-3, September 2020. | Retrieval Number: 100.1/ijrte.B3266079220 | DOI: 10.35940/ijrte.B3266.099320
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Abstract: 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.
Keywords: Artificial neural network, General Circulation model, Multiple linear regression, Upper Bhima Basin