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Prediction of Inflow to the Ujjani Dam Reservoir using Linear Regression and Hybrid Model | OMICS International| Abstract
ISSN: 2157-7617

Journal of Earth Science & Climatic Change
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  • Review   
  • J Earth Sci Clim Change,
  • DOI: 10.4172/2157-7617.1000544

Prediction of Inflow to the Ujjani Dam Reservoir using Linear Regression and Hybrid Model

Dattatray Rajmane
*Corresponding Author :

Received Date: Feb 08, 2021 / Accepted Date: Feb 22, 2021 / Published Date: Mar 01, 2021

Abstract

Assessment of impact of climate change is very essential for the areas where the water scarcity is the main issue. Ujjani dam one of the largest dams of Maharashtra state in India is constructed on Bhima River in 1980 which supplies water to downstream cultivable area of Solapur and Pune district. In this study statistical downscaling model was developed for downscaling and projecting the temperature and rainfall by considering the GFDL-CM3 (GCM) model under scenario RCP 6.0. Statistical downscaling models showed a very good correlation (R2) between NCEP predictors and hydro metrological predictands. Using the projected values of temperature and rainfall, inflow to the reservoir was predicted by developing the three different models namely; Multiple linear Regression, Artificial Neural Network and Wavelet Neural Network. The models were evaluated by using mean square error criteria. It is observed that there is a change in rainfall pattern, it increases in the months of September to December however it decreases in the months of June to August, and this is due to corresponding changes in rainfall. The inflow to the reservoir has been predicted in three different time period viz 2020-29, 2050-59 and 2080-89.

Keywords: Statistical downscaling; Multiple linear regressions; Artificial neural network; Wavelet neural network; Ujjani dam

Citation: Rajmane D, Waikar M (2021) Prediction of Inflow to the Ujjani Dam Reservoir using Linear Regression and Hybrid Model. J Earth Sci Clin Change 12: 1000544. Doi: 10.4172/2157-7617.1000544

Copyright: © 2021 Rajmane D et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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