Open AccessEstimation of Discharge and Total Water Level at Yedgaon Dam using Data Driven Techniques
Author(s) -
Preeti Kulkarni,
Shreenivas Londhe,
Nikita Sainkar,
Sayali Rote
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1197/1/012021
Subject(s) - correlation coefficient , mean squared error , water level , statistics , weir , coefficient of determination , hydrology (agriculture) , mean absolute error , pearson product moment correlation coefficient , genetic programming , environmental science , mathematics , computer science , engineering , geotechnical engineering , geography , machine learning , cartography
A reservoir operation planning using Data driven Techniques is gaining its momentum in hydrological area with good prediction and Estimation capabilities. The present work aims at using the 5 years data of Water Level to estimate the discharge and water level at the Yedgaon dam which is like pick up weir having its own yield and storage. It receives water from Dimbhe (though DLBC), Wadaj (through MLBC), Manikdoh (through river) and through Pimpalgaojoge (through river), in the Kukadi project of Maharashtra State, India. 4 different models were developed to estimate the water level using the Data Driven Techniques: M5 Model Tree, Support Vector Regression, Multi Gene Genetic Programming and Random Forest. The Accuracy of the developed models is assessed by the values of coefficient of correlation, coefficient of efficiency, mean absolute error and root mean squared error and comparison is done between actual values and Predicted values. The results indicated that the MGGP model was superior as compared to other techniques with correlation coefficient as 0.86 with an advantage of a single equation to estimate the water level.