NAVRACHANA UNIVERSITY

Application of soft computing techniques to predict reservoir water level

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dc.contributor.author Shinglot, Vrushaly K.
dc.contributor.author Tiwari, Monika R.
dc.contributor.author Bhatt, Shardav U.
dc.contributor.author Shrimali, Narendra J.
dc.date.accessioned 2021-10-01T07:19:54Z
dc.date.available 2021-10-01T07:19:54Z
dc.date.issued 2016-07
dc.identifier.issn 2231-2307
dc.identifier.uri http://27.109.7.66:8080/xmlui/handle/123456789/704
dc.description International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-6 Issue-3, July 2016, P. 55-59 en_US
dc.description.abstract In this work, the reservoir water level has been predicted using one of the soft computing techniques named Artificial Neural Network. The reservoir water level is influenced by many parameters. Among which the most influencing parameters have been considered here: amount of rainfall, temperature and evaporation. For this analysis, the reservoir made on Shetrunji River Dam in Dhari, Amreli district, Gujarat, India has been chosen as it was overflown seven times in last ten years. This shows the importance of water level prediction at this particular reservoir. The Neural Network is trained using the past data collected and further used to predict water level for the unknown data. The approach of the multiple regression is also shown for its comparison with the Soft computing approach. Computations and experimental works were done by programming in software MATLAB. Such modeling is useful for planning and decision making of opening gates for reservoir operation particularly during monsoon and water scarcity en_US
dc.language.iso en en_US
dc.publisher Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. en_US
dc.subject Terms—Soft Computing en_US
dc.subject Artificial Neural Network en_US
dc.subject Regression en_US
dc.subject Back Propagation Algorithm en_US
dc.subject Reservoir water level Prediction en_US
dc.title Application of soft computing techniques to predict reservoir water level en_US
dc.type Article en_US


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