Development of Hydro-Environment Model for Maintaining a Reservoir using Artificial Intelligence
Rohit Anumarlapudi1, Naga Chaitanya Kavuri2
1Rohit Anumarlapudi, Department of Civil Engineering, Koneru Lakshmaiah Educational Foundation Deemed to be University, Guntur (A.P), India.
2Dr. K. Nagachaitanya Kavuri, Department of Civil Engineering, Koneru Lakshmaiah Educational Foundation Deemed to be University, Guntur (A.P), India.
Manuscript received on 04 May 2019 | Revised Manuscript received on 16 May 2019 | Manuscript Published on 28 May 2019 | PP: 836-839 | Volume-7 Issue-6C2 April 2019 | Retrieval Number: F11540476C219/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Forecasting and maintenance plays an important role for optimal reservoir operations. Present study mainly refers in developing an Artificial Intelligence (AI) model which helps in maintaining reservoir and amplify the decision making scientifically. In this development process, multi-layer perceptron, a method which can give the regression and correlate the parameters that influence the inflow of reservoir is used. Parameters like rainfall (mm), temperature (°C), land-use land-cover and relative humidity (Rh %) data is gathered from Andhra Pradesh State Disaster Management Authority (APSDMA). To obtain this correlation, 8years of data is collected with reference to Prakasam barrage upstream up to pulichintala project, Krishna district, Andhra Pradesh, India. These collected data are shaped into matrix form and tested using different training algorithms like Levenberg–Marquardt, Bayesian regularization and scaled conjugate gradient algorithms. From theabove-mentionedmodels Levenberg–Marquardtand Bayesian regularizationalgorithms exhibits better performance and accuracy compared to scaled conjugate gradient algorithm.
Keywords: Artificial Intelligence, Hydro-Environment, Forecasting, Multi-Layer Perceptron.
Scope of the Article: Building and Environmental Acoustics