Hybridizing the Machine Learning Techniques for Prediction of Sediment Yield
Bezawada Supriya1, Arvind Yadav2, Guda Navya3, Penke Satyannarayana4
1Arvind Yadav*, Department of Electronics and Computer Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, (A.P.), India.
2Bezawada Supriya1, Department of Electronics and Computer Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram-522502, (A.P.), India.
3Guda Navya1, Department of Electronics and Computer Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram-522502, (A.P.), India.
4Penke Satyannarayana1,, Department of Electronics and Computer Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, (A.P.), India.
Manuscript received on January 13, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on February 10, 2020. | PP: 992-996 | Volume-9 Issue-4, February 2020. | Retrieval Number: C8911019320/2020©BEIESP | DOI: 10.35940/ijitee.C8911.029420
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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: Rivers are an integral part of the hydrologic cycle and are the major dynamic geologic agents that play major role for transformation of sediments from land to the oceans. Sedimentation is the biggest problem. Evaluation of suspended sediment yield is an essential parameter under the assessment on Dam filling, protecting of aquatic organism and wildlife habitats, understanding the flood capacity and hydroelectric equipment in hydro-electric power. The assurance of sediment yield through different traditional way isn’t exactly correct because of the participation of different complex processes. There are many limitations of traditional methods but it can be overcome by artificial intelligence techniques. So, in this study, the MOGA-ANN (Multi-objective genetic algorithm based artificial neural network) hybrid artificial intelligence method is used to estimate the sediment yield in Krishna river basin, India. The research done for evaluation of the suspended sediment load by taking 20 years of data from Vijayawada, gauging station which is the downward station in Krishna river. The proposed MOGA-ANN model provided low root mean square error (0.03354) and high correlation coefficient (0.9214) during test phase. It exhibited satisfactory performance.
Keywords: Water Discharge, Artificial Neural Network, Multi-Objective Genetic Algorithm, Suspended Sediment yield, Krishna River.
Scope of the Article: Artificial Intelligence and machine learning