Monthly Suspended Sediment Load Estimation Using Artificial Neural Network and Traditional Models in Krishna River Basin, India
Arla Rama Krishna1, Arvind Yadav2, Thottempudi Bhavani3, Penke Satyannarayana4
1Arvind Yadav*, Department of Electronics and Computer Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
2Arla Rama Krishna, Department of Electronics and Computer Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India
3Thottempudi Bhavani, Department of Electronics and Computer Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
4Penke Satyannarayana, Department of Electronics and Computer Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
Manuscript received on December 17, 2019. | Revised Manuscript received on December 23, 2019. | Manuscript published on January 10, 2020. | PP: 613-618 | Volume-9 Issue-3, January 2020. | Retrieval Number: B7743129219/2020©BEIESP | DOI: 10.35940/ijitee.B7743.019320
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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: The measurement of sediment yield is essential for getting the information of the mass balance between sea and land. It is difficult to directly measure the suspended sediment because it takes more time and money. One of the most common pollutants in the aquatic environment is suspended sediments. The sediment loads in rivers are controlled by variables like canal slope, basin volume, precipitation seasonality and tectonic activity. Water discharge and water level are the major controlling factor for estimate the sediment load in the Krishna River. Artificial neural network (ANN) is used for sediment yield modeling in the Krishna River basin, India. The comparative results show that the ANN is the easiest model for the suspended sediment yield estimates and provides a satisfactory prediction for very high, medium and low values. It is also noted that the Multiple Linear Regressions (MLR) model predicted an many number of negative sediment outputs at lower values. This is entirely unreality because the suspended sediment result can not be negative in nature. The ANN is provided better results than traditional models. The proposed ANN model will be helpful where the sediment measures are not available.
Keywords: Back Propagation Algorithm, Suspended Sediment Yield, Krishna River, Artificial Neural Network
Scope of the Article: Neural information processing