Daily Suspended Sediment Discharge Prediction Using Multiple Linear Regression and Artificial Neural Network

Sideng, Uca and Toriman, Ekhwan and Jaafar, Othman and Maru, Rosmini and Arfan, Amal and Ahmar, Ansari Saleh (2018) Daily Suspended Sediment Discharge Prediction Using Multiple Linear Regression and Artificial Neural Network. In: Joint Workshop of KO2PI 2017 & ICMSTEA 2016.

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Abstract

Prediction of suspended sediment discharge in a catchments area is very important because it can be used to evaluation the erosion hazard, management of its water resources, water quality, hydrology project management (dams, reservoirs, and irrigation) and to determine the extent of the damage that occurred in the catchments. Multiple Linear Regression analysis and artificial neural network can be used to predict the amount of daily suspended sediment discharge. Regression analysis using the least square method, whereas artificial neural networks using Radial Basis Function (RBF) and feedforward multilayer perceptron with three learning algorithms namely Levenberg-Marquardt (LM), Scaled Conjugate Descent (SCD) and Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFGS). The number neuron of hidden layer is three to sixteen, while in output layer only one neuron because only one output target. The mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2 ) and coefficient of efficiency (CE) of the multiple linear regression (MLRg) value Model 2 (6 input variable independent) has the lowest the value of MAE and RMSE (0.0000002 and 13.6039) and highest R2 and CE (0.9971 and 0.9971). When compared between LM, SCG and RBF, the BFGS model structure 3-7-1 is the better and more accurate to prediction suspended sediment discharge in Jenderam catchment. The performance value in testing process, MAE and RMSE (13.5769 and 17.9011) is smallest, meanwhile R2 and CE (0.9999 and 0.9998) is the highest if it compared with the another BFGS Quasi-Newton model (6-3-1, 9-10-1 and 12-12-1). Based on the performance statistics value, MLRg, LM, SCG, BFGS and RBF suitable and accurately for prediction by modeling the non linear complex behavior of suspended sediment responses to rainfall, water depth and discharge. The comparison between artificial neural network (ANN) and MLRg, the MLRg Model 2 accurately for to prediction suspended sediment discharge (kg/day) in Jenderan catchment area.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	FMIPA > Geografi PASCASARJANA > Geografi PASCASARJANA > Geografi
Divisions:	FAKULTAS MIPA
Depositing User:	Zainatun
Date Deposited:	24 Jun 2023 01:25
Last Modified:	27 Jun 2023 19:28
URI:	http://eprints.unm.ac.id/id/eprint/30872

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