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

Uca, Mohd. Ekhwan Toriman, Othman Jaafar, Rosmini Maru, Amal Arfan, Ansari Saleh Ahmar

Research output: Contribution to journalConference article

2 Citations (Scopus)

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.

Original languageEnglish
Article number012030
JournalJournal of Physics: Conference Series
Volume954
Issue number1
DOIs
Publication statusPublished - 22 Feb 2018

Fingerprint

regression analysis
sediments
newton
root-mean-square errors
predictions
coefficients
neurons
project management
hydrology
water resources
irrigation
water quality
self organizing systems
dams
water depth
output
descent
least squares method
learning
hazards

Keywords

  • artificial neural network
  • Jenderam catchment
  • multiple linear regression
  • Suspended sediment discharge

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Daily Suspended Sediment Discharge Prediction Using Multiple Linear Regression and Artificial Neural Network. / Uca, ; Toriman, Mohd. Ekhwan; Jaafar, Othman; Maru, Rosmini; Arfan, Amal; Ahmar, Ansari Saleh.

In: Journal of Physics: Conference Series, Vol. 954, No. 1, 012030, 22.02.2018.

Research output: Contribution to journalConference article

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