The prediction of suspended solids of river in forested catchment using artificial neural network

Juahir, Hafizan and Ramli, Mohammad Firuz and Yusoff, Mohd. Kamil and Mohd Zain, Sharifuddin and Oksel, O. and Mat Perak, Z. and Haron, A.R. (2008) The prediction of suspended solids of river in forested catchment using artificial neural network. The Malaysian Forester, 71 (1). p. 10. ISSN 0302-2935

Full text not available from this repository.


This study presents an artificial neural network (ANN) model that is able to predict suspended solids concentrations in forested catchment namely Berring River, Kelantan, Malaysia.The network was trained using data collected during a period of 13 days in April 2001. The sampling location was established in the middle section of the river for collecting water samples. The study was carried out for a duration of two weeks in April 2001. The water sample was collected at 60% of the total depth from the river bed for every two hours starting from 6:00 am to 12:00 midnight for the whole duration of the study period. In this study five parameters were selected as input parameter for the network which are turbidity, flow velocity, depth, width, and weather condition of during the sampling period, while suspended solids as desire output. The data fed to the neural network were divided into two set: a training set and testing set. 116 of the data were used in training set and 24 remained as testing set. A network of the model was detected automatically by the network to give good predictions for both training and testing data set. A partitioning method of the connection weights of the network was used to study the relative percentage contribution of each of the input variables. It was found that turbidity and river width gives 73.03% and 24.73% each. The performance of the neural network model was measured by computing the correlation coefficient which gives the value of 0.93. It’s shown that the neural network gives superior predictions. Based on the results of this study, ANN modeling appears to be a promising technique for the prediction of suspended solids. Dynamic Metadata(s)

Item Type:Article
Keyword:Artificial neural network; Backpropagation; Suspended solids; Turbidity; River; Forested catchment
Subject:Forest management - Kelantan -Berring River.
Subject:Forest - Neural networks (Computer science).
Subject:Suspended sediments - Kelantan -Berring River.
Faculty or Institute:Faculty of Environmental Studies
ID Code:17277
Deposited By: Khairil Ridzuan Khahirullah
Deposited On:05 Jan 2012 07:31
Last Modified:05 Jan 2012 07:31

Repository Staff Only: Edit item detail

Document Download Statistics

This item has been downloaded for since 05 Jan 2012 07:31.

View statistics for "The prediction of suspended solids of river in forested catchment using artificial neural network"

Universiti Putra Malaysia Institutional Repository

Universiti Putra Malaysia Institutional Repository is an on-line digital archive that serves as a central collection and storage of scientific information and research at the Universiti Putra Malaysia.

Currently, the collections deposited in the IR consists of Master and PhD theses, Master and PhD Project Report, Journal Articles, Journal Bulletins, Conference Papers, UPM News, Newspaper Cuttings, Patents and Inaugural Lectures.

As the policy of the university does not permit users to view thesis in full text, access is only given to the first 24 pages only.