Soil Pore Water Pressure and River Suspended Sediment Modelling using Artificial Neural Networks

• Main Author: Ul Mustafa, Muhammad Raza
• Format: Thesis
• Language: English
• Published: 2012
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://utpedia.utp.edu.my/21616/1/2012%20-CIVIL%20-%20SOIL%20PORE%20WATER%20PRESSURE%20AND%20RIVER%20SUSPENDED%20SEDIMENT%20MODELLING%20USING%20ARTIFICIAL%20NEURAL%20NETWORKS%20-%20MUHAMMAD%20RAZA%20UL%20MUSTAFA.pdf; http://utpedia.utp.edu.my/21616/

Prediction of water resources variables required for design and modelling purposes is subjected to various degrees of uncertainties, ambiguities and challenges. This lack of clarity is because of inappropriate or partial implementation of nonlinear mathematical modelling techniques, lack of relevant data and the degree of complexity involved in representing the physical process in a predictive modeL Artificial Neural Network (ANN) is a powerful and robust computational technique and has recently been widely and successfully used as a prediction tool in various fields including water resources engineering. ANN models are mostly preferred as a predictive tool because of their ability to map nonlinear patterns between the dependent and independent variables without a mathematical description of the physical process involved. This study reports the attempts and associated findings in predicting some hydrologic variables using the A1--.'N technique. Since the number of hydrologic variables relevant to water resources engineering is very large, the number of variables investigated in this study was restricted to two namely, the soil pore water pressure and river suspended sediment concentration. These two variables were investigated on the premise that (i) sufficient field data on the time series of these two dependant variables and associated independent variables were obtainable; (ii) it was concluded from literature review that no attempt has yet been made to predict soil pore water pressure responses to rainfall using ANN and the issue has great relevance to slope stability and hill-slope hydrological studies (iii) even though attempts for suspended sediment prediction using ANN have been reported in literature, hardly any attempt has been made to assess the suitability of different ANN training algorithms in predicting river suspended sediment concentration.