Md. Mahmudul Haque

Quantification of Water Savings due to Drought Restrictions in Water Demand Forecasting Models

AbstractThis paper presents a technique to quantify water savings due to implementation of water restrictions by adopting water restriction indexes as a continuous numerical predictor variable in regression analysis. The adopted modeling technique compares four methods: yearly base difference method, weighted average method, before and after method, and expected use method. These methods are applied to single and multiple dwelling residential sectors in the Blue Mountains region, Australia. In the study, three forms of multiple regression techniques are adopted: raw data, semi-log, and log-log. The model performances are evaluated by a number of statistics such as relative error, Nash-Sutcliffe coefficient, and percentage bias. Moreover, the potential of using the water restriction savings and water conservation savings as continuous predictor variables in the water demand forecasting model is investigated. The performances of different modeling techniques are evaluated using split-sample and leave-one-ou...

Flood estimation in ungauged catchments: application of artificial intelligence based methods for Eastern Australia

Regional flood frequency analysis (RFFA) is used to estimate design floods in ungauged and data poor gauged catchments, which involves the transfer of flood characteristics from gauged to ungauged catchments. In Australia, RFFA methods have focused on the application of empirical methods based on linear forms of traditional models such as the probabilistic rational method, the index flood method and the quantile regression technique (QRT). In contrast to these traditional linear-models, non-linear methods such as artificial neural networks (ANNs) and gene expression programming (GEP) can be applied to RFFA problems. The particular advantage of these techniques is that they do not impose a model structure on the data, and they can better deal with non-linearity of the input and output relationship in regional flood modelling. These non-linear techniques have been applied successfully in a wide range of hydrological problems; however, there have been only limited applications of these techniques in RFFA problems, particularly in Australia. This paper focuses on the development and testing of the ANNs and GEP based RFFA models for eastern parts of Australia. This involves relating flood quantiles to catchment characteristics so that the developed prediction models can be used to estimate design floods in ungauged site. A data set comprising of 452 stations from eastern Australia was used to develop the new RFFA models. An independent testing shows that the non-linear methods are quite successful in RFFA and can be used as an alternative method to the more traditional approaches currently used in eastern Australia. The results based on ANN and GEP-based RFFA techniques have been found to outperform the ordinary least squares based QRT (linear technique).

A comparison of linear and nonlinear regression modelling for forecasting long term urban water demand : a case study for Blue Mountains Water Supply System in Australia

Prediction of long term water demand is necessary to assess the future adequacy of water resources, to attain an efficient allocation of water supplies among competing water users and to ensure long-term water sustainability. In order to predict future water demand and assess the effects of future climate and other factors on water demand, suitable mathematical models are needed. The study compares a multiple linear and nonlinear regression model to forecast monthly water demand in the Blue Mountains Water Supply System, Australia. The performance of the developed models are assessed through the relative error (RE), the coefficient of determination (R2), the percent bias (PBIAS) and the accuracy factor (Af), computed from the observed and model predicted water demand values. The RE, R2, PBIAS, Af , values are found to be 0.46%, 0.88, 2.07% and 1.04, respectively for multiple linear regression model and 2.49%, 0.30, -20.79% and 1.21, respectively for multiple nonlinear regression model. The results of the study show that the developed multiple linear regression model is capable of predicting water demand more accurately than multiple nonlinear regression model.