The water demand of a city is a complex and non linear function of climatic, socioeconomic, institutional and management variables. Identifying the prominent variables among these is essential in order to adequately predict water demand, and to plan and manage water resources and the supply systems. Further, the need for such identification becomes more pronounced when data constraints arise. The objective of this study was to establish, using correlation and sensitivity analyses, a minimum set of variables required to predict water demand with significant accuracy. Artificial Neural Networks (ANN) models were developed to predict short-term (daily) and medium-term (monthly) demands for Bangkok. Using meteorological and water utility variables for short-term prediction, and different ANN architecture, 16 sets of models with a 1-, 2-and 3-day lead period were developed. Although the best fit models for the three lead periods used different input variables, prediction accuracies over 98% were achieved by using only the historic daily demand (HDD) as the explanatory variable. Similarly, for medium-term prediction, 11 sets of models with lead periods of 1-, 2-and 6-months were developed, using meteorological, water utility and socioeconomic variables. The best fit models for the three lead periods used all explanatory variables but prediction accuracies of more than 98% were obtained by downsizing the variable set. The meteorological variables have a greater influence on medium-term prediction as compared to short-term prediction, suggesting that future water demand in Bangkok could be significantly affected by climate change.
Water security is a global concern because of the growing impact of human activities and climate change on water resources. Studies had been performed at global, country, and city level to assess the water security issues. However, assessment of water security at a domestic scale is lacking. This paper develops a new domestic water security assessment framework accounting for water supply, sanitation, and hygiene through twelve indicators. Water supply, sanitation, and hygiene are central to key water-related sustainable development goals. The framework is subsequently applied to the city of Addis Ababa, Ethiopia. From the domestic water security assessment of Addis Ababa, the water supply dimension was found to be of good level, whereas the sanitation and hygiene dimensions were of poor and fair level, respectively, indicating both a challenge and an opportunity for development. Because the analysis is spatially explicit at the city-branch level (in Addis), variation in domestic water security performance across Addis Ababa can be assessed, allowing efficient targeting of scant resources (financial, technical, personnel). Analysis further shows that a lack of institutional capacity within the utility, existing infrastructure leading to ‘lock-in’ and hindering maintenance and upgrade initiatives, and an unreliable power supply are the main issues leading to poor domestic water security in the study city. These areas should be tackled to improve the current situation and mitigate future problems. The developed framework is generic enough to be applied to other urban and peri-urban areas, yet provides planners and policy makers with specific information on domestic water security considering water supply, sanitation and hygiene, and accounting for within-city variability. This work could therefore have practical applicability for water service providers.
97.5% of the water utilities in Japan serve less than 50,000 customers, and are called small water utilities. The Performance Indicator system in Japan, used to evaluate the performance of various aspects of the supply system, currently has 137 items, which are too many in number for the small utilities to adopt because of resource and financial constraints. The objective of this study is to, thus, revise the existing PI system to arrive at a reduced, relevant and practical structure that provides enough information to rationally evaluate small water supply systems in Japan. Principal Component Analysis was used to reduce the dimensionality of the original data. The results suggest that only 9 components, consisting of 33 items (called 9-cPIS), are sufficient for evaluating the small water utilities. The effectiveness of the 9-cPIS in benchmarking, evaluating business models, and the planning and management of the water utilities has been discussed further.
ABSTRACT:The artificial neural network (ANN), a data-driven approach, is a powerful tool for forecasting rainfall. However, selecting the appropriate explanatory variables in order to develop ANN models for this purpose is a major challenge. Recent studies in various fields have highlighted the usefulness of the mutual information (MI) technique in identifying explanatory variables for application in non-linear problems, which, however, has largely been unexplored in forecasting rainfall. The present study was carried out to fill this knowledge gap. Three ANN models were developed, with different explanatory variables, to forecast the rainfall in Mumbai, India. Model A used temporal data of past rainfall events, Model B used selected meteorological data apart from rainfall and Model C used those variables identified by the MI technique. When the results of Model C were compared with those of Models A and B, a reduction of 5.79 and 4.11% in normalized mean square error, respectively, 16.66 and 12.90% improvement in efficiency index, respectively, and 3.22 and 4.24% reduction in the root mean square error, respectively, were observed. Thus, this study highlights the superiority of the MI technique in selecting explanatory variables for ANN modelling, not only because of the enhanced performance of the model with respect to various indicators but also because this performance has been achieved with a simple ANN architecture.
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