This paper presents an optimization model to simulate short-term pair-wise spot-market trading of surface water abstraction licenses (water rights). The approach uses a node-arc multicommodity formulation that tracks individual supplier-receiver transactions in a water resource network. This enables accounting for transaction costs between individual buyer-seller pairs and abstractor-specific r...

This paper presents a novel methodology to perform adaptive Water Demand Forecasting (WDF) for up to 24 hours ahead with the aim to support near real-time operational management of smart Water Distribution Systems (WDSs). The novel WDF methodology is exclusively based on the analysis of water demand time series (i.e., demand signals) and makes use of Evolutionary Artificial Neural Networks (EAN...

Urmia Lake in northwestern Iran has been facing critical water level shrinkage in recent years with ‎serious environmental consequences. It is expected that with the current trend, the lake will be ‎completely dried out in a few years from now. This study attempts to assess the impacts of human ‎activities on water level fluctuations of Urmia Lake by developing a comprehensive hydrologic ‎simul...

In light of the current and future threats to global water security the current research focuses on trialing interventions to promote urban water conservation. We report an experimental study designed to test the long-term impact of three different interventions on household water consumption in South East Queensland. Participants from 221 households were recruited and completed an initial surv...

A comprehensive evaluation framework that can assess a wide range of water supply and demand management policy options in terms of economic, social, environmental, risk-based, and functional performance is crucial to ascertain their level of sustainability. However, such a detailed, generic, and holistic policy evaluation framework is not found in the literature. This paper reviews studies to e...

This paper presents a residential water demand forecasting model using a back propagation neural network (BPNN) in the context of residential buildings in Korea. The water demand of a building demonstrates a highly complex and non-linear phenomenon reflecting such features as geographic and climatic and special types of buildings. We describe the impact of several potential determinant factors ...