The use of water quality indices (WQIs) as a tool to evaluate the status of water quality in rivers has been introduced since the 1960s. The WQI transforms selected water quality parameters into a dimensionless number so that changes in river water quality at any particular location and time could be presented in a simple and easily understandable manner. Although many WQIs have been developed, there is no worldwide accepted method for implementing the steps used for developing a WQI. Thus, there is a continuing interest to develop accurate WQIs that suit a local or regional area. This paper aims to provide significant contribution to the development of future river WQIs through a review of 30 existing WQIs based on the four steps needed to develop a WQI. These steps are the selection of parameters, the generation of sub-indices, the generation of parameter weights and the aggregation process to compute the final index value. From the 30 reviewed WQIs, 7 were identified as most important based on their wider use and they were discussed in detail. It was observed that a major factor that influences wider use of a WQI is the support provided by the government and authorities to implement a WQI as the main tool to evaluate the status of rivers. Since there is a lot of subjectivity and uncertainty involved in the steps for developing and applying a WQI, it is recommended that the opinion of local water quality experts is taken, especially in the first three steps (through techniques like Delphi method). It was also observed that uncertainty and sensitivity analysis was rarely undertaken to reduce uncertainty and hence such an analysis is recommended for future studies.

In the recent past, machine learning (ML) techniques such as artificial neural networks (ANN) have been increasingly used to model algal bloom dynamics. In the present paper, along with ANN, we select genetic programming (GP) for modelling and prediction of algal blooms in Tolo Harbour, Hong Kong. The study of the weights of the trained ANN and also the GP-evolved equations shows that they correctly identify the ecologically significant variables. Analysis of various ANN and GP scenarios indicates that good predictions of longterm trends in algal biomass can be obtained using only chlorophyll-a as input. The results indicate that the use of biweekly data can simulate long-term trends of algal biomass reasonably well, but it is not ideally suited to give short-term algal bloom predictions.

Abstract:In the past few decades, there have been extensive efforts on measuring sustainability. One example is the development of assessment tools based on sustainability indicators. Several individuals and organisations have suggested various indices for assessing sustainability. This paper focuses on the review of water sustainability assessment using the indicator-based approach. It discusses major definitions of sustainable development that have been proposed and more specific concepts of sustainability based on sustainability principles and criteria. It then proceeds with the review of existing definitions, principles and guidelines on sustainable water resource management. The paper then explores elements of indicatorbased water sustainability assessment. These elements include the selection of components and indicators, obtaining sub-index values, weighting schemes for components and indicators, aggregation of components and indicators, robustness analysis of the index, and interpretation of the final index value. These six elements are explored considering four existing water sustainability indices and two other sustainability indices that are thought to be useful for the development and use of water sustainability indices.The review presented in this paper on indicator-based water sustainability assessment can provide significant inputs to water stakeholders worldwide for using existing indices, for customising existing indices for their applications, and for developing new water sustainability indices. These indices can provide information on current conditions of water resources, including identifying all factors contributing to the improvement of water resources. This information can be used to communicate the current status of existing water resources to the wider community. Also, the water sustainability indices can be used to assist decision makers to prioritise issues, challenges and programs related to water resource management.

Genetic programming (GP), a relatively new evolutionary technique, is demonstrated in this study to evolve codes for the solution of problems. First, a simple example in the area of symbolic regression is considered. GP is then applied to real‐time runoff forecasting for the Orgeval catchment in France. In this study, GP functions as an error updating scheme to complement a rainfall‐runoff model, MIKE11/NAM. Hourly runoff forecasts of different updating intervals are performed for forecast horizons of up to nine hours. The results show that the proposed updating scheme is able to predict the runoff quite accurately for all updating intervals considered and particularly for updating intervals not exceeding the time of concentration of the catchment. The results are also compared with those of an earlier study, by the World Meteorological Organization, in which autoregression and Kalman filter were used as the updating methods. Comparisons show that GP is a better updating tool for real‐time flow forecasting. Another important finding from this study is that nondimensionalizing the variables enhances the symbolic regression process significantly.