A review on integration of artificial intelligence into water quality modelling

Abstract

With the development of computing technology, numerical models are often employed to simulate flow and water quality processes in coastal environments. However, the emphasis has conventionally been placed on algorithmic procedures to solve specific problems. These numerical models, being insufficiently user-friendly, lack knowledge transfers in model interpretation. This results in significant constraints on model uses and large gaps between model developers and practitioners. It is a difficult task for novice application users to select an appropriate numerical model. It is desirable to incorporate the existing heuristic knowledge about model manipulation and to furnish intelligent manipulation of calibration parameters. The advancement in artificial intelligence (AI) during the past decade rendered it possible to integrate the technologies into numerical modelling systems in order to bridge the gaps. The objective of this paper is to review the current state-of-the-art of the integration of AI into water quality modelling. Algorithms and methods studied include knowledge-based system, genetic algorithm, artificial neural network, and fuzzy inference system. These techniques can contribute to the integrated model in different aspects and may not be mutually exclusive to one another. Some future directions for further development and their potentials are explored and presented.

Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models

“…(ASCE 2000a, b). These models were used by many researchers for engineering problems (Yilmaz and Kaynar 2011;Wang et al 2010; Bandyopadhyay and Chattopadhyay 2007), water quality study (Singh et al 2009 andChau 2006) and hydrological and hydraulic modeling (ASCE 2000a, b). In the present study, two kinds of ANN, i.e., the multi-layer perceptron (MLP) with backpropagation algorithm and Radial basis neural networks (RBNN), were used.…”

A Fuzzy Water Quality Index for Watershed Quality Analysis and Management

“…These concepts are discussed in depth in Bárdossy (1995), Yen e Langari (1999), Ross (2004), Cruz (2004) and Caldeira et al (2007). The concept of fuzzy sets for modeling water quality was considered by Dahiya (2007), Nasiri et al (2007) Chau (2006, Ocampo-Duque et al (2006), Icaga (2007), and Chang et al (2001), Lermontov et al (2009), Ramesh et al (2010, Taner et al (2011).…”

“…In this context, new, alternative integration methods are being developed. Artificial Intelligence has thus become a tool for modeling water quality (Chau, 2006). Traditional methodologies cannot classify and quantify environmental effects of a subjective nature or even provide formalism for…”

Monthly Forecasting of Water Quality Parameters within Bayesian Networks: A Case Study of Honolulu, Pacific Ocean

“…To predict water parameters such as BOD, electrical conductivity and chloride, Ahmad et al (2001) developed the multiplicative ARIMA models for the river Ganges in India [3]. Chau (2006) reviewed the integration of AI techniques including knowledge-based system, genetic algorithm, artificial neural network, and fuzzy inference system into water quality modeling [4]. Said et al (2011) used hydrographic data in order to investigate changes in Atlantic water characteristics as a result of natural and anthropogenic activities [5].…”