Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors

Gazzaz, Nabeel M.; Yusoff, Mohd Kamil; Aris, Ahmad Zaharin; Juahir, Hafizan; Ramli, Mohammad Firuz

doi:10.1016/j.marpolbul.2012.08.005

Cited by 330 publications

(170 citation statements)

References 69 publications

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“…For example, water utilized for irrigation must be completely minimal in dissolved minerals to avoid salination of soils; water for recreation using should reach criteria for fecal coli forms; and potable water must be safe for drinking and cleaning [6][7][8]. Therefore, traditional method of water quality assessment can be described as the analysis of the biological, chemical, and physical properties of water in guide to natural quality, human health impacts, and wanted uses [9,10]. For instance, The Physico-chemical parameters and some metal concentrations were measured to determine the water quality of the Beyler Reservoir Kastamonu-Turkey that the reservoir has a considerably high quality water [11].…”

Section: Introductionmentioning

confidence: 99%

Water Quality Index (WQI) for Main Source of Drinking Water (Karaçomak Dam) in Kastamonu City, Turkey

Imneisi¹,

Aydin²

2016

J Environ Anal Toxicol

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Section: Introductionmentioning

confidence: 99%

Water Quality Index (WQI) for Main Source of Drinking Water (Karaçomak Dam) in Kastamonu City, Turkey

Imneisi¹,

Aydin²

2016

J Environ Anal Toxicol

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“…Debels et al (2005) and Koçer and Sevgili (2014) used PCA to cluster several parameters into "certain groups" and then removed some of them to develop a WQI with a minimum number of parameters. Gazzaz et al (2012) employed the PFA to reduce number of water quality parameters by considering only parameters that exhibit large factor loadings for subsequent analysis.…”

Section: Methodsmentioning

confidence: 99%

“…The assessment of these three categories by field monitoring of rivers provide basic data for detecting trends, for providing water quality information to water authorities, and for making recommendations for future actions. This assessment is usually conducted by referring to natural water quality, human health and intended uses (Pesce & Wunderlin 2000;Gazzaz et al 2012). In fact, monitoring all parameters with different sources of pollution entering a river basin is laborious and expensive.…”

Section: Introductionmentioning

confidence: 99%

Development of river water quality indices—a review

Sutadian

Muttil

Yilmaz

et al. 2015

Environ Monit Assess

359

210

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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.

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“…This relationship is approximately the same as the relationship between point F, which is nearest to the prediction center by Euclidean distance and its next point, namely: Y(q+1)=a+bY(q). The specific steps are as follows： a. Reconstruct the phase space and determine the embedding delay time [8] and embedding dimension [9].…”

Section: Preparing the New File Withmentioning

confidence: 99%

Application of chaotic prediction model based on wavelet transform on water quality prediction

Zhang

Zou

Zhao

2016

IOP Conf. Ser.: Earth Environ. Sci.

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Abstract. Dissolved oxygen (DO) is closely related to water self-purification capacity. In order to better forecast its concentration, the chaotic prediction model, based on the wavelet transform, is proposed and applied to a certain monitoring section of the Mentougou area of the Haihe River Basin. The result is compared with the simple application of the chaotic prediction model. The study indicates that the new model aligns better with the real data and has a higher accuracy. Therefore, it will provide significant decision support for water protection and water environment treatment. IntroductionThe first paragraph after a heading is not indented (Bodytext style). Along with economic development and improvements in people's standard of living, bodies of water are facing more and more serious pollution and destruction. However, polluted water has certain self-purification abilities. It can reduce the concentration of pollutants to normal levels by physical, chemical and biological functions. Dissolved oxygen (DO) is the basis for studying water bodies' self-purification abilities. Consequently, research concerning trends of DO in water and predicting its concentration has substantial theoretical and realistic significance. Because of the complexity of the water environment and the limitation of single methods, combined forecasting methods have been widely used, such as the radial basis function (RBF) water quality prediction model based on particle swarm optimization, the BP neural network prediction model based on ant-colony optimization and the gray and neural network combination model [1][2][3]. These methods led to accurate predictions of water quality trends and concentrations of pollutants.Water environment systems are quite complex. The DO value could be affected by plenty of factors. As a signal processing technology, the wavelet transform has been widely used recently. The decomposition and reconstruction of original signals can lead to better extraction of the feature information of the original signal [4]. The chaotic prediction model based on the wavelet transform is established by combining the wavelet transform and the chaotic prediction model, which has simple calculations and high precision. Comparing it with the chaotic prediction model proves the validity of the new model.

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Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors

Cited by 330 publications

References 69 publications

Water Quality Index (WQI) for Main Source of Drinking Water (Karaçomak Dam) in Kastamonu City, Turkey

Water Quality Index (WQI) for Main Source of Drinking Water (Karaçomak Dam) in Kastamonu City, Turkey

Development of river water quality indices—a review

Application of chaotic prediction model based on wavelet transform on water quality prediction

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