A novel Bayesian maximum entropy-based approach for optimal design of water quality monitoring networks in rivers

Salman, Reza; Nikoo, Mohammad Reza; Shojaeezadeh, Shahab Aldin; Beiglou, Pouyan Hatami Bahman; Sadegh, Mojtaba; Adamowski, Jan; Alamdari, Nasrin

doi:10.1016/j.jhydrol.2021.126822

Cited by 11 publications

(9 citation statements)

References 46 publications

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“…However, the workflow of the developed method was built according to three components ( Fig. 1 ): 1) Data processing and calculation of the Entropy Weighted Water Quality Index (EWQI), which is useful to reduce subjectivity in assessing groundwater quality [3] , [4] , [5] ; 2) Virtual generation of groundwater quality samples using MVD-VSG and developing DNN models. DNN model is theoretically more accurate than traditional ML models as concluded in previous comparative studies [ 6 , 7 ].…”

Section: Methods Descriptionmentioning

confidence: 99%

“…It can evaluate an amount and a degree of pertinent information from disorderly and uncertain data pertaining to predicting the output of a probabilistic event. Its application was conducted in various hydrological studies, namely: drought indices, flood risk evaluation, and water quality assessment [ 3 , 12 , 13 ]. Hence, the EWQI was embedded in the method to minimize the subjectivity in assessing groundwater quality.…”

Section: Methods Descriptionmentioning

confidence: 99%

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An approach based on multivariate distribution and Gaussian copulas to predict groundwater quality using DNN models in a data scarce environment

Nafii¹,

Lamane²,

Taleb³

et al. 2023

MethodsX

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Section: Methods Descriptionmentioning

confidence: 99%

Section: Methods Descriptionmentioning

confidence: 99%

An approach based on multivariate distribution and Gaussian copulas to predict groundwater quality using DNN models in a data scarce environment

Nafii¹,

Lamane²,

Taleb³

et al. 2023

MethodsX

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“…The BME analysis involves three steps namely the prior stage, the meta-prior stage and the posterior stage [4,17,18]. At the prior stage, data on the general knowledge is collected and processed to build the prior distribution.…”

Section: Brief Bckground On Bayesian Maximum Entropy*mentioning

confidence: 99%

On the Impacts of Hard Data Patterns on Bayesian Maximum Entropy Performance: Simulation-based Analysis

E.¹,

E.²,

T³

et al. 2022

Preprint

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Bayesian Maximum Entropy (BME) is increasingly used in predicting and mapping spatio-temporal data. However, studies that have fully evaluated its robustness empirically are rare. Therefore, this research examined empirically the effect of skewness, sample size and spatial dependency level using simulated data. We considered symmetric data, data positively skewed by 1, 3, 6 and 9, data with weak, moderate, and strong spatial dependency levels, and sample sizes from 100 to 500 at the interval length of 50. The results showed that the variation of sample sizes and spatial dependency levels do not affect the Mean Square Error (MSE) and bias of BME prediction. However, skewness affects the MSE of prediction but does not affect the bias. This result indicates that BME is robust to sample size and is unbiased. Despite the significant difference due to skewness, a graphical plot showed values of MSE close to zero, suggesting that BME can be considered robust to skewness.

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“…Rivers are an indispensable water resource and can provide drinking water that is essential for human livelihood, industrial water supply and demand, and valuable natural habitats [1,2]. According to the United Nations Environment Programme (UNEP) report, water pollution has worsened since the 1990s in many rivers in Latin America, Africa, and Asia [3].…”

Section: Introductionmentioning

confidence: 99%

“…The methodology uses linear regressive techniques while taking into account the spatial and temporal autocorrelation of residuals. [2] presented a Bayesian maximum entropy (BME)-based framework to optimize the locations of water quality monitoring stations (WQMS) in rivers to obtain the highest value of information with the lowest number of monitoring stations. In this study, BME is employed as a flexible, accurate, and effective approach in geostatistics to optimize the spatiotemporal coverage of potential WQMS.…”

Section: Introductionmentioning

confidence: 99%

Optimal Location of Water Quality Monitoring Stations Using an Artificial Neural Network Modeling in the Qarah-Chay River Basin, Iran

Goudarzi

Hedayatiaghmashhadi

Kazemi

et al. 2022

Water

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The economic development, livelihood and drinking water of millions of people in the central plateau of Iran depend on the Qarah-Chay River, but due to a lack of inappropriate monitoring, it has been exposed to destruction and pollution. Consequently, an assessment of the river’s water quality is of utmost importance for both the management of human health and the maintenance of a safe environment, which can be achieved by determining the best locations for pollution monitoring stations along rivers. In this study, artificial neural networks (ANNs) has been used to optimize the locations for Qarah-Chay River monitoring stations in Markazi province, Iran. The data are collected based on the Iranian Water Quality Index (IRWQI), the US National Sanitation Foundation Water Quality Index (NSFWQI) and the Oregon Water Quality Index (OWQI). The database is given to a multilayer perceptron (MLP) neural network along with a geographic information system (GIS). The output of this study identified six pollution monitoring stations on the river, which are mainly downstream due to the accumulation of land uses and the concentration of pollution. The gradient of the MLP network training courses model from the proposed monitoring stations is 0.062299. In addition, the performance evaluation criteria of the proposed MLP model for F1-score, recall, precision and accuracy were 0.85, 0.84, 0.88 and 0.88, respectively. The results obtained help managers to properly monitor the river’s water resources with accuracy, efficiency and lower cost; furthermore, the findings were able to provide scientific references for river water quality monitoring and river ecosystem protection.

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A novel Bayesian maximum entropy-based approach for optimal design of water quality monitoring networks in rivers

Cited by 11 publications

References 46 publications

An approach based on multivariate distribution and Gaussian copulas to predict groundwater quality using DNN models in a data scarce environment

An approach based on multivariate distribution and Gaussian copulas to predict groundwater quality using DNN models in a data scarce environment

On the Impacts of Hard Data Patterns on Bayesian Maximum Entropy Performance: Simulation-based Analysis

Optimal Location of Water Quality Monitoring Stations Using an Artificial Neural Network Modeling in the Qarah-Chay River Basin, Iran

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