Characterizing an unknown pollution source in groundwater resources systems using PSVM and PNN

Bashi-Azghadi, Seyyed Nasser; Kerachian, Reza; Bazargan‐Lari, Mohammad Reza; Solouki, Kazem

doi:10.1016/j.eswa.2010.04.019

Cited by 65 publications

(22 citation statements)

References 25 publications

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“…Recently, Bashi-Azghadi et al (2010) presented two methodologies for probabilistic characterizing an unknown pollution source in terms of location and amount of leakage using groundwater quality monitoring data. They used probabilistic Fig.…”

Section: Probabilistic Neural Networkmentioning

confidence: 99%

A probabilistic water quality index for river water quality assessment: a case study

Nikoo

Kerachian

Malakpour‐Estalaki

et al. 2010

Environ Monit Assess

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Available water quality indices have some limitations such as incorporating a limited number of water quality variables and providing deterministic outputs. This paper presents a hybrid probabilistic water quality index by utilizing fuzzy inference systems (FIS), Bayesian networks (BNs), and probabilistic neural networks (PNNs). The outputs of two traditional water quality indices, namely the indices proposed by the National Sanitation Foundation and the Canadian Council of Ministers of the Environment, are selected as inputs of the FIS. The FIS is trained based on the opinions of several water quality experts. Then the trained FIS is used in a Monte Carlo analysis to provide the required input-output data for training both the BN and PNN. The trained BN and PNN can be used for probabilistic water quality assessment using water quality monitoring data. The efficiency and applicability of the proposed methodology is evaluated using water quality data obtained from water quality monitoring system of the Jajrood River in Iran.

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Section: Probabilistic Neural Networkmentioning

confidence: 99%

A probabilistic water quality index for river water quality assessment: a case study

Nikoo

Kerachian

Malakpour‐Estalaki

et al. 2010

Environ Monit Assess

Self Cite

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“…A potential application in expert system research is to develop an intelligent vehicle to search autonomously for sources of hazardous chemicals or pollutants (Bashi-Azghadi, Kerachian, Bazargan-Lari, & Solouki, 2010;Zhou, Huang, & Chan, 2004), victims in earthquake wreckage (Hamp et al, 2014) using expert knowledge of chemical plumes. A critical problem in designing such a system is to construct a navigation mechanism which guides the vehicle to track a chemical plume towards its source.…”

Section: Introductionmentioning

confidence: 99%

Autonomous tracking of chemical plumes developed in both diffusive and turbulent airflow environments using Petri nets

Yuan

Oswald

2015

Expert Systems with Applications

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“…In some recent studies, RF was not only used as an empirical model in hydrological modeling but also as a flexible method to estimate the uncertainty of other models (De Oña et al, 2012;Loosvelt et al, 2012;Solomatine & Shrestha, 2009). Other types of ML models such as support vector machine and k-nearest neighbors have also been used in many water studies (Bashi-Azghadi et al, 2010;Lin et al, 2006;St-Hilaire et al, 2012;Yoon et al, 2011). However, the rapid expansion of ML methods creates challenges to identify the best approach for specific application contexts, bearing in mind variability in the dominant catchment processes, and also the (often patchy) nature of data available to support the prediction.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Landscape Characteristics on Groundwater Dissolved Organic Nitrogen: Insights From Machine Learning Methods and Sensitivity Analysis

Wang

Hipsey

Ahmed

et al. 2018

Water Resources Research

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The effect of groundwater nutrient inputs on river and estuary water quality and the potential impacts of urbanization on groundwater are central concerns in many coastal areas. It has been previously identified that dissolved organic nitrogen (DON) can be the dominant form of total dissolved nitrogen (TDN) in some aquifers. However, there is a paucity of evidence about the sources and flow paths of DON, relative to inorganic nitrogen in groundwater. DON and dissolved organic carbon/DON were first compared against different landscape variables in this study, and no significant relationships were found. However, the relationships became statistically significant when shallow samples (sampling depth < 10 m) were separated from deep samples. A random forest model and sensitivity analysis were then applied to further our understanding of the ecohydrological drivers and seasonal patterns that shape DON variability. The random forest algorithm was built to classify 171 groundwater wellbores into three classes (low: <0.5 mg/L; medium: 0.5–2.5 mg/L; and high: >2.5 mg/L) which achieved 72% classification accuracy using landscape characteristics, hydrological conditions, and temporal information. The results indicated that the effects of landscapes on sandy shallow groundwater DON were controlled both by certain landscape characteristics and depth to groundwater. A conceptual model of groundwater DON is therefore proposed where the balance of exposure and processing time scales from the surface to groundwater is the critical control on the preservation of landscape signatures; we expect that this conceptual model would be applicable for other sandy, shallow groundwater areas.

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Characterizing an unknown pollution source in groundwater resources systems using PSVM and PNN

Cited by 65 publications

References 25 publications

A probabilistic water quality index for river water quality assessment: a case study

A probabilistic water quality index for river water quality assessment: a case study

Autonomous tracking of chemical plumes developed in both diffusive and turbulent airflow environments using Petri nets

The Impact of Landscape Characteristics on Groundwater Dissolved Organic Nitrogen: Insights From Machine Learning Methods and Sensitivity Analysis

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