Prediction of annual drinking water quality reduction based on Groundwater Resource Index using the artificial neural network and fuzzy clustering

Azimi, Saeed; Moghaddam, Mehdi Azhdary; Monfared, Seyed Arman Hashemi

doi:10.1016/j.jconhyd.2018.10.010

Cited by 58 publications

(21 citation statements)

References 23 publications

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“…The outcome of the study demonstrated the efficiency of incorporating AI model with GIS. The study by Azimi et al [ 41 ] presented ANN and modified fuzzy clustering models for the evaluation of decreases in the quality of drinking water. The performance of the models was evaluated on real instances of the southeast aquifers in the central region of Iran.…”

Section: Introductionmentioning

confidence: 99%

Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios

Shiri

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2021

PLoS ONE

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Groundwater is one of the most important freshwater resources, especially in arid and semi-arid regions where the annual amounts of precipitation are small with frequent drought durations. Information on qualitative parameters of these valuable resources is very crucial as it might affect its applicability from agricultural, drinking, and industrial aspects. Although geo-statistics methods can provide insight about spatial distribution of quality factors, applications of advanced artificial intelligence (AI) models can contribute to produce more accurate results as robust alternative for such a complex geo-science problem. The present research investigates the capacity of several types of AI models for modeling four key water quality variables namely electrical conductivity (EC), sodium adsorption ratio (SAR), total dissolved solid (TDS) and Sulfate (SO4) using dataset obtained from 90 wells in Tabriz Plain, Iran; assessed by k-fold testing. Two different modeling scenarios were established to make simulations using other quality parameters and the geographical information. The obtained results confirmed the capabilities of the AI models for modeling the well groundwater quality variables. Among all the applied AI models, the developed hybrid support vector machine-firefly algorithm (SVM-FFA) model achieved the best predictability performance for both investigated scenarios. The introduced computer aid methodology provided a reliable technology for groundwater monitoring and assessment.

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Section: Introductionmentioning

confidence: 99%

Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios

Shiri

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2021

PLoS ONE

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“…The credit risk assessment of general commercial banks is based on the financial indicators of financing enterprises, and the credit risk assessment of small-and medium-sized enterprises in the financing process is comprehensively examined [33]. The same is true for chain finance, but the difference is that supply chain finance can be used as an indicator of credit risk evaluation in addition to the financing enterprise itself, and the conditions of the core enterprise related to the financing enterprise and the entire supply chain can be used as a reference indicator for credit risk evaluation.…”

Section: Supply Chain Financial Risk Assessmentmentioning

confidence: 99%

Research on Supply Chain Financial Risk Assessment Based on Blockchain and Fuzzy Neural Networks

Wang

2021

Wireless Communications and Mobile Computing

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With the development of supply chain finance, the credit risk of small- and medium-sized financing enterprises from the perspective of supply chain finance has arisen. Risk management is one of the key tasks of the credit business of banks and other financial institutions, which runs through all aspects of the credit business before, during, and after the loan. This article combines blockchain and fuzzy neural network algorithms to study the credit risk of SME financing from the perspective of supply chain finance. This article builds a supply chain financial system through blockchain technology and integrates supply chain financial information into blocks. The fuzzy neural network algorithm is used for financial data processing and risk assessment, effectively solving and improving the risk processing level of the supply chain. Through further simulation, the application effect of blockchain and machine learning algorithms in the supply chain financial system was verified.

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“…The PNN is another type of neural network model which estimates the potentiality or susceptibility with the help of classified sample data (Azimi et al 2018). This method was developed based on probability density algorithms.…”

Section: Predictive Neural Network (Pnn)mentioning

confidence: 99%

“…These networks were developed through the implementation of two layers. In this implemented network, the second layer, dissimilar to other networks, targets the outcomes in the vector form with values ranging between 1 and 0 (Azimi et al 2018;Dou et al 2018). In this work, first, the continuous values were converted through a fuzzy membership function and newly developed values were applied as input layers in the PNN model.…”

Section: Predictive Neural Network (Pnn)mentioning

confidence: 99%

“…The raster values were then converted into three different seats to demarcate the situation of the two "occurrence" and "nonoccurrence" points. The newly developed data layer was implemented to demarcate the membership severity of the two individual classes (Azimi et al 2018;Dou et al 2018). Finally, the criterion of the potential index, the potential of groundwater, was employed into four classes.…”

Section: Predictive Neural Network (Pnn)mentioning

confidence: 99%

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Spatial Prediction of Groundwater Potentiality Mapping Using Machine Learning Algorithms

Saha¹,

Gayen²,

Mukherjee³

et al. 2021

Preprint

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Machine learning techniques offer powerful tools for the assessment and management of groundwater resources. Here, we evaluated the groundwater potential maps (GWPMs) in Md. Bazar Block of Birbhum District, India using four GIS-based machine-learning algorithms (MLA) such as predictive neural network (PNN), decision tree (DT), Naïve Bayes classifier (NBC), and random forest (RF). We used a database of 85 dug wells and one piezometer location identified using extensive field study, and employed 12 influencing factors (elevation, slope, drainage density (DD), topographical wetness index, geomorphology, lineament density, rainfall, geology, pond density, land use/land cover (LULC), geology, and soil texture) for evaluation through GIS. The 85 dug wells and 1 piezometer locations were sub-divided into two classes: 70:30 for training and model validation. The DT, RF, PNN, and NBC MLAs were implemented to analyse the relationship between the dug well locations and groundwater influencing factors to generate GWPMs. The results predict excellent groundwater potential areas (GPA) DT RF of 17.38%, 14.69%, 20.43%, and 13.97% of the study area, respectively. The prediction accuracy of each GWPM was determined using a receiver operating characteristic (ROC) curve. Using the 30% data sets (validation data), accuracies of 80.1%, 78.30%, 75.20%, and 69.2% were obtained for the PNN, RF, DT, and NBC models, respectively. The ROC values show that the four implemented models provide satisfactory and suitable results for GWP mapping in this region. In addition, the well-known mean decrease Gini (MDG) from the RF MLA was implemented to determine the relative importance of the variables for groundwater potentiality assessment. The MDG revealed that drainage density, lineament density, geomorphology, pond density, elevation, and stream junction frequency were the most useful determinants of GWPM. Our approach to delineate the GWPM can aid in the effective planning and management of groundwater resources in this region.

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Prediction of annual drinking water quality reduction based on Groundwater Resource Index using the artificial neural network and fuzzy clustering

Cited by 58 publications

References 23 publications

Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios

Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios

Research on Supply Chain Financial Risk Assessment Based on Blockchain and Fuzzy Neural Networks

Spatial Prediction of Groundwater Potentiality Mapping Using Machine Learning Algorithms

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