Multicriteria-analysis of deep groundwater quality using WQI and fuzzy logic tool in GIS: A case study of Kebilli region, SW Tunisia

Brahim, Fatma Ben; Boughariou, Emna; Bouri, Salem

doi:10.1016/j.jafrearsci.2021.104224

Cited by 26 publications

(11 citation statements)

References 77 publications

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“…Expert opinion determines how each index is weighted, in terms of the root of the function in Equation (9). As was given more weight than other elements due to its significance.…”

Section: Level 1: Data Fusionmentioning

confidence: 99%

Developing a Data-Fused Water Quality Index Based on Artificial Intelligence Models to Mitigate Conflicts between GQI and GWQI

Nadiri

Barzegar

Sadeghfam

et al. 2022

Water

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The study of groundwater quality is typically conducted using water quality indices such as the Groundwater Quality Index (GQI) or the GroundWater Quality Index (GWQI). The indices are calculated using field data and a scoring system that uses ratios of the constituents to the prescribed standards and weights based on each constituent’s relative importance. The results obtained by this procedure suffer from inherent subjectivity, and consequently may have some conflicts between different water quality indices. An innovative feature drives this research to mitigate the conflicts in the results of GQI and GWQI by using the predictive power of artificial intelligence (AI) models and the integration of multiple water quality indicators into one representative index using the concept of data fusion through the catastrophe theory. This study employed a two-level AI modeling strategy. In Level 1, three indices were calculated: GQI, GWQI, and a data-fusion index based on four pollutants including manganese (Mn), arsenic (As), lead (Pb), and iron (Fe). Further data fusion was applied at Level 2 using supervised learning methods, including Mamdani fuzzy logic (MFL), support vector machine (SVM), artificial neural network (ANN), and random forest (RF), with calculated GQI and GWQI indices at Level 1 as inputs, and data-fused indices target values derived from Level 1 fusion as targets. We applied these methods to the Gulfepe-Zarinabad subbasin in northwest Iran. The results show that all AI models performed reasonably well, and the difference between models was negligible based on the root mean square errors (RMSE), and the coefficient of determination (r2) metrics. RF (r2 = 0.995 and RMSE = 0.006 in the test phase) and MFL (r = 0.921 and RMSE = 0.022 in the test phase) had the best and worst performances, respectively. The results indicate that AI models mitigate the conflicts between GQI and GWQI results. The method presented in this study can also be applied to modeling other aquifers.

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“…Expert opinion determines how each index is weighted, in terms of the root of the function in Equation (9). As was given more weight than other elements due to its significance.…”

Section: Level 1: Data Fusionmentioning

confidence: 99%

Developing a Data-Fused Water Quality Index Based on Artificial Intelligence Models to Mitigate Conflicts between GQI and GWQI

Nadiri

Barzegar

Sadeghfam

et al. 2022

Water

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“…Ben Brahim et al (2021) [81] indicated that the Kebili region, in southwestern Tunisia, is an arid desert region. They proposed to apply water quality index models and Fuzzy Logic models using the geographic information systems environment for the purpose of addressing the spatial division of water and assessing the quality of drinking and irrigation water.…”

Section: Gis With Other Technologymentioning

confidence: 99%

Combination of GIS with Different Technologies for Water Quality: An Overview

Habeeb

Weli

2021

HighTech. Innov. J.

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Water is one of the most important requirements in daily life, which represents the largest part of the Earth. As a result of the economic, industrial and social development in most countries led to increased pollution in water resources. It is, therefore, necessary to monitor water quality continuously to prevent a future catastrophe that adversely affects the quality and quantity of water wealth. Geographic Information System (GIS) is used in various fields to monitor, analyze data collected from different geographical locations. Integration of GIS and other technologies has become an indispensable tool. This gives us direct control over solution expansion, cost reduction, powerful complex case analysis as well as increased accuracy and efficiency of geospatial data. In recent years, many combinations of GIS with different technologies such as remote sensing, wireless sensor networks, internet of things approaches have been proposed due to the rapid progress of technology development in many applications. However, in last several years, there is no survey paper has been published about water quality using the integration of GIS and other technologies. Therefore, this paper investigates the status of continuous search in the field of GIS and its integration with other technologies (Remote Sensing, Internet of Things, Web, etc.) for water quality management and monitoring to maintain the water resources in a proper way. As conclusions, the integration of the GIS with these technologies is a powerful platform for analyzing and processing big data and mapping geographic remotely in less time, less cost, high speed and with more accurate details and in real time compared to traditional geographic information systems. This paper also highlights future research trends about the cooperation of GIS with other technologies for matters that are related to water quality. Doi: 10.28991/HIJ-2021-02-03-10 Full Text: PDF

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“…Nevertheless, in order to reduce the uncertainty and increase the effectiveness of environmental monitoring, a lot of approaches to RS data are proposed as an alternative to processing climate data, some of them using Landsat products [30] and their integration with the Google Earth Engine platform [31], or mixed approaches with GIS-based mapping [32][33][34], and some of them using hydrological datasets for evaluating physicochemical parameters of groundwater in inflow [35]. Other examples include evaluating the content of soil organic carbon stocks in topsoils using bioclimatic data [36] or monitoring agricultural and irrigated lands and their behaviour during drought periods [37].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Monitoring Seasonal Fluctuations in Saline Lakes of Tunisia Using Earth Observation Data Processed by GRASS GIS

Lemenkova

2023

Land

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This study documents the changes in the Land Use/Land Cover (LULC) in the region of saline lakes in north Tunisia, Sahara Desert. Remote sensing data are a valuable data source in monitoring LULC in lacustrine landscapes, because variations in the extent of lakes are visible from space and can be detected on the images. In this study, changes in LULC of the salt pans of Tunisia were evaluated using a series of 12 Landsat 8-9 Operational Land Imager (OLI) and Thermal Infrared (TIRS) images. The images were processed with the Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS) software. The study area included four salt lakes of north Tunisia in the two regions of the Gulf of Hammamet and Gulf of Gabès: (1) Sebkhet de Sidi el Hani (Sousse Governorate), (2) Sebkha de Moknine (Mahdia Governorate), (3) Sebkhet El Rharra and (4) Sebkhet en Noual (Sfax). A quantitative estimate of the areal extent analysed in this study is 182 km × 185 km for each Landsat scene in two study areas: Gulf of Hammamet and Gulf of Gabès. The images were analysed for the period 2017–2023 on months February, April and July for each year. Spatio-temporal changes in LULC and their climate–environmental driving forces were analysed. The results were interpreted and the highest changes were detected by accuracy assessment, computing the class separability matrices, evaluating the means and standard deviation for each band and plotting the reject probability maps. Multi-temporal changes in LULC classes are reported for each image. The results demonstrated that changes in salt lakes were determined for winter/spring/summer months as detected changes in water/land/salt/sand/vegetation areas. The accuracy of the classified images was evaluated using pixel rejection probability values, which were filtered out using the ‘r.mapcalc’ module of GRASS GIS. The confidence levels were computed and visualised with a series of maps along with the error matrix and measured convergence level of classified pixels. This paper contributes to the environmental monitoring of Tunisian landscapes and analysis of climate effects on LULC in landscapes of north Africa.

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Multicriteria-analysis of deep groundwater quality using WQI and fuzzy logic tool in GIS: A case study of Kebilli region, SW Tunisia

Cited by 26 publications

References 77 publications

Developing a Data-Fused Water Quality Index Based on Artificial Intelligence Models to Mitigate Conflicts between GQI and GWQI

Developing a Data-Fused Water Quality Index Based on Artificial Intelligence Models to Mitigate Conflicts between GQI and GWQI

Combination of GIS with Different Technologies for Water Quality: An Overview

Monitoring Seasonal Fluctuations in Saline Lakes of Tunisia Using Earth Observation Data Processed by GRASS GIS

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