Multi Criteria Analysis for the monitoring of aquifer vulnerability: A scientific tool in environmental policy

Valle, Renato Farias do; Varandas, Simone; Fernandes, Luís Filipe Sanches; Pacheco, Fernando António Leal

doi:10.1016/j.envsci.2015.01.010

Cited by 51 publications

(20 citation statements)

References 39 publications

(35 reference statements)

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“…The thematic maps (e.g., Figures 1-3) were prepared in ArcMap software of ESRI [47], a common tool in spatial analysis of hydrologic and environmental data widely used in many studies [42,44,[48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66]. The base information was compiled from various spatial databases, namely the maps published by the Brazilian Institute for Geography and Statistics (https://ww2.ibge.gov.br) on the 1: 100,000 scale, and the digital terrain model obtained from the ASTER GDEN V2 satellite image with a spatial resolution of 30 m.…”

Section: Thematic Mapsmentioning

confidence: 99%

A Regression Model of Stream Water Quality Based on Interactions between Landscape Composition and Riparian Buffer Width in Small Catchments

Pissarra

Valera

Costa

et al. 2019

Water

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Riparian vegetation represents a protective barrier between human activities installed in catchments and capable of generating and exporting large amounts of contaminants, and stream water that is expected to keep quality overtime. This study explored the combined effect of landscape composition and buffer strip width (L) on stream water quality. The landscape composition was assessed by the forest (F) to agriculture (A) ratio (F/A), and the water quality by an index (IWQ) expressed as a function of physico-chemical parameters. The combined effect (F/A × L) was quantified by a multiple regression model with an interaction term. The study was carried out in eight catchments of Uberaba River Basin Environmental Protection Area, located in the state of Minas Gerais, Brazil, and characterized by very different F/A and L values. The results related to improved water quality (larger IWQ values) with increasing values of F/A and L, which were not surprising given the abundant similar reports widespread in the scientific literature. But the effect of F/A × L on IWQ was enlightening. The interaction between F/A and L reduced the range of L values required to sustain IWQ at a fair level by some 40%, which is remarkable. The interaction was related to the spatial distribution of infiltration capacity within the studied catchments. The high F/A catchments should comprise a larger number of infiltration patches, allowing a dominance of subsurface flow widespread within the soil layer, a condition that improves the probability of soil water to cross and interact with a buffer strip before reaching the stream. Conversely, the low F/A catchments are prone to the generation of an overland flow network, because the absence of permanent vegetation substantially reduces the number of infiltration patches. The overland flow network channelizes runoff and conveys the surface water into specific confluence points within the stream, reducing or even hampering an interaction with a buffer strip. Notwithstanding the interaction, the calculated L ranges (45–175 m) are much larger than the maximum width imposed by the Brazilian Forest Code (30 m), a result that deserves reflection.

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Section: Thematic Mapsmentioning

confidence: 99%

A Regression Model of Stream Water Quality Based on Interactions between Landscape Composition and Riparian Buffer Width in Small Catchments

Pissarra

Valera

Costa

et al. 2019

Water

Self Cite

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“…This will allow a given region to be classified into sub-regions in terms of their vulnerability intensity and relevant measures to be designed that will prevent pollution from reaching vulnerable areas (Mohammadi et al 2009;Umar et al 2009;Rezaei et al 2013). In this context, the inclusion of vulnerability maps in groundwater quality protection plans would be beneficial, along with the discussion of groundwater contamination risk with stakeholders that include politicians, entrepreneurs, opinion makers, general public (Valle Junior et al 2015).…”

Section: Introductionmentioning

confidence: 98%

A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran

Barzegar

Moghaddam

Baghban

2015

Stoch Environ Res Risk Assess

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Vulnerability maps are designed to show areas of greatest potential for groundwater contamination on the basis of hydrogeological conditions and human impacts. The objective of this research is (1) to assess the groundwater vulnerability using DRASTIC method and (2) to improve the DRASTIC method for evaluation of groundwater contamination risk using AI methods, such as ANN, SFL, MFL, NF and SCMAI approaches. This optimization method is illustrated using a case study. For this purpose, DRASTIC model is developed using seven parameters. For validating the contamination risk assessment, a total of 243 groundwater samples were collected from different aquifer types of the study area to analyze NO À 3 concentration. To develop AI and CMAI models, 243 data points are divided in two sets; training and validation based on cross validation approach. The calculated vulnerability indices from the DRASTIC method are corrected by the NO À 3 data used in the training step. The input data of the AI models include seven parameters of DRASTIC method. However, the output is the corrected vulnerability index using NO À 3 concentration data from the study area, which is called groundwater contamination risk. In other words, there is some target value (known output) which is estimated by some formula from DRASTIC vulnerability and NO À 3 concentration values. After model training, the AI models are verified by the second NO À 3 concentration dataset. The results revealed that NF and SFL produced acceptable performance while ANN and MFL had poor prediction. A supervised committee machine artificial intelligent (SCMAI), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of vulnerability. The performance of SCMAI was also compared to those of the simple averaging and weighted averaging committee machine intelligent (CMI) methods. As a result, the SCMAI model produced reliable estimates of groundwater contamination risk.

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“…In addition, the intense exploitation of water resources in urban areas can generate water scarcity and even depletion [44][45][46]. At catchment scale, the inadequate managing of water sources and recharge areas can reduce water availability and increase the vulnerability of surface and groundwater to contamination [46][47][48][49][50][51][52][53][54][55][56][57]. It is therefore time for policy makers and water planners to start a robust assessment of groundwater recharge potential.…”

Section: Introductionmentioning

confidence: 99%

Groundwater Recharge Potential for Sustainable Water Use in Urban Areas of the Jequitiba River Basin, Brazil

et al. 2019

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The zoning of groundwater recharge potential would be attractive for water managers, but is lacking in many regions around the planet, including in the Jequitiba River basin, Minas Gerais, Brazil. In this study, a physically based spatially distributed method to evaluate groundwater recharge potential at catchment scale was developed and tested in the aforementioned Jequitiba River basin. The data for the test was compiled from institutional sources and implemented in a Geographic Information System. It comprised meteorological, hydrometric, relief, land use, and soil data. The average results resembled the annual recharge calculated by a hydrograph method, which worked as validation method. The spatial variation of recharge highlighted the predominant contribution of flat areas, porous aquifers, and forested regions to groundwater recharge. They also exposed the negative effect of urbanization. In combination, these factors elected the following sectors of the Jequitiba River basin as regions of high recharge potential: the south-southeast part of the headwaters in Prudente de Morais; Sete Lagoas towards the central part of the basin; and the region between Funilândia and Jequitiba, near the Jequitiba river mouth. Some management practices were suggested to improve groundwater recharge. The map of groundwater recharge potential produced in this study is valuable and is therefore proposed as tool for planners in the sustainable use of groundwater and protection of recharge areas.

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Multi Criteria Analysis for the monitoring of aquifer vulnerability: A scientific tool in environmental policy

Cited by 51 publications

References 39 publications

A Regression Model of Stream Water Quality Based on Interactions between Landscape Composition and Riparian Buffer Width in Small Catchments

A Regression Model of Stream Water Quality Based on Interactions between Landscape Composition and Riparian Buffer Width in Small Catchments

A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran

Groundwater Recharge Potential for Sustainable Water Use in Urban Areas of the Jequitiba River Basin, Brazil

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