Mapping of potential sites for rainwater harvesting structures using GIS and MCDM approaches: case study of the Toudgha watershed, Morocco

Ouali, Lamya; Hssaisoune, Mohammed; Kabiri, Lahcen; Slimani, Moulay M’hamed; Mouquaddam, Kamal El; Namous, Mustapha; Arioua, Abdelkrim; Moussa, Asma Ben; Benqlilou, H.; Bouchaou, Lhoussaine

doi:10.1007/s41207-022-00294-7

Cited by 11 publications

(10 citation statements)

References 33 publications

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“…The Q was integrated for the first time on the GWP prediction analysis. Indeed, it presents a high importance that confirms the interdependence of groundwater and surface water [3]. In contrast to previous research, the FD was classified as the less important factor.…”

Section: Importance Of Gwp-influencing Factorscontrasting

confidence: 58%

“…The study was conducted within the Toudgha watershed, which is in the southeast of the Moroccan kingdom and covers an area of 2296 km 2 , ranging between 31 • 51 35 N-31 • 9 14 N and 5 • 10 45 W-5 • 57 01 W (Figure 1). The area is characterized by an arid climate [3],which is strongly influenced by the continental and desert context. The interannual average rainfall is 154 mm, which shows high spatial and temporal variability marked by a spatial difference in the average precipitation of 110 mm between the northwest (i.e., the mountainous region) and the southeast (i.e., the lowland region) and a temporal difference in the average precipitation of 295 mm between the wettest and driest years.…”

Section: The Study Areamentioning

confidence: 99%

“…Extensive interpretation using a geologic map, topographic map, high-resolution images from Google Earth Pro, and the colored composition of the Landsat OLI-8 image bands was carried out in order to sort and eliminate lineaments other than fractures (roads, rivers, etc.). The climatic factor map was obtained from the Global Precipitation Measurement (GPM) data validated by Ouali et al [3] within the studied area. The lithology map was digitized from the 1:200,000 geological map of Morocco.…”

Section: Mapping the Gwp-influencing Factormentioning

confidence: 99%

“…Oases in arid lands are home to thousand-year-old civilizations that have survived due to the availability of water resources and their management. Nowadays, water stress is often frequent and widespread; therefore, with the quasi-absence of surface water, groundwater represents the principal water supply source in those areas [1][2][3][4][5]. Thus, its availability is of paramount interest to territorial sustainable development [6].…”

Section: Introductionmentioning

confidence: 99%

“…In this context, the combination of remote sensing (RS) data, geographic information systems (GIS), and multi-criteria decision-making (MCDM) analyses based on statistical methods represent a popular, rapid, and cost-effective assessment method of water resource management in general and groundwater identification [3,15]. In previous years, this combination has allowed many researchers worldwide to provide large-scale spatio-temporal analyses of several datasets within various geo-environmental issues in general terms and for groundwater monitoring, particularly in [8] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco

et al. 2023

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Water availability is a key factor in territorial sustainable development. Moreover, groundwater constitutes the survival element of human life and ecosystems in arid oasis areas. Therefore, groundwater potential (GWP) identification represents a crucial step for its management and sustainable development. This study aimed to map the GWP using ten algorithms, i.e., shallow models comprising: multilayer perceptron, k-nearest neighbor, decision tree, and support vector machine algorithms; hybrid models comprising: voting, random forest, adaptive boosting, gradient boosting (GraB), and extreme gradient boosting; and the deep learning neural network. The GWP inventory map was prepared using 884 binary data, with “1” indicating a high GWP and “0” indicating an extremely low GWP. Twenty-three GWP-influencing factors have been classified into numerical data using the frequency ration method. Afterwards, they were selected based on their importance and multi-collinearity tests. The predicted GWP maps show that, on average, only 11% of the total area was predicted as a very high GWP zone and 17% and 51% were estimated as low and very low GWP zones, respectively. The performance analyses demonstrate that the applied algorithms have satisfied the validation standards for both training and validation tests with an average area under curve of 0.89 for the receiver operating characteristic. Furthermore, the models’ prioritization has selected the GraB model as the outperforming algorithm for GWP mapping. This study provides decision support tools for sustainable development in an oasis area.

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Section: Importance Of Gwp-influencing Factorscontrasting

confidence: 58%

Section: The Study Areamentioning

confidence: 99%

Section: Mapping the Gwp-influencing Factormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco

et al. 2023

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Geospatial identification of possible rainwater harvesting locations within a high-altitude Gilgit River basin, Pakistan

Asif,

Yaseen,

Shahid

et al. 2024

Theor Appl Climatol

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The management of water resources is no longer an option but a necessity especially in countries with a high rate of population growth like Pakistan. Pakistan has a high-water scarcity, an extremely varying topography, and is also one of the countries encountering the problem of climate change. Therefore, prudent utilization of available water resources is a matter in the plains of Pakistan as in its high-altitude regions. Instead of wasting water which is a valuable resource, rainwater harvesting is a way of saving rainwater as an alternative source of fresh water for better use. The aim of this study was to search for potential rainwater-harvesting areas in a high-altitude Gilgit River basin. This research employed the SCS-CN technique in estimating surface runoff in the GIS environment. The geospatial study used four input parameters: slope of the land, land covers, surface runoff and drainage density. The research concluded that some lands, especially those that are deserted in dry area and agricultural lands, can serve as the sites of rainwater harvesting, with only 4% of area that was highly suitable for the purpose. Nevertheless, 37% of the investigated area was shown as totally unviable for rainwater collection installations because of steep slopes and substantial volume of snow. Simultaneously, the rainwater storage design solutions suggested were check dam, percolation tank, and agricultural ponds with the area of 3.5%, 1%, and 0.75% of the given land area, respectively, for rainwater harvesting. The locations of potential catchment and planned rainwater harvesting structures would assist the local authorities and the policy makers to provide plans and design on the construction of water storages and also the optimization of water use.

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A Pythagorean fuzzy Einstein weighted averaging operator-based MCDM model for the selection of sustainable urban drainage system

Choudhury,

Saha,

Bhowmik

et al. 2024

Environ Dev Sustain

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Mapping of potential sites for rainwater harvesting structures using GIS and MCDM approaches: case study of the Toudgha watershed, Morocco

Cited by 11 publications

References 33 publications

Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco

Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco

Geospatial identification of possible rainwater harvesting locations within a high-altitude Gilgit River basin, Pakistan

A Pythagorean fuzzy Einstein weighted averaging operator-based MCDM model for the selection of sustainable urban drainage system

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