High Demand in a Land of Water Scarcity: Iran

Zehtabian, Gholamreza; Khosravi, Hassan; Ghodsi, Maryam

doi:10.1007/978-90-481-2776-4_5

Cited by 20 publications

(8 citation statements)

References 4 publications

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“…[1]. In another way, groundwater availability and accessibility control sustainable development at a global, regional and local scale [2]. Large numbers of countries of the earth are facing the problem of water scarcity at the societal level [3].…”

Section: Introductionmentioning

confidence: 99%

“…RS technique provides the spatial and non-spatial information-even over the inaccessible areas in a short duration [13]. Therefore, RS technique also a powerful, efficient, accurate tool for collecting, restoring, manipulating, analyzing the spatial data of the surface and sub-surface water research, e.g., groundwater recharge, potentiality, evaluation of water quality [2,14]. Specifically, satellite imagery can provide hydrological characteristics, i.e., drainage network, flow accumulation, drainage density, recharge, and other geomorphologic characteristics [15].…”

Section: Introductionmentioning

confidence: 99%

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Application of Probabilistic and Machine Learning Models for Groundwater Potentiality Mapping in Damghan Sedimentary Plain, Iran

et al. 2019

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Groundwater is one of the most important natural resources, as it regulates the earth’s hydrological system. The Damghan sedimentary plain area, located in the region of a semi-arid climate of Iran, has very critical conditions of groundwater due to massive pressure on it and is in need of robust models for identifying the groundwater potential zones (GWPZ). The main goal of the current research is to prepare a groundwater potentiality map (GWPM) considering the probabilistic, machine learning, data mining, and multi-criteria decision analysis (MCDA) approaches. For this purpose, 80 wells collected from the Iranian groundwater resource department and field investigation with global positioning system (GPS), have been selected randomly and considered as the groundwater inventory datasets. Out of 80 wells, 56 (70%) wells have been brought into play for modeling and 24 (30%) for validation purposes. Elevation, slope, aspect, convergence index (CI), rainfall, drainage density (Dd), distance to river, distance to fault, distance to road, lithology, soil type, land use/land cover (LU/LC), normalized difference vegetation index (NDVI), topographic wetness index (TWI), topographic position index (TPI), and stream power index (SPI) have been used for modeling purpose. The area under the receiver operating characteristic (AUROC), sensitivity (SE), specificity (SP), accuracy (AC), mean absolute error (MAE), and root mean square error (RMSE) are used for checking the goodness-of-fit and prediction accuracy of approaches to compare their performance. In addition, the influence of groundwater determining factors (GWDFs) on groundwater occurrence was evaluated by performing a sensitivity analysis model. The GWPMs, produced by technique for order preference by similarity to ideal solution (TOPSIS), random forest (RF), binary logistic regression (BLR), weight of evidence (WoE) and support vector machine (SVM) have been classified into four categories, i.e., low, medium, high and very high groundwater potentiality with the help of the natural break classification methods in the GIS environment. The very high groundwater potentiality class is covered 15.09% for TOPSIS, 15.46% for WoE, 25.26% for RF, 15.47% for BLR, and 18.74% for SVM of the entire plain area. Based on sensitivity analysis, distance from river, and drainage density represent significantly effects on the groundwater occurrence. validation results show that the BLR model with best prediction accuracy and goodness-of-fit outperforms the other five models. Although, all models have very good performance in modeling of groundwater potential. Results of seed cell area index model that used for checking accuracy classification of models show that all models have suitable performance. Therefore, these are promising models that can be applied for the GWPZs identification, which will help for some needful action of these areas.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Probabilistic and Machine Learning Models for Groundwater Potentiality Mapping in Damghan Sedimentary Plain, Iran

et al. 2019

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“…The population of Iran was about 69 million in 2003. There are different estimates of the future population of Iran (e.g., Zehtabian et al, 2010). According to the most recent revision of the United Nations world population prospects (UN, 2011), the projected population of Iran will be about 87 million by 2025.…”

Section: Freshwater Resources and Demands In Iranmentioning

confidence: 99%

“…To achieve food self-sufficiency, the production of crops, such as wheat, maize, oilseed crops, sugar beet and rice, is high on the agenda of Iran's agricultural policymakers (Bansouleh, 2009 (FAO, 2009). It is estimated that 55% of the water comes from groundwater and 45% is exploited from surface water (Zehtabian et al, 2010). The projected withdrawals for irrigated agriculture by 2025 are 124 × 10 9 m 3 y -1 .…”

Section: Freshwater Resources and Demands In Iranmentioning

confidence: 99%

“…Despite the shortage of water in the country, the emphasis has been more on water resources development rather than on water resources management (Zehtabian et al, 2010). As such, considering the situation in Iran, a well-balanced allocation of freshwater resources to different uses, e.g., agriculture, municipalities, industries and natural ecosystems, is required.…”

Section: Freshwater Resources and Demands In Iranmentioning

confidence: 99%

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Integrating water resources modelling and remote sensing in Karkheh river basin, Iran

Muthuwatta¹

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Spatial-Temporal Changes of Water Resources: Associated Impact as a Natural Hazard

Sabzevari

Eslamian²

2023

Disaster Risk Reduction for Resilience

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High Demand in a Land of Water Scarcity: Iran

Cited by 20 publications

References 4 publications

Application of Probabilistic and Machine Learning Models for Groundwater Potentiality Mapping in Damghan Sedimentary Plain, Iran

Application of Probabilistic and Machine Learning Models for Groundwater Potentiality Mapping in Damghan Sedimentary Plain, Iran

Integrating water resources modelling and remote sensing in Karkheh river basin, Iran

Spatial-Temporal Changes of Water Resources: Associated Impact as a Natural Hazard

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