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1.
The purpose of current study is to produce groundwater qanat potential map using frequency ratio (FR) and Shannon's entropy (SE) models in the Moghan watershed, Khorasan Razavi Province, Iran. The qanat is basically a horizontal, interconnected series of underground tunnels that accumulate and deliver groundwater from a mountainous source district, along a water- bearing formation (aquifer), and to a settlement. A qanat locations map was prepared for study area in 2013 based on a topographical map at a 1:50,000-scale and extensive field surveys. 53 qanat locations were detected in the field surveys. 70 % (38 locations) of the qanat locations were used for groundwater potential mapping and 30 % (15 locations) were used for validation. Fourteen effective factors were considered in this investigation such as slope degree, slope aspect, altitude, topographic wetness index (TWI), stream power index (SPI), slope length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, lithology, land use, drainage density, and fault density. Using the above conditioning factors, groundwater qanat potential map was generated implementing FR and SE models, and the results were plotted in ArcGIS. The predictive capability of frequency ratio and Shannon's entropy models were determined by the area under the relative operating characteristic curve. The area under the curve (AUC) for frequency ratio model was calculated as 0.8848. Also AUC for Shannon's entropy model was 0.9121, which depicts the excellence of this model in qanat occurrence potential estimation in the study area. So the Shannon's entropy model has higher AUC than the frequency ratio model. The produced groundwater qanat potential maps can assist planners and engineers in groundwater development plans and land use planning.  相似文献   

2.
Improving the accuracy of flood prediction and mapping is crucial for reducing damage resulting from flood events. In this study, we proposed and validated three ensemble models based on the Best First Decision Tree (BFT) and the Bagging (Bagging-BFT), Decorate (Bagging-BFT), and Random Subspace (RSS-BFT) ensemble learning techniques for an improved prediction of flood susceptibility in a spatially-explicit manner. A total number of 126 historical flood events from the Nghe An Province (Vietnam) were connected to a set of 10 flood influencing factors (slope, elevation, aspect, curvature, river density, distance from rivers, flow direction, geology, soil, and land use) for generating the training and validation datasets. The models were validated via several performance metrics that demonstrated the capability of all three ensemble models in elucidating the underlying pattern of flood occurrences within the research area and predicting the probability of future flood events. Based on the Area Under the receiver operating characteristic Curve (AUC), the ensemble Decorate-BFT model that achieved an AUC value of 0.989 was identified as the superior model over the RSS-BFT (AUC = 0.982) and Bagging-BFT (AUC = 0.967) models. A comparison between the performance of the models and the models previously reported in the literature confirmed that our ensemble models provided a reliable estimate of flood susceptibilities and their resulting susceptibility maps are trustful for flood early warning systems as well as development of mitigation plans.  相似文献   

3.
Groundwater potential analysis prepares better comprehension of hydrological settings of different regions. This study shows the potency of two GIS-based data driven bivariate techniques namely statistical index (SI) and Dempster–Shafer theory (DST) to analyze groundwater potential in Broujerd region of Iran. The research was done using 11 groundwater conditioning factors and 496 spring positions. Based on the ground water potential maps (GPMs) of SI and DST methods, 24.22% and 23.74% of the study area is covered by poor zone of groundwater potential, and 43.93% and 36.3% of Broujerd region is covered by good and very good potential zones, respectively. The validation of outcomes displayed that area under the curve (AUC) of SI and DST techniques are 81.23% and 79.41%, respectively, which shows SI method has slightly a better performance than the DST technique. Therefore, SI and DST methods are advantageous to analyze groundwater capacity and scrutinize the complicated relation between groundwater occurrence and groundwater conditioning factors, which permits investigation of both systemic and stochastic uncertainty. Finally, it can be realized that these techniques are very beneficial for groundwater potential analyzing and can be practical for water-resource management experts.  相似文献   

4.
Delineation of the groundwater potential zones is one of the most essential process for the sustainable management of the groundwater sources. However, groundwater studies are quite hard and complex for many regions besides consuming time and cost. This study focused on the groundwater potential mapping in Bey?ehir Lake Basin. Mainly, fuzzy-analytic hierarchy process (fuzzy-AHP) integrated with GIS was used to determine potential zones for groundwater. Seven parameters, namely lithology, lineament, drainage density, land use, slope, soil type, and rainfall were evaluated and Groundwater Potential Index (GWPI) was calculated using weight and rating coefficients of each parameter. According to obtained results, GWPI varies from 0.07665 to 0.28243 in the basin. The low, moderate, and high groundwater potential classes were determined with quantile classification method. The groundwater potential map demonstrates that the high groundwater potential area is located around the lake shore, in the alluvium and limestone fields because high permeability rates depend on soil type, low slope, karstic structure, and agricultural activities in these regions. In addition, the distribution of the springs confirms with groundwater potential area determined with this study.  相似文献   

5.
《地学前缘(英文版)》2020,11(5):1805-1819
In Punjab(Pakistan),the increasing population and expansion of land use for agriculture have severely exploited the regional groundwater resources.Intensive pumping has resulted in a rapid decline in the level of the water table as well as its quality.Better management practices and artificial recharge are needed for the development of sustainable groundwater resources.This study proposes a methodology to delineate favorable groundwater potential recharge zones(FPRI) by integrating maps of groundwater potential recharge index(PRI) with the DRASTIC-based groundwater vulnerability index(VI).In order to evaluate both indexes,different thematic layers corresponding to each index were overlaid in ArcGIS.In the overlay analysis,the weights(for various thematic layers) and rating values(for sub-classes) were allocated based on a review of published literature.Both were then normalized and modified using the analytical hierarchical process(AHP) and a frequency ratio model respectively.After evaluating PRI and FPRI,these maps were validated using the area under the curve(AUC) method.The PRI map indicates that 53% of the area assessed exists in very low to low recharge zones,22% in moderate,and 25% in high to excellent potential recharge zones.The VI map indicates that 38% of the area assessed exists in very low to low vulnerability,33% in moderate,and 29% in high to very high vulnerability zones.The FPRI map shows that the central region of Punjab is moderately-to-highly favorable for recharge due to its low vulnerability and high recharge potential.During the validation process,it was found that the AUC estimated with modified weights and rating values was 79% and 67%,for PRI and VI indexes,respectively.The AUC was less when evaluated using original weights and rating values taken from published literature.Maps of favorable groundwater potential recharge zones are helpful for planning and implementation of wells and hydraulic structures in this region.  相似文献   

6.
Landslide susceptibility mapping (LSM) is important for catastrophe management in the mountainous regions. They focus on generating susceptibility maps beginning from landslide inventories and considering the main predisposing parameters. The aim of this study was to assess the susceptibility of the occurrence of debris flows in the Zêzere River basin and its surrounding area using logistic regression (LR) and frequency ratio (FR) models. To achieve this, a landslide inventory map was created using historical information, satellite imagery, and extensive field works. One hundred landslides were mapped, of which 75% were randomly selected as training data, while the remaining 25% were used for validating the models. The landslide influence factors considered for this study were lithology, elevation, slope gradient, slope aspect, plan curvature, profile curvature, normalized difference vegetation index (NDVI), distance to roads, topographic wetness index (TWI), and stream power index (SPI). The relationships between landslide occurrence and these factors were established, and the results were then evaluated and validated. Validation results show that both methods give acceptable results [the area under curve (AUC) of success rates is 83.71 and 76.38 for LR and FR, respectively]. Furthermore, the AUC results for prediction accuracy revealed that LR model has the highest predictive performance (AUC of predicted rate?=?80.26). Hence, it is concluded that the two models showed reasonably good accuracy in predicting the landslide susceptibility in the study area. These two models have the potential to aid planners in development and land-use planning and to offer tools for hazard mitigation measures.  相似文献   

7.
An approach is presented for the evaluation of groundwater potential using remote sensing, geographic information system, geoelectrical, and multi-criteria decision analysis techniques. The approach divides the available hydrologic and hydrogeologic data into two groups, exogenous (hydrologic) and endogenous (subsurface). A case study in Salboni Block, West Bengal (India), uses six thematic layers of exogenous parameters and four thematic layers of endogenous parameters. These thematic layers and their features were assigned suitable weights which were normalized by analytic hierarchy process and eigenvector techniques. The layers were then integrated using ArcGIS software to generate two groundwater potential maps. The hydrologic parameters-based groundwater potential zone map indicated that the ‘good’ groundwater potential zone covers 27.14% of the area, the ‘moderate’ zone 45.33%, and the ‘poor’ zone 27.53%. A comparison of this map with the groundwater potential map based on subsurface parameters revealed that the hydrologic parameters-based map accurately delineates groundwater potential zones in about 59% of the area, and hence it is dependable to a certain extent. More than 80% of the study area has moderate-to-poor groundwater potential, which necessitates efficient groundwater management for long-term water security. Overall, the integrated technique is useful for the assessment of groundwater resources at a basin or sub-basin scale.  相似文献   

8.
Forest fire is known as an important natural hazard in many countries which causes financial damages and human losses; thus, it is necessary to investigate different aspects of this phenomenon. In this study, performance of four models of linear and quadratic discriminant analysis (LDA and QDA), frequency ratio (FR), and weights-of-evidence (WofE) was investigated to model forest fire susceptibility in the Yihuang area, China. For this purpose, firstly, a forest fire locations map was prepared implementing MODIS satellite images and field surveys. Then, it was classified into two groups including training (70%) and validation (30%) by a random algorithm. In addition, 13 forest fire effective factors were prepared and used such as slope degree, slope aspect, altitude, Topographic Wetness Index (TWI), plan curvature, land use, Normalized Difference Vegetation Index (NDVI), annual rainfall, distance from roads and rivers, wind effect, annual temperature, and soil texture. Using the training dataset and effective factors, LDA, QDA, FR, and WofE models were applied and forest fire susceptibility maps were prepared. Finally, area under the curve (AUC) of receiver operating characteristics (ROC) was implemented for investigating the performance of the models. The results depicted that WofE had the best performance (AUC = 82.2%), followed by FR (AUC = 80.9%), QDA (AUC = 78.3%), and LDA (AUC = 78%), respectively. The results of this study showed the high contribution of altitude, slope degree, and temperature. On the other hand, it was seen that slope aspect and soil had the lowest importance in forest fire susceptibility mapping. From the AUC results, it can be concluded that FR, WofE, LDA, and QDA had acceptable performance and could be used for forest fire susceptibility mapping at the regional scale.  相似文献   

9.

The main purpose of this study was to compare and evaluate the performance of two multicriteria models for landslide susceptibility assessment in Constantine, north-east of Algeria. The landslide susceptibility maps were produced using the analytic hierarchy process (AHP) and Fuzzy AHP (FAHP) via twelve landslides conditioning factors, including the slope gradient, lithology, land cover, distance from drainage network, distance from the roads, distance from faults, topographic wetness index, stream power index, slope curvature, Normalized Difference Vegetation Index, slope aspect and elevation. In this study, the mentioned models were used to derive the weighting value of the conditioning factors. For the validation process of these models, the receiver operating characteristic analysis, and the area under the curve (AUC) were applied by comparing the obtained results to The landslide inventory map which prepared using the archives of scientific publications, reports of local authorities, and field survey as well as analyzing satellite imagery. According to the AUC values, the FAHP model had the highest value (0.908) followed by the AHP model (0.777). As a result, the FAHP model is more consistent and accurate than the AHP in this case study. The outcome of this paper may be useful for landslide susceptibility assessment and land use management.

  相似文献   

10.
This paper demonstrates a partial least-squares regression (PLS) method for geochemical modelling, and then uses the models and geological favourable features to obtain mineral potential maps. The PLS is one of multivariate analysis technologies, which can identify variations in associations and correlations among geochemical elements and mineralisation. The method is here used to calculate principal components as well as to identify correlations between Pb–Zn (mineralization) and 25 stream sediment elements for constructing geochemical models in the Huayuan-Fenghuang district of northwestern Hunan Province, China. The models showing the distribution of geochemical anomaly are useful in interpreting the distribution of faults and the Cambrian Qingxudong Formation (ore-bearing formation), and to better define the architecture on mineralisation in the study area. In addition, the models and other favourable features (proxies) are easily integrated into single possibility map by Boost Weights-of-Evidence (Boost WofE) approach for targets.  相似文献   

11.
Three statistical models—frequency ratio (FR), weights-of-evidence (WofE) and logistic regression (LR)—produced groundwater-spring potential maps for the Birjand Township, southern Khorasan Province, Iran. In total, 304 springs were identified in a field survey and mapped in a geographic information system (GIS), out of which 212 spring locations were randomly selected to be modeled and the remaining 92 were used for the model evaluation. The effective factors—slope angle, slope aspect, elevation, topographic wetness index (TWI), stream power index (SPI), slope length (LS), plan curvature, lithology, land use, and distance to river, road, fault—were derived from the spatial database. Using these effective factors, groundwater spring potential was calculated using the three models, and the results were plotted in ArcGIS. The receiver operating characteristic (ROC) curves were drawn for spring potential maps and the area under the curve (AUC) was computed. The final results indicated that the FR model (AUC?=?79.38 %) performed better than the WofE (AUC?=?75.69 %) and LR (AUC?=?63.71 %) models. Sensitivity and factor analyses concluded that the bivariate statistical index model (i.e. FR) can be used as a simple tool in the assessment of groundwater spring potential when a sufficient number of data are obtained.  相似文献   

12.
Predictive modeling of hydrological time series is essential for groundwater resource development and management. Here, we examined the comparative merits and demerits of three modern soft computing techniques, namely, artificial neural networks (ANN) optimized by scaled conjugate gradient (SCG) (ANN.SCG), Bayesian neural networks (BNN) optimized by SCG (BNN.SCG) with evidence approximation and adaptive neuro-fuzzy inference system (ANFIS) in the predictive modeling of groundwater level fluctuations. As a first step of our analysis, a sensitivity analysis was carried out using automatic relevance determination scheme to examine the relative influence of each of the hydro-meteorological attributes on groundwater level fluctuations. Secondly, the result of stability analysis was studied by perturbing the underlying data sets with different levels of correlated red noise. Finally, guided by the ensuing theoretical experiments, the above techniques were applied to model the groundwater level fluctuation time series of six wells from a hard rock area of Dindigul in Southern India. We used four standard quantitative statistical measures to compare the robustness of the different models. These measures are (1) root mean square error, (2) reduction of error, (3) index of agreement (IA), and (4) Pearson’s correlation coefficient (R). Based on the above analyses, it is found that the ANFIS model performed better in modeling noise-free data than the BNN.SCG and ANN.SCG models. However, modeling of hydrological time series correlated with significant amount of red noise, the BNN.SCG models performed better than both the ANFIS and ANN.SCG models. Hence, appropriate care should be taken for selecting suitable methodology for modeling the complex and noisy hydrological time series. These results may be used to constrain the model of groundwater level fluctuations, which would in turn, facilitate the development and implementation of more effective sustainable groundwater management and planning strategies in semi-arid hard rock area of Dindigul, Southern India and alike.  相似文献   

13.
Geoelectrical resistivity investigation using Vertical Electrical Sounding (VES) technique was conducted at Port Blair, South Andaman Island, to locate the fractures in different formations and to decipher its groundwater potential. A total of 40 VES were carried out covering the entire study area using Schlumberger electrode configuration out of which 34 VES fall in Andaman Flysch formations and the remaining VES in Ophiolite formations. The interpreted resistivity results were integrated with nine borehole lithologs for the subsurface analysis. The combination of VES with borehole litholog data has provided a close correspondence on subsurface hydrogeological conditions. The interpreted VES data of various formations showed drastic variations in the resistivity ranging from higher in Ophiolite, moderate in Andaman Flysch and very low in valleys of Andaman Flysch formations. The study further revealed that the weathered and fractured volcanics of Ophiolite groups of rocks and sandstone that occur in the Andaman Flysch formations constitute the productive water bearing zones categorized as good groundwater potential zone. Based on the geoelectrical parameters, viz., thickness of layers and the layer resistivity values, a groundwater potential map was prepared, in which good, moderate, and poor groundwater zones were demarcated. Further, numerical, spatial and litho-geoelectric models of resistivity were analyzed in terms of groundwater potential and these models have thus enabled to prepare a comprehensive groundwater development and management plans proving its efficacy in this art of exploratory investigations.  相似文献   

14.
Groundwater is the most prioritized water source in India and plays an indispensable role in India's economy. The groundwater potential mapping is key to the sustainable groundwater development and management. A hybrid methodology is applied to delineate potential groundwater zones based on remote sensing, geographical information systems(GIS) and analytic hierarchy process(AHP) as on multicriteria decision making. For the purpose of demonstrating field application, Chittar watershed, Tamilnadu, India is studied as an example. The important morphological characteristics considered in the study are lithology, geomorphology, lineament density, drainage density, slope, and Soil Conservation Service–Curve Number(SCS-CN). These six thematic layers are generated in a GIS platform. Based on intersecting the layers, AHP method, the values for adopting the pairwise comparison normalized weight and normalized subclasses weightage were given. The normalized subclass weightage is input into each layer subclass. Then, weighted linear combination method is used to add the data layers in GIS platform to generate groundwater potential Index(GWPI) map. The GWPI map is validated based on the net recharge computed from the differences of measured groundwater levels between the pre-monsoon and post-monsoon in the year 2018. The kappa statistics are used to measure level spatial consistency between the GWPI and net recharge map. The overall average spatial matching accuracy between the two data sets is 0.86, while the kappa coefficient for GWPI with net recharge, 0.78. The results show that in Chittar watershed about 870 km~2 area is divided into high potential zone(i.e. sum of very high and high potential zone), 667 km~2 area, as the moderate one and the rest 105 km~2 area, as the poor zone(i.e. sum of very poor and poor potential zone).  相似文献   

15.
Groundwater resources in the semi-arid regions of southern India are under immense pressure due to large-scale groundwater abstraction vis-à-vis meager rainfall recharge. Therefore, understanding and evaluating the spatial distribution of groundwater is essential for viable utilization of the resource. Here, we assess groundwater potential at the watershed scale, in a semi-arid environment with crystalline aquifer system without a perennial surface water source using remote sensing, geophysical, and GIS-based integrated multi-parameter approach. GIS-based weighed overlay analysis is performed with input parameters, viz., geology, geomorphology, lineament density, land use, soil, drainage density, slope, and aquifer thickness. The watershed is categorized into four zones, namely, “very good” (GWP4), “good” (GWP3), “moderate” (GWP2), and “low” (GWP1) in terms of groundwater potential. Overall, ~?70% of the study area falls under moderate to low groundwater potential, indicating a serious threat to the future availability of the resource. Therefore, serious measures are required for maintaining aquifer resilience in this over-exploited aquifer (e.g., restricting groundwater withdrawal from GWP1 and GWP2 zones). Further, as the aquifer is under tremendous anthropogenic pressure, rainwater harvesting and artificial recharge during monsoon are advocated for sustainable aquifer management. Due to the direct dependence of crop production vis-à-vis farmer economy on groundwater, this study is an important step towards sustainable groundwater management and can be applied in diverse hydrological terrains.  相似文献   

16.
The impact of mining causes deterioration of environment and decline of groundwater level in the adjoining mining areas, which influences groundwater source for domestic and agriculture purposes. This necessitated locating and exploiting of new groundwater source. A fast, cost-effective and economical way of locating and exploration is to study and analyze remote sensing data. Interpreted remote sensing data were used to select sites for carrying out surface geophysical investigations. Various geomorphologic units were demarcated, and the lineaments were identified by interpretation of false color composite satellite imageries. The potential for occurrence of groundwater in the Sukinda Valley was classified as very good, good, moderate and poor by interpreting the images. Sub-surface geophysical investigations, namely vertical electrical soundings, were carried out to delineate and demarcate potential water-bearing zones. Integrated studies of interpretation of geomorphologic, lineaments and geophysical data (aquifer thickness) were used to prepare groundwater potential map. The studies reveal that the groundwater potential of shallow aquifers is due to geomorphologic features, and the potential of deeper aquifers is determined by lineaments and degree of weathering.  相似文献   

17.
Identifying a good site for groundwater exploitation in hard-rock terrains is a challenging task. In Sinai, Egypt, groundwater is the only source of water for local inhabitants. Interpretation of satellite data for delineation of lithological units and weathered zones, and for mapping of lineament density and their trends, provides a valuable aid for the location of groundwater promising areas. Complex deformational histories of the wide range of lithological formations add to the difficulty. Groundwater prospect mapping is a systematic approach that considers the major controlling factors which influence the aquifer and quality of groundwater. The presented study aims to delineate, identify, model and map groundwater potential zones in arid South Sinai using remote sensing data and a geographic information system (GIS) to prepare various hydromorphogeological thematic maps such as maps of slope, drainage density, lithology, landforms, structural lineaments, rainfall intensity and plan curvature. The controlling-factor thematic maps are each allocated a fixed score and weight, computed by using a linear equation approach. Furthermore, each weighted thematic map is statistically computed to yield a groundwater potential zone map of the study area. The groundwater potential zones thus obtained were divided into five categories (very poor, poor, moderate, good and very good) and were validated using the relation between the zone and the spatial distribution of productive wells and of previous geophysical investigations from a literature review. The results show the groundwater potential zones in the study area, and create awareness for better planning and management of groundwater resources.  相似文献   

18.
Various groundwater potential zones for the assessment of groundwater availability in the Bojnourd basin have been investigated using remote sensing, GIS, and a probabilistic approach. Five independent groundwater factors, including topography, ground slope, stream density, geology units, lineament density, and a groundwater productivity factor, i.e., springs’ discharge, were applied. Discharge rates of 226 springs over the area were collected, and the probabilistic model was designed by the discharge rates of springs as the dependent variable. For training the probabilistic model, a ratio of 70/30% of springs’ discharge was applied and discharge rates of 151 springs were selected to randomly train the model. The frequency ratio for each factor was calculated, and the groundwater potential zones were extracted by summation of frequency ratio maps. The groundwater potential map was also classified into four classes, viz., “very good” (with a frequency ratio of >6.75), “good” (5.5FR6.75), “moderate” (4.75FR5.5), and “poor” (FR4.75). Then, the model was verified based on a success-rate curve method which resulted in obtaining an accuracy ratio of 75.77%. Finally, sensitivity analysis was applied by a factor removal method in five steps. Results reveal that topography factor has the biggest effect on the groundwater potential map and removing this factor eventuates in the lowest accuracy of the final map (AUC = 63. 73%). The groundwater potential map is fairly affected by removing the lineament density factor with an accuracy of 68.80%. Removing the lineament density factor has the lowest effect on the final map with accuracy of 68.80%.  相似文献   

19.
In this study, we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models. We created a geographic information system database, and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth, aerial photographs, and other validated sources. A support vector regression (SVR) machine-learning model was used to divide the landslide inventory into training (70%) and testing (30%) datasets. The landslide susceptibility map was produced using 14 causative factors. We applied the established gray wolf optimization (GWO) algorithm, bat algorithm (BA), and cuckoo optimization algorithm (COA) to fine-tune the parameters of the SVR model to improve its predictive accuracy. The resultant hybrid models, SVR-GWO, SVR-BA, and SVR-COA, were validated in terms of the area under curve (AUC) and root mean square error (RMSE). The AUC values for the SVR-GWO (0.733), SVR-BA (0.724), and SVR-COA (0.738) models indicate their good prediction rates for landslide susceptibility modeling. SVR-COA had the greatest accuracy, with an RMSE of 0.21687, and SVR-BA had the least accuracy, with an RMSE of 0.23046. The three optimized hybrid models outperformed the SVR model (AUC = 0.704, RMSE = 0.26689), confirming the ability of metaheuristic algorithms to improve model performance.  相似文献   

20.
The geospatial mapping of groundwater prospective zones is essential to support the needs of local inhabitants and agricultural activities in arid regions such as El-Qaà area, Sinai Peninsula, Egypt. The study aims to locate new wells that can serve to cope with water scarcity. The integration of remote sensing, geographic information systems (GIS) and geophysical techniques is a breakthrough for groundwater prospecting. Based on these techniques, several factors contributing to groundwater potential in El-Qaà Plain were determined. Geophysical data were supported by information derived from a digital elevation model, and from geologic, geomorphologic and hydrologic data, to reveal the promising sites. All the spatial data that represent the contributing factors were integrated and analyzed in a GIS framework to develop a groundwater prospective model. An appropriate weightage was specified to each factor based on its relative contribution towards groundwater potential, and the resulting map delineates the study area into five classes, from very poor to very good potential. The very good potential zones are located in the Quaternary deposits, with flat to gentle topography, dense lineaments and structurally controlled drainage channels. The groundwater potential map was tested against the distribution of groundwater wells and cultivated land. The integrated methodology provides a powerful tool to design a suitable groundwater management plan in arid regions.  相似文献   

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