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1.
The main aim of this study is to generate groundwater spring potential maps for the Ningtiaota area (China) using three statistical models namely statistical index (SI), index of entropy (IOE) and certainty factors (CF) models. Firstly, 66 spring locations were identified by field surveys, out of which, 46 (70%) spring locations were randomly selected for training the models and the rest 20 (30%) spring locations were used for validation. Secondly, 12 spring influencing factors, namely slope angle, slope aspect, altitude, profile curvature, plan curvature, sediment transport index, stream power index, topographic wetness index, distance to roads, distance to streams, lithology and normalized difference vegetation index (NDVI) were derived from the spatial database. Subsequently, using the mentioned factors and the three models, groundwater spring potential values were calculated and the results were plotted in ArcGIS 10.0. Finally, the area under the curve was used to validate groundwater spring potential maps. The results showed that the IOE model, with the highest success rate of 0.9126 and the highest prediction rate of 0.9051, showed the preferable performance in this study. The results of this study may be helpful for planners and engineers in groundwater resource management and other similar watersheds.  相似文献   

2.
Groundwater is the most valuable natural resource in arid areas. Therefore, any attempt to investigate potential zones of groundwater for further management of water supply is necessary. Hence, many researchers have worked on this subject all around the world. On the other hand, the Generalized Additive Model (GAM) has been applied to environmental and ecological modelling, but its applicability to other kinds of predictive modelling such as groundwater potential mapping has not yet been investigated. Therefore, the main purpose of this study is to evaluate the performance of GAM model and then its comparison with three popular GIS-based bivariate statistical methods, namely Frequency Ratio (FR), Statistical Index (SI) and Weight-of-Evidence (WOE) for producing groundwater spring potential map (GSPM) in Lorestan Province Iran. To achieve this, out of 6439 existed springs, 4291 spring locations were selected for training phase and the remaining 2147 springs for model evaluation. Next, the thematic layers of 12 effective spring parameters including altitude, plan curvature, slope angle, slope aspect, drainage density, distance from rivers, topographic wetness index, fault density, distance from fault, lithology, soil and land use/land cover were mapped and integrated using the ArcGIS 10.2 software to generate a groundwater prospect map using mentioned approaches. The produced GSPMs were then classified into four distinct groundwater potential zones, namely low, moderate, high and very high classes. The results of the analysis were finally validated using the receiver operating characteristic (ROC) curve technique. The results indicated that out of four models, SI is superior (prediction accuracy of 85.4%) following by FR, GAM and WOE, respectively (prediction accuracy of 83.7, 77 and 76.3%). The result of groundwater spring potential map is helpful as a guide for engineers in water resources management and land use planning in order to select suitable areas to implement development schemes and also government entities.  相似文献   

3.
Water shortage and population growth in Iran rapidly diminish groundwater supplies. Thus, finding the techniques such as GIS that can be used as powerful tools in groundwater management, and predicting groundwater potential is required. The main objective of this study is to evaluate the efficiency of the statistical index (SI), frequency ratio (FR) weights of evidence (WoE) and evidential belief function (EBF) models for groundwater potential mapping at Kuhdasht region, Lorestan province, Iran. For this purpose, 12 groundwater influencing factors were considered in this investigation. From 171 available wells in the study area, 114 wells (67%) and 57 wells (33%) were used based on random selection in SI, FR, WoE and EBF models as training and validation data-sets, respectively. The area under the ROC curve (AUC) for SI, FR, WoE and EBF models was calculated as 91.8, 91, 93.6 and 93.3%, respectively. These curve values indicated that all four models have reasonably good accuracy in spatially predicting groundwater potential in this area.  相似文献   

4.
The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM and LANDSAT images of spatial resolution 30?m were used to construct groundwater potential zones (GPZ) map by integrating geological fractures, drainage network, slope and relief, and convergence index maps of the study area. Weight and score of each map were developed according to their level of contribution toward groundwater accumulation and spatial distribution of groundwater wells. The area that has very high potential for groundwater is located at the foot of Oman Mountains and Al Dhaid Depression covering an area of about 59.33?km², which is 4.40% of the study area. Further hydrological map and data on hydraulic properties of shallow aquifer, as recorded from observation wells in the regions, have been used to validate the produced GPZ map. The validation result showed sufficient agreement between the produced GPZ map.  相似文献   

5.
The study aims at delineating groundwater potential zones using geospatial technology and analytical hierarchy process (AHP) techniques in mining impacted hard rock terrain of Ramgarh and part of Hazaribagh districts, Jharkhand, India. Relevant thematic layers were prepared and assigned weight based on Saaty’s 9-point scale and normalized by eigenvector technique of AHP to identify groundwater prospect in the study area. The weighted linear combination method was applied to prepare the groundwater potential index in geographic information system. Final groundwater prospects were classified as excellent, very good, good, moderate, poor and very poor groundwater potential zones. Study thus revealed that the excellent, very good and good groundwater potential zones, respectively, cover 148.3, 373.66 and 438.86 km2 of the study area, whereas the poor groundwater potential zone covers 180.05 km2. Validation was done through a receiver operating characteristic curve, which indicated that AHP had good prediction accuracy (AUC = 75.45%).  相似文献   

6.
A methodology for groundwater evaluation has been developed by the combined use of numerical model and spatial modeling using GIS. The developed methodology has been applied on the sub-basin of the Banganga River, India. Initially, the groundwater potential zones have been delineated by spatial modeling. Different thematic maps of the basin like geology, geomorphology, soil, drainage, slope factor and landuse/landcover have been used to identify the groundwater potential zones. Further, the groundwater flow model for the study area has been developed in the MODFLOW. The groundwater flow vector map has been developed and superimposed on the potential zone map to validate the results of spatial modeling. Finally, the different scenarios have been conceptualized by varying the discharge of the wells and purposing the location for new rainwater harvesting structures. Results reveal that increasing the discharge of the wells in the potential zones put less stress on the aquifer. The suggested locations of rainwater harvesting structures also help to reduce the overall decline of groundwater in the area. The hydrological and spatial modeling presented in this study is highly useful for the evaluation of groundwater resources and for deciding the location of rainwater harvesting structures in semi-arid regions.  相似文献   

7.
The main objective of the present work is to delineate the groundwater potential zones in Bilari watershed of district Shivpuri, Madhya Pradesh. Remote Sensing data and GIS were used to delineate the groundwater potential zones of the area. IRS-1D (LISS III) data have been utilized to extract information on various themes such as geomorphology, structure, drainage and land use/land cover. Available lithology and soil maps have also been used. DEM has been generated from contours taken from Survey of India topographical maps in order to obtain the slope percentage and slope aspect of the area. The groundwater potential zones were delineated by weighted overlay analysis. The themes geology, geomorphology, slope and soil were considered and the weightages assigned to different classes of respective themes according to their role in groundwater potential. Finally, five groundwater potential zones viz., very good, good, moderate; poor and very poor were delineated for the study area. It was estimated that about 110.41?sq km area which forms 37.55% of the total area are in the zones of very poor, poor and moderate category and about 183.75?sq km (62.45%) in zones of good and very good category.  相似文献   

8.
The development of groundwater favourability map is an effective tool for the sustainability management of groundwater resources in typical agricultural regions, such as southern Perak Province, Malaysia. Assessing the potentiality and pollution vulnerability of groundwater is a fundamental phase of favourability mapping. A geographic information system (GIS)-based Boolean operator of a spatial analyst module was applied to combine a groundwater potentiality map (GPM) model and a groundwater vulnerability to pollution index (GVPI) map, thereby establishing the favourable zones for drinking water exploration in the investigated area. The area GPM model was evaluated by applying a GIS-based Dempster–Shafer–evidential belief function model. In the evaluation, six geoelectrically determined groundwater potential conditioning factors (i.e. overburden resistivity, overburden thickness, aquifer resistivity, aquifer thickness, aquifer transmissivity and hydraulic conductivity) were synthesized by employing the probability-based algorithms of the model. The generated thematic maps of the seven hydrogeological parameters of the DRASTIC model were considered as pollution potential conditioning factors and were analysed with the developed ordered weighted average–DRASTIC index model algorithms to construct the GVPI map. Approximately 88.8 and 85.71% prediction accuracies for the Groundwater Potentiality and GVPI maps were established using the reacting operating characteristic curve method and water quality status–vulnerability zone relationship scheme, respectively. Finally, the area groundwater favourability map (GFM) model was produced by applying a GIS-based Boolean operator on the Groundwater Potentiality and GVPI maps. The GFM model reveals three distinct zones: ‘not suitable’, ‘less suitable’ and ‘very suitable’ zones. The area analysis of the GFM model indicates that more than 50% of the study area is covered by the ‘very suitable’ zones. Results produce a suitability map that can be used by local authorities for the exploitation and management of drinking water in the area. The study findings can also be applied as a tool to help increase public awareness of groundwater issues in developing countries.  相似文献   

9.
The present study has been undertaken to delineate the groundwater potential zones in the hard rock terrain of Palamu district, Jharkhand using the advanced applications of remote sensing, geographical information systems and analytic hierarchy process techniques. The integration and analyses of various thematic databases viz., geomorphology, lithology, soil, slope, lineament density, weathered zone thickness, drainage density and rainfall proved useful in the delineation of GWP zones. The study indicates that only 136?km2 of the study area exhibit excellent groundwater potential, 248?km2 has very good groundwater potential, whereas 36.89 and 38.23% are under poor and very poor groundwater potential zones, respectively. Hence, only a total of 11.6% of the area (490?km2) is classified as high to excellent groundwater potential. The final groundwater prospect map obtained was classified as excellent potential, very good potential, good potential, moderate potential, poor potential and very poor potential zone.  相似文献   

10.
ABSTRACT

Several machine learning regression models have been advanced for the estimation of crop biophysical parameters with optical satellite imagery. However, literature on the comparative performances of such models is still limited in range and scope, especially under multiple data sources, despite the potential of multi-source imagery to improving crop monitoring in cloudy areas. To fill in this knowledge gap, this study explored the synergistic use of Landsat-8, Sentinel-2A, China’s environment and disaster monitoring and forecasting satellites (HJ-1 A and B) and Gaofen-1 (GF-1) data to evaluate four machine learning regression models that include Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and Gradient Boosting Decision Tree (GBDT), for rice dry biomass estimation and mapping. Taking a major rice cultivation area in southeast China as case study during the 2016 and 2017 growing seasons, a cross-calibrated time series of the Enhanced Vegetation Index (EVI) was obtained from the quad-source optical imagery and on which the aforementioned models were applied, respectively. Results indicate that in the before rice heading scenario, the most accurate dry biomass estimates were obtained by the GBDT model (R2 of 0.82 and RMSE of 191.8 g/m2) followed by the RF model (R2 of 0.79 and RMSE of 197.8 g/m2). After heading, the k-NN model performed best (R2 of 0.43 and RMSE of 452.1 g/m2) followed by the RF model (R2 of 0.42 and RMSE of 464.7 g/m2). Whist the k-NN model performed least in the before heading scenario, SVM performed least in the after heading scenario. These findings may suggest that machine learning regression models based on an ensemble of decision trees (RF and GBDT) are more suitable for the estimation of rice dry biomass, at least with optical satellite imagery. Studies that would extend the evaluation of these machine learning models, to other parameters like leaf area index, and to microwave imagery, are hereby recommended.  相似文献   

11.
Abstract

The present study was an attempt to delineate potential groundwater zones in Kalikavu Panchayat of Malappuram district, Kerala, India. The geo-spatial database on geomorphology, landuse, geology, slope and drainage network was generated in a geographic information system (GIS) environment from satellite data, Survey of India topographic sheets and field observations. To understand the movement and occurrence of groundwater, the geology, geomorphology, structural set-up and recharging conditions have to be well understood. In the present study, the potential recharge areas are delineated in terms of geology, geomorphology, land use, slope, drainage pattern, etc. Various thematic data generated were integrated using a heuristic method in the GIS domain to generate maps showing potential groundwater zones. The composite output map scores were reclassified into different zones using a decision rule. The final output map shows different zones of groundwater prospect, viz., very good (15.57% of the area), good (43.74%), moderate (28.38%) and poor (12.31%). Geomorphic units such as valley plains, valley fills and alluvial terraces were identified as good to excellent prospect zones, while the gently sloping lateritic uplands were identified as good to moderate zones. Steeply sloping hilly terrains underlain by hard rocks were identified as poor groundwater prospect zones.  相似文献   

12.
The use of remote sensing data with other ancillary data in a geographic information system (GIS) environment is useful to delineate groundwater potential zonation map of Ken–Betwa river linking area of Bundelkhand. Various themes of information such as geomorphology, land use/land cover, lineament extracted from digital processing of Landsat (ETM+) satellite data of the year 2005 and drainage map were extracted from survey of India topographic sheets, and elevation, slope data were generated from shuttle radar topography mission (SRTM) digital elevation model (DEM). These themes were overlaid to generate groundwater potential zonation (GWPZ) map of the area. The final map of the area shows different zones of groundwater prospects, viz., good (5.22% of the area), moderate (65.83% of the area) poor (15.31% of the area) and very poor (13.64% of area).  相似文献   

13.
Groundwater exploration in the Western Doon valley has been carried out to delineate the groundwater potential and groundwater quality zones suitable for domestic purposes based on the integrated use of Remote Sensing and Geographical Information Systems (GIS). The Western Doon Valley, occupying broad synclinal troughs in the evolving fold-thrust system of sub-Himalaya, which is filled by post-Siwalik fluvial and debris flow deposits in the late Quaternary-Holocene. The Western Doon Valley area is bounded by the Mussoorie range in the north with 1800–2800 m elevation and in the south by young topographic relief of the frontal Siwalik range with ~800 m average elevation. Groundwater quality of Western Doon valley through pictorially representation in the GIS environment, it is inferred that calcium, magnesium, total hardness and nitrate at some locations above the desirable limit. The groundwater prospects map has been prepared by integrating the hydrogeomorphologic, land use/land cover from satellite data (IRS-ID, LISS-III data) slope, soil, drainage density, depth to water table of pre-monsoon and post-monsoon periods (unconfined aquifer), water table fluctuation, static water level (confined to semi-confined aquifers), specific capacity, discharge and drawdown maps using index overlay method in the GIS environment. The groundwater prospects are depicted in five categories Very high, high, moderate, low and very low (runoff zone) integrated with the groundwater quality zones which have been prepared from hydrochemical data. The results indicated that 16.82 % of the area is under Very high potential zone category with 16.11 % and 0.71 % of desirable and undesirable quality of groundwater and 18.65 %, 42.06 %, 6.96 % and 15.46 % classified as high, moderate, low and very low potential zones with desirable and undesirable quality of groundwater for domestic purposes. This study be useful for designing the groundwater prospects and management plan for the sustainable development of study area.  相似文献   

14.
Assessment of groundwater potential zones using GIS technique   总被引：1，自引：0，他引：1  
A case study was conducted to find out the groundwater potential zones in Kattakulathur block, Tamil Nadu, India with an aerial extent of 360.60 km2. The thematic maps such as geology, geomorphology, soil hydrological group, land use / land cover and drainage map were prepared for the study area. The Digital Elevation Model (DEM) has been generated from the 10 m interval contour lines (which is derived from SOI, Toposheet 1:25000 scale) and obtained the slope (%) of the study area. The groundwater potential zones were obtained by overlaying all the thematic maps in terms of weighted overlay methods using the spatial analysis tool in ArcGIS 9.2. During weighted overlay analysis, the ranking has been given for each individual parameter of each thematic map and weights were assigned according to the influence such as soil −25%, geomorphology − 25%, land use/land cover −25%, slope − 15%, lineament − 5% and drainage / streams − 5% and find out the potential zones in terms of good, moderate and poor zones with the area of 49.70 km2, 261.61 km2 and 46.04 km2 respectively. The potential zone wise study area was overlaid with village boundary map and the village wise groundwater potential zones with three categories such as good, moderate and poor zones were obtained. This GIS based output result was validated by conducting field survey by randomly selecting wells in different villages using GPS instruments. The coordinates of each well location were obtained by GPS and plotted in the GIS platform and it was clearly shown that the well coordinates were exactly seated with the classified zones.  相似文献   

15.
The present work accentuated the expediency of remote sensing and geographic information system (GIS) applications in groundwater studies, especially in the identification of groundwater potential zones in Ithikkara River Basin (IRB), Kerala, India. The information on geology, geomorphology, lineaments, slope and land use/land cover was gathered from Landsat ETM + data and Survey of India (SOI) toposheets of scale 1:50,000 in addition, GIS platform was used for the integration of various themes. The composite map generated was further classified according to the spatial variation of the groundwater potential. Four categories of groundwater potential zones namely poor, moderate, good and very good were identified and delineated. The hydrogeomorphological units like valley fills and alluvial plain and are potential zones for groundwater exploration and development and valley fills associated with lineaments is highly promising area for groundwater extraction. The spatial variation of the potential indicates that groundwater occurrence is controlled by geology, structures, slope and landforms.  相似文献   

16.
The remote sensing data combined with Geographical Information System (GIS) technique has been proved to be very efficient in identification of groundwater potential of any area. In the present paper, IRS 1 A, LISS II data has been used to identify the groundwater potential zones by integrating various thematic maps generated on 1:50,000 scale. These maps are integrated after assigning weight factors to the identified features in each thematic map depending upon their infiltration capacities and the groundwater potential zones in Bhamini mandai (developmental block) of Srikakulam district, Andhra Pradesh are demarcated. The area of investigation has been classified into seven groundwater potential zones. The present results show that integration of all attributes provides more accurate results in groundwater potential zones identification.  相似文献   

17.
High spatial resolution mapping of natural resources is much needed for monitoring and management of species, habitats and landscapes. Generally, detailed surveillance has been conducted as fieldwork, numerical analysis of satellite images or manual interpretation of aerial images, but methods of object-based image analysis (OBIA) and machine learning have recently produced promising examples of automated classifications of aerial imagery. The spatial application potential of such models is however still questionable since the transferability has rarely been evaluated.We investigated the potential of mosaic aerial orthophoto red, green and blue (RGB)/near infrared (NIR) imagery and digital elevation model (DEM) data for mapping very fine-scale vegetation structure in semi-natural terrestrial coastal areas in Denmark. The Random Forest (RF) algorithm, with a wide range of object-derived image and DEM variables, was applied for classification of vegetation structure types using two hierarchical levels of complexity. Models were constructed and validated by cross-validation using three scenarios: (1) training and validation data without spatial separation, (2) training and validation data spatially separated within sites, and (3) training and validation data spatially separated between different sites.Without spatial separation of training and validation data, high classification accuracies of coastal structures of 92.1% and 91.8% were achieved on coarse and fine thematic levels, respectively. When models were applied to spatially separated observations within sites classification accuracies dropped to 85.8% accuracy at the coarse thematic level, and 81.9% at the fine thematic level. When the models were applied to observations from other sites than those trained upon the ability to discriminate vegetation structures was low, with 69.0% and 54.2% accuracy at the coarse and fine thematic levels, respectively.Evaluating classification models with different degrees of spatial correlation between training and validation data was shown to give highly different prediction accuracies, thereby highlighting model transferability and application potential. Aerial image and DEM-based RF models had low transferability to new areas due to lack of representation of aerial image, landscape and vegetation variation in training data. They do, however, show promise at local scale for supporting conservation and management with vegetation mappings of high spatial and thematic detail based on low-cost image data.  相似文献   

18.
Photo-geomorphic and hydrogeological studies over an area of 2020 sq km in the semi-arid tract of Visakhapatnam district, have enabled in identifying five zones of potential groundwater/hydromorphic units. Fluvial plain has the highest hydromorphic potential with a weight point score of 25, and coastal plain has poor to very poor potential with a score of 12. Wash plain, rolling/sandy plain, and piedmont plain have good to moderate to poor potential with scores of 20, 17 and 14 respectively. Geophysical surveys and the drilling of exploratory borewells along fractures/fracture traces and abandoned channels, revealed the existence of deep weathered and fractured zones capable of yielding large quantities of water.  相似文献   

19.
Water is the most important natural resource which forms the core of the ecological system. The advent of remote sensing has opened up new vistas in groundwater prospect evaluation, exploration and management. The role of hydrogeomorphological units in tile storage of groundwater from the Kancheepuram distict has been investigated using IRS P6 LISS-III data. The Kancheepuram district exhibits diverse hydrogeomorphological conditions where the groundwater regime is controlled mainly by topography and geology. The extent of various water prospectus zones in terms of percentage includes, maximum area, particularly the north-western, and central part which is characterized by good potential occupying about 43% of total area. The moderate potential is marked by only 35%, and is scattered along the northern and southern side of the study area, the remaining 12% is of poor prospectus, which is falling in the coastal region of the study area.  相似文献   

20.
The rapid increase in human population has increased the groundwater resources demand for drinking, agricultural and industrial purposes. The main purpose of this study is to produce groundwater potential map (GPM) using weights-of-evidence (WOE) and evidential belief function (EBF) models based on geographic information system in the Azna Plain, Lorestan Province, Iran. A total number of 370 groundwater wells with discharge more than 10 m3s?1were considered and out of them, 256 (70%) were randomly selected for training purpose, while the remaining114 (30%) were used for validating the model. In next step, the effective factors on the groundwater potential such as altitude, slope aspect, slope angle, curvature, distance from rivers, drainage density, topographic wetness index, fault distance, fault density, lithology and land use were derived from the spatial geodatabases. Subsequently, the GPM was produced using WOE and EBF models. Finally, the validation of the GPMs was carried out using areas under the ROC curve (AUC). Results showed that the GPM prepared using WOE model has the success rate of 73.62%. Similarly, the AUC plot showed 76.21% prediction accuracy for the EBF model which means both the models performed fairly good predication accuracy. The GPMs are useful sources for planners and engineers in water resource management, land use planning and hazard mitigation purpose.  相似文献   

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