首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献，搜索用时 24 毫秒
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.
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   

3.
The objective of this study is to integrate geographic information system and bivariate frequency ratio method for the mapping of flowing well zones in the west and southwest parts of the Euphrates river basin of Iraq. Ten groundwater conditioning factors are identified as controlling factors of groundwater movement based on data availability, literature review, and expert’s opinions. The spatial association between flowing well locations and groundwater controlling factors is investigated by means of a probabilistic frequency ratio approach. Seventy percent or 148 wells from an inventory of 211 flowing wells in the study area are randomly selected for training, and the remaining 30 or 63% wells are used for validation of the probabilistic frequency ratio model. The estimated probabilistic ratio values are overlaid and summed to produce the groundwater potential index map. The results reveal that groundwater potential in 128,547 km2 or 84% of the total area is very low to low. The moderate potential zone covers an area of about 11,210 km2 or 7%, while the high and very high potential zones are found in an area of 12,982 km2 or 9% of the study area. Validation of obtaining results by means of a receiver operating characteristic technique reveals that the predictive accuracy of 94% indicating the excellent performance of the proposed approach for spatial zoning of groundwater flowing well boundary at Iraqi desert.  相似文献   

4.
The Ant Miner algorithm was compared with the bivariate frequency ratio (FR) and boosted regression trees (BRT) algorithms in terms of its capacity to assess groundwater potential. A geospatial dataset was prepared that contains two components: a flowing well inventory map and eleven factors relevant to groundwater conditions. Average nearest neighbor technique was used to investigate the spatial pattern of flowing wells and to find the appropriate distance between flowing and nonflowing points in the study area. A wrapper approach known as random forest classifier and a filtering approach known as information gain ratio were used to identify the most relevant groundwater factors. The developed models were validated via the area under the operating characteristic curve. Results revealed that the Ant Miner model performed better in terms of both success (0.944) and prediction (0.92) rates compared to FR and BRT. Furthermore, the Ant Miner algorithm derived five simple, easily interpreted rules for predicting groundwater potential that can be used by hydrogeologists for identifying potential groundwater well locations with minimal effort and cost.  相似文献   

5.
The aim of this paper is to use a knowledge-driven expert-based geographical information system (GIS) model coupling with remote-sensing-derived parameters for groundwater potential mapping in an area of the Upper Langat Basin, Malaysia. In this study, nine groundwater storage controlling parameters that affect groundwater occurrences are derived from remotely sensed imagery, available maps, and associated databases. Those parameters are: lithology, slope, lineament, land use, soil, rainfall, drainage density, elevation, and geomorphology. Then the parameter layers were integrated and modeled using a knowledge-driven GIS of weighted linear combination. The weightage and score for each parameter and their classes are based on the Malaysian groundwater expert opinion survey. The predicted groundwater potential map was classified into four distinct zones based on the classification scheme designed by Department of Minerals and Geoscience Malaysia (JMG). The results showed that about 17% of the study area falls under low-potential zone, with 66% on moderate-potential zone, 15% with high-potential zone, and only 0.45% falls under very-high-potential zone. The results obtained in this study were validated with the groundwater borehole wells data compiled by the JMG and showed 76% of prediction accuracy. In addition statistical analysis indicated that hard rock dominant of the study area is controlled by secondary porosity such as distance from lineament and density of lineament. There are high correlations between area percentage of predicted groundwater potential zones and groundwater well yield. Results obtained from this study can be useful for future planning of groundwater exploration, planning and development by related agencies in Malaysia which provide a rapid method and reduce cost as well as less time consuming. The results may be also transferable to other areas of similar hydrological characteristics.  相似文献   

6.
The Mugling–Narayanghat road section falls within the Lesser Himalaya and Siwalik zones of Central Nepal Himalaya and is highly deformed by the presence of numerous faults and folds. Over the years, this road section and its surrounding area have experienced repeated landslide activities. For that reason, landslide susceptibility zonation is essential for roadside slope disaster management and for planning further development activities. The main goal of this study was to investigate the application of the frequency ratio (FR), statistical index (SI), and weights-of-evidence (WoE) approaches for landslide susceptibility mapping of this road section and its surrounding area. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. A landslide inventory map was prepared using earlier reports, aerial photographs interpretation, and multiple field surveys. A total of 438 landslide locations were detected. Out these, 295 (67 %) landslides were randomly selected as training data for the modeling using FR, SI, and WoE models and the remaining 143 (33 %) were used for the validation purposes. The landslide conditioning factors considered for the study area are slope gradient, slope aspect, plan curvature, altitude, stream power index, topographic wetness index, lithology, land use, distance from faults, distance from rivers, and distance from highway. The results were validated using area under the curve (AUC) analysis. From the analysis, it is seen that the FR model with a success rate of 76.8 % and predictive accuracy of 75.4 % performs better than WoE (success rate, 75.6 %; predictive accuracy, 74.9 %) and SI (success rate, 75.5 %; predictive accuracy, 74.6 %) models. Overall, all the models showed almost similar results. The resultant susceptibility maps can be useful for general land use planning.  相似文献   

7.
Groundwater is one of the most valuable natural resources, which is an immensely important and dependable source of water supply in all climatic regions over the world. Groundwater is in demand in areas where surface water supply is inadequate and nonsexist in the Chhatna Block, Bankura district and is located on the eastern slope of Chotonagpur Plateau, which is mapped on 73 I/15, 73 I/16 and 73 M/3, and falls between latitude 23°10′–23°30′N and longitude 86°47′–87°02′E. It represents plain land and gentle slope, which is responsible for infiltration and groundwater recharge. The groundwater in this region is confined within the fracture zones and weathered residuum. The present investigation is, therefore, undertaken to delineate potential zones for groundwater development with the help of a remote-sensing study. IRS–LISS-III data along with other data sets, e.g., existing toposheets and field observation data, have been utilized to extract information on the hydrogeomorphic features which include valley fills, buried pediment moderate, buried pediment shallow and structural hills, lineament density contour and slope map of this hard rock terrain. The target of this study is to delineate the groundwater potential zones in Chhatna block, Bankura District, West Bengal. Satellite imagery, along with other data sets, has been utilized to extract information on the groundwater controlling features of this study area. Three features (hydrogeomorphology, slope, and lineaments) that influence groundwater occurrences were analyzed and integrated. All the information layers have been integrated through GIS analysis and the groundwater potential zones have been delineated. The weighted index overlay method has been followed to delineate groundwater potential zones. The results indicate that good to excellent groundwater potential zones are available in almost the entire block. The results show that there is good agreement between the predicted groundwater potential map and the existing groundwater borehole databases. The area is characterized by hard rock terrain—still due to the presence of planation surface along valley fills; it became the prospective zone. The area has been categorized into four distinct zones: excellent, good, fair and poor. Excellent groundwater potential zones constitute 30–35 % of the total block area; good groundwater potential zones occupy a majority of the block, covering approximately 55–60 % and the fair potential zones occupy about 10–15 % of the total block. Poor potential zones occupy a very insignificant portion (less than 1 %).  相似文献   

8.
The present work attempts to interpret the groundwater vulnerability of the Melaka State in peninsular Malaysia. The state of groundwater pollution in Melaka is a critical issue particularly in respect of the increasing population, and tourism industry as well as the agricultural, industrial and commercial development. Focusing on this issue, the study illustrates the groundwater vulnerability map for the Melaka State using the DRASTIC model together with remote sensing and geographic information system (GIS). The data which correspond to the seven parameters of the model were collected and converted into thematic maps by GIS. Seven thematic maps defining the depth to water level, net recharge, aquifer media, soil media, topography, impact of vadose zone and hydraulic conductivity were generated to develop the DRASTIC map. In addition, this map was integrated with a land use map for generating the risk map to assess the effect of land use activities on the groundwater vulnerability. Three types of vulnerability zones were assigned for both DRASTIC map and risk map, namely, high, moderate and low. The DRASTIC map illustrates that an area of 11.02 % is low vulnerability, 61.53 % moderate vulnerability and 23.45 % high vulnerability, whereas the risk map indicates that 14.40 % of the area is low vulnerability, 47.34 % moderate vulnerability and 38.26 % high vulnerability in the study area. The most vulnerability area exists around Melaka, Jasin and Alor Gajah cities of the Melaka State.  相似文献   

9.
Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments,but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT)model and the K-means cluster algorithm to produce a regional landslide susceptibility map.Yanchang County,a typical landslide-prone area located in northwestern China,was taken as the area of interest to introduce the proposed application procedure.A landslide inventory containing 82 landslides was prepared and subse-quently randomly partitioned into two subsets:training data(70％landslide pixels)and validation data(30％landslide pixels).Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means clus-ter algorithm.The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC)curve)of the proposed model was the highest,reaching 0.88,compared with traditional models(support vector machine(SVM)=0.85,Bayesian network(BN)=0.81,frequency ratio(FR)=0.75,weight of evidence(WOE)=0.76).The landslide frequency ratio and fre-quency density of the high susceptibility zones were 6.76/km2 and 0.88/km2,respectively,which were much higher than those of the low susceptibility zones.The top 20％interval of landslide occurrence probability contained 89％of the historical landslides but only accounted for 10.3％of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without contain-ing more"stable"pixels.Therefore,the obtained susceptibility map is suitable for application to landslide risk management practices.  相似文献   

10.
The study demonstrates the potential of geographical information system and statistical-based approaches to identify the hydrological processes and demarcate the groundwater prospect zones of the Gangolli basin, Karnataka State, India. The basin is situated in humid tropical climate and influenced by three major rivers viz. Kollur (6th order stream), Chakra (6th order stream) and Haladi (7th order stream) which cover an area of ~1,512 km2 and cumulative length of ~84 km. Various thematic maps—drainage, geomorphology, geology, slope, soil, lineament and lineament density—were prepared using Survey of India topographic maps, Indian remote sensing (IRS-P6) images and other published maps. Hydrogeomorphologic characteristics were correlated with different morphometric parameters to identify the hydrological processes and demarcate the groundwater potential zones of the basin. All the hydrological units and morphometric parameters were assigned suitable weightages according to their relative importance to groundwater potentiality to identify the most deficit/surplus zones of groundwater. Based on hydrological characteristics, integrated thematic maps reveal that ~14 % (~217 km2) of basin area falls under very good, ~32 % (~486 km2) under good, ~23 % (~353 km2) under moderate, and 30 % (~443 km2) under poor zones for groundwater potential. From the sub-basin-wise prioritisation, it has been inferred that SB-III scored highest groundwater potential, followed by SB-X. Result of morphometric analyses with the hydrologic parameters indicates that ~99 % area of SB-III and SB-X are under very good to moderate groundwater potential zone. This study clearly demonstrates that hydrological parameters in relation with morphometric analyses are useful to demarcate the prospect zones of groundwater.  相似文献   

11.
A case study was conducted to find the groundwater potential zones in an area between the Serang and Bogowonto rivers, Kulon Progo Regency, Java, Indonesia. The objectives of this study were to delineate the groundwater potential zone based on a number of groundwater parameters that can be surveyed in the field and to incorporate the geomorphological conditions into these data. The geomorphology interpretation was conducted using the landform approach. This approach begins by preparing supporting data such as an Indonesian Topographic Map containing contour and land use data; a regional geology map containing lithology type and geology structures; and soil, climate, and hydrological data. The determination of the geomorphology unit was conducted manually by the visual interpretation of Digital Landsat ETM+ with some image interpretation keys. Four groundwater parameters were surveyed in the field: (a) depth to the water table, (b) water table fluctuation, (c) fluid electrical conductivity to represent groundwater quality, and (d) aquifer thickness. The groundwater potential zones were obtained by overlaying all the groundwater field parameters in terms of weighted overlay methods using the spatial analysis tool in ArcGIS 9.2. During the weighted overlay analysis, rankings were produced for each individual parameter of each groundwater field parameter, and weights were assigned based on the amount of influence they had (i.e., depth to the water table—30 %, water table fluctuation—20 %, aquifer thickness—30 %, and fluid conductivity—20 %). We then found the good, moderate, and poor zones in terms of groundwater potential, which had areas of 5.83, 4.53, and 2.36 km2, respectively. Areas with good groundwater potential are located largely within sand dunes, beach ridges, beaches, and fluviomarine plain landforms, which are characterized by a shallow water table, low fluctuation, thick aquifer, and low EC value. Moderate groundwater zones are generally characterized by poor water quality (high EC value), which is found to some degree in the alluvial plain. The regions with poor groundwater potential are spread mainly across the landforms composed of igneous rock (thin aquifers), such as denudational hills, which act as run-off zones due to their steep slope.  相似文献   

12.
This is the first landslide inventory map in the island of Lefkada integrating satellite imagery and reports from field surveys. In particular, satellite imagery acquired before and after the 2003 earthquake were collected and interpreted with the results of the field survey that took place 1 week after this strong (Mw?=?6.3) event. The developed inventory map indicates that the density of landslides decreases from west to east. Furthermore, the spatial distribution of landslides was statistically analyzed in relation to the geology and topography for investigating their influence to landsliding. This was accomplished by overlaying these causal factors as thematic layers with landslide distribution data. Afterwards, weight values of each factor were calculated using the landslide index method and a landslide susceptibility map was developed. The susceptibility map indicates that the highest susceptibility class accounts for 38 % of the total landslide activity, while the three highest classes that cover the 10 % of the surface area, accounting for almost the 85 % of the active landslides. Our model was validated by applying the approaches of success and prediction rate to the dataset of landslides that was previously divided into two groups based on temporal criteria, estimation and validation group. The outcome of the validation dataset was that the highest susceptibility class concentrates 18 % of the total landslide activity. However, taking into account the frequency of landslides within the three highest susceptibility classes, more than 85 %, the model is characterized as reliable for a regional assessment of earthquake-induced landslides hazard.  相似文献   

13.
During the last three decades, remotely sensed data (both satellite images and aerial photographs) have been increasingly used in groundwater exploration and management exercises. An integrated approach has been adopted in the present study to delineate groundwater recharge potential zones using RS and GIS techniques. IRS-1C satellite imageries and Survey of India toposheets are used to prepare various thematic layers viz. geology, soil, land-use, slope, lineament and drainage. These layers were then transformed in to raster data using feature to raster converter tool in ArcGIS 9.3 software. The raster maps of these factors are allocated a fixed score and weight computed from Influencing Factor (IF) technique. The weights of factors contributing to the groundwater recharge are derived using aerial photos, geology maps, a land use database, and field verification. Subjective weights are assigned to the respective thematic layers and overlaid in GIS platform for the identification of potential groundwater recharge zones within the study area. Then these potential zones were categories as ‘high’, ‘moderate’, ‘low’, ‘poor’. The resulted map shows that 19 % of the area has highest recharge potential, mainly confined to buried pediplain, agriculture land-use and river terraces (considerable amount of precipitated water percolates into subsurface), 28 % of the area has moderate groundwater recharge potentiality and rest of the area has low to poor recharge potentiality. The residual hills and linear ridges with steep slopes are not suitable for artificial recharge sites. Finally, 13 % of total average annual precipitated water (840 mm) percolates downward and ultimately contributes to recharge the aquifers in the Kovilpatti Municipality area. The paper is an attempt to suggest for maintaining the proper balance between the groundwater quantity and its exploitation.  相似文献   

14.
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   

15.
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   

16.
Airborne magnetic data and Landsat imagery, as well as time-domain electromagnetic soundings, were used to assess groundwater potential of the region around the town of Mafikeng in North West Province, South Africa. Lineaments were extracted based on on-screen digital data derived from magnetic and Landsat 7 imagery. The relationship between lineament-intersection density and borehole yield was assessed using statistical analysis. The results were discussed with respect to factors that determine the groundwater potential of the area. Correlation between lineament-intersection frequency and borehole yield is 60?C65% in the south and southeastern parts, and 45?C50% within 5?C30?km radius of Mafikeng. The eastern and western parts of the study area are characterized by weak or no correlation. Areas that correspond to high correlation between lineament-intersection frequency and borehole yield suggest the significance of cross-cutting structures in controlling groundwater potential zones, while results that suggest low or no correlation represent the influence of other factors. The overall results demonstrate that the combined analysis of airborne magnetic data, satellite imagery, borehole yield and ground-based time-domain electromagnetic soundings provide the best approach for groundwater assessment within the hard rock and carbonate terrains of the study area.  相似文献   

17.
Without a doubt, landslide is one of the most disastrous natural hazards and landslide susceptibility maps (LSMs) in regional scale are the useful guide to future development planning. Therefore, the importance of generating LSMs through different methods is popular in the international literature. The goal of this study was to evaluate the susceptibility of the occurrence of landslides in Zonouz Plain, located in North-West of Iran. For this purpose, a landslide inventory map was constructed using field survey, air photo/satellite image interpretation, and literature search for historical landslide records. Then, seven landslide-conditioning factors such as lithology, slope, aspect, elevation, land cover, distance to stream, and distance to road were utilized for generation LSMs by various models: frequency ratio (FR), logistic regression (LR), artificial neural network (ANN), and genetic programming (GP) methods in geographic information system (GIS). Finally, total four LSMs were obtained by using these four methods. For verification, the results of LSM analyses were confirmed using the landslide inventory map containing 190 active landslide zones. The validation process showed that the prediction accuracy of LSMs, produced by the FR, LR, ANN, and GP, was 87.57, 89.42, 92.37, and 93.27 %, respectively. The obtained results indicated that the use of GP for generating LSMs provides more accurate prediction in comparison with FR, LR, and ANN. Furthermore; GP model is superior to the ANN model because it can present an explicit formulation instead of weights and biases matrices.  相似文献   

18.
Gully erosion is an important environmental issue with severe impacts. This study aimed to characterize gully erosion susceptibility and assess the capability of information value (InfVal) and frequency ratio (FR) models for its spatial prediction in Ourika watershed of the High Atlas region of Morocco. These two bivariate statistical methods have been used for gully erosion susceptibility mapping by comparing each data layer of causative factor to the existing gully distribution. Weights to the gully causative factors are assigned based on gully density. Gullies have been mapped through field surveys and Google earth high-resolution images. Lithofacies, land use, slope gradient, length-slope, aspect, stream power index, topographical wetness index and plan curvature were considered predisposing factors to gullying. The digitized gullies were randomly split into two parts. Sixty-five percent (65%) of the mapped gullies were randomly selected as training set to build gully susceptibility models, while the remaining 35% cases were used as validation set for the models’ validation. The results showed that barren and sparse vegetation lands and slope gradient above 50% were very susceptible to gully erosion. The ROC curve was used for testing the accuracy of the mentioned models. The analysis confirms that the FR model (AUC 80.61%) shows a better accuracy than InfVal model (AUC 52.07%). The performance of the gully erosion susceptibility map constructed by FR model is greater than that of the map produced by InfVal model. The findings proved that GIS-based bivariate statistical methods such as frequency ratio model could be successfully applied in gully susceptibility mapping in Morocco mountainous regions and in other similar environments. The produced susceptibility map represents a useful tool for sustainable planning, conservation and protection of land from gully processes.  相似文献   

19.
Land subsidence is one of the frequent geological hazards worldwide. Urban areas and agricultural industries are the entities most affected by the consequences of land subsidence. The main objective of this study was to estimate the land subsidence (sinkhole) hazards at the Kinta Valley of Perak, Malaysia, using geographic information system and remote sensing techniques. To start, land subsidence locations were observed by surveying measurements using GPS and using the tabular data, which were produced as coordinates of each sinkhole incident. Various land subsidence conditioning factors were used such as altitude, slope, aspect, lithology, distance from the fault, distance from the river, normalized difference vegetation index, soil type, stream power index, topographic wetness index, and land use/cover. In this article, a data-driven technique of an evidential belief function (EBF), which is in the category of multivariate statistical analysis, was used to map the land subsidence-prone areas. The frequency ratio (FR) was performed as an efficient bivariate statistical analysis method in order compare it with the acquired results from the EBF analysis. The probability maps were acquired and the results of the analysis validated by the area under the (ROC) curve using the testing land subsidence locations. The results indicated that the FR model could produce a 71.16 % prediction rate, while the EBF showed better prediction accuracy with a rate of 73.63 %. Furthermore, the success rate was measured and accuracies of 75.30 and 79.45 % achieved for FR and EBF, respectively. These results can produce an understanding of the nature of land subsidence as well as promulgate public awareness of such geo-hazards to decrease human and economic losses.  相似文献   

20.
This paper aims at mapping the potential groundwater recharge zones in the southern part of Jordan Valley (JV). This area is considered as the most important part for agricultural production in Jordan. The methodology adopted in this study is based on utilizing the open ended SLUGGER-DQL score model, which was developed by Raymond et al (2009). Geographic information systems were used in this study to build up the different layers of this model and to create the potential groundwater recharge zones. Based on the generated SLUGGER-DQL potential map, it was found that about 70.8 % of the investigated area was categorized as high potential for groundwater recharge, 18.7 % as moderate, and 10.5 % as low potential for groundwater recharge. To validate the model results, sensitivity analysis was carried out to assess the influence of each model parameter on the obtained results. Based on this analysis, it was found that the slope parameter (S) is the most sensitive parameter among SLUGGER-DQL model parameters, followed by water level in summer (L), well density (D), water quality (Q), runoff availability (R), land use/land cover, geology (GE), whereas the lowest sensitive parameter is the geology parameter (GE). Moreover, the parameters R, D, and Q show the lowest effective weights. The effective weight for each parameter was found to differ from the assigned theoretical weight by SLUGGER-DQL index model.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号