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1.
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   

2.
Empirical multivariate predictive models represent an important tool to estimate gully erosion susceptibility. Topography, lithology, climate, land use and vegetation cover are commonly used as input for these approaches. In this paper, two multivariate predictive models were generated for two gully erosion processes in San Giorgio basin (Italy) and Mula River basin (Spain) using only topographical attributes as independent variables. Initially, nine models (five for San Giorgio and four for Mula) with pixel sizes ranging from 2 to 50 m were generated, and validation statistics were calculated to estimate the optimal pixel size. The best models were selected based on model performance using the area under the receiver operating characteristic (AUC) curve and the generalized cross-validation. The best pixel size was 4 m in the San Giorgio basin and 20 m in the Mula basin. The finest resolution was not necessarily the best; rather, the relationship between digital elevation model resolution and size of the landform was important. The two selected models showed an excellent performance with AUC values of 0.859 and 0.826 for San Giorgio and Mula, respectively. The Topographic Wetness Index and the general curvature were identified as key topographical attributes in San Giorgio and Mula basins, respectively. Both attributes were related to the processes observed in the field and described in the literature. Finally, maps of gully erosion susceptibility were produced for each basin. These maps showed that 22 and 20 % of San Giorgio and Mula basins, respectively, present favourable conditions for the development of gullies.  相似文献   

3.
This work summarizes the results of a geomorphological and bivariate statistical approach to gully erosion susceptibility mapping in the Turbolo stream catchment (northern Calabria, Italy). An inventory map of gully erosion landforms of the area has been obtained by detailed field survey and air photograph interpretation. Lithology, land use, slope, aspect, plan curvature, stream power index, topographical wetness index and length-slope factor were assumed as gully erosion predisposing factors. In order to estimate and validate gully erosion susceptibility, the mapped gully areas were divided in two groups using a random partitions strategy. One group (training set) was used to prepare the susceptibility map, using a bivariate statistical analysis (Information Value method) in GIS environment, while the second group (validation set) to validate the susceptibility map, using the success and prediction rate curves. The validation results showed satisfactory agreement between the susceptibility map and the existing data on gully areas locations; therefore, over 88% of the gullies of the validation set are correctly classified falling in high and very high susceptibility areas. The susceptibility map, produced using a methodology that is easy to apply and to update, represents a useful tool for sustainable planning, conservation and protection of land from gully processes. Therefore, this methodology can be used to assess gully erosion susceptibility in other areas of Calabria, as well as in other regions, especially in the Mediterranean area, that have similar morphoclimatic features and sensitivity to concentrated erosion.  相似文献   

4.
This work aims to evaluate the predictive capability of three bivariate statistical models, namely information value, frequency ratio, and evidential belief functions, in gully erosion susceptibility mapping in northeastern Maysan Governorate (Ali Al-Gharbi District) in southern Iraq. The gully inventory map, consisting of 21 gullies of different sizes, was prepared based on the interpretation of remotely sensed data supported by field survey. The gully inventory data (polygon format) were randomly partitioned into two sets: 14 gullies for build and training the bivariate model, and the remaining 7 gullies for validating purposes. Twelve gully influential factors were selected based on data availability and the literature review. The selected factors were related to lithology, geomorphology, soil, land cover, and topography (primary and secondary) settings. Analysis of factor importance using information gain ratio proved that out of 12 gully influential factors, eight were of more importance in developing gullies (the average merit was greater than zero). The most important factors and the training gully inventory map were used to generate three gully erosion susceptibility maps based on the three bivariate models used. For validation, the area under the operating characteristics curves for both success and prediction rates was used. The results indicated that the highest prediction rate of 82.9% was achieved using the information value technique. All the bivariate models had prediction rates greater than 80%, and thus they were regarded as very good estimators. The final conclusion was that the bivariate models offer advanced techniques for mapping gully erosion susceptibility.  相似文献   

5.
Toroud Watershed in Semnan Province, Iran is a prone area to gully erosion that causes to soil loss and land degradation. To consider the gully erosion, a comprehensive map of gully erosion susceptibility is required as useful tool for decreasing losses of soil. The purpose of this research is to generate a reliable gully erosion susceptibility map (GESM) using GIS-based models including frequency ratio (FR), weights-of-evidence (WofE), index of entropy (IOE), and their comparison to an expert knowledge-based technique, namely, Analytic Hierarchy Process (AHP). At first, 80 gully locations were identified by extensive field surveys and Google Earth images. Then, 56 (70%) gully locations were randomly selected for modeling process, and the remaining 26 (30%) gully locations were used for validation of four models. For considering geo-environmental factors, VIF and tolerance indices are used and among 18 factors, 13 factors including elevation, slope degree, slope aspect, plan curvature, distance from river, drainage density, distance from road, lithology, land use/land cover, topography wetness index (TWI), stream power index (SPI), normalized difference vegetation index (NDVI), and slope–length (LS) were selected for modeling aims. After preparing GESMs through the mentioned models, final maps divided into five classes including very low, low, moderate, high, and very high susceptibility. The receiver operating characteristic (ROC) curve and the seed cell area index (SCAI) as two validation techniques applied for assessment of the built models. The results showed that the AUC (area under the curve) in training data are 0.973 (97.3%), 0.912 (91.2%), 0.939 (93.9%), and 0.926 (92.6%) for AHP, FR, IOE, and WofE models, respectively. In contrast, the prediction rates (validating data) were 0.954 (95.4%), 0.917 (91.7), 0.925 (92.5%), and 0.921 (92.1%) for above models, respectively. Results of AUC indicated that four model have excellent accuracy in prediction of prone areas to gully erosion. In addition, the SCAI values showed that the produced maps are generally reasonable, because the high and very high susceptibility classes had very low SCAI values. The results of this research can be used in soil conservation plans in the study area.  相似文献   

6.
《地学前缘(英文版)》2020,11(6):2207-2219
This investigation assessed the efficacy of 10 widely used machine learning algorithms (MLA) comprising the least absolute shrinkage and selection operator (LASSO), generalized linear model (GLM), stepwise generalized linear model (SGLM), elastic net (ENET), partial least square (PLS), ridge regression, support vector machine (SVM), classification and regression trees (CART), bagged CART, and random forest (RF) for gully erosion susceptibility mapping (GESM) in Iran. The location of 462 previously existing gully erosion sites were mapped through widespread field investigations, of which 70% (323) and 30% (139) of observations were arbitrarily divided for algorithm calibration and validation. Twelve controlling factors for gully erosion, namely, soil texture, annual mean rainfall, digital elevation model (DEM), drainage density, slope, lithology, topographic wetness index (TWI), distance from rivers, aspect, distance from roads, plan curvature, and profile curvature were ranked in terms of their importance using each MLA. The MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE (root mean square error), MAE (mean absolute error), and R-squared. Based on the comparisons among MLA, the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared, and was therefore selected as the best model. The variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least importance. According to the GESM generated using RF, most of the study area is predicted to have a low (53.72%) or moderate (29.65%) susceptibility to gully erosion, whereas only a small area is identified to have a high (12.56%) or very high (4.07%) susceptibility. The outcome generated by RF model is validated using the ROC (Receiver Operating Characteristics) curve approach, which returned an area under the curve (AUC) of 0.985, proving the excellent forecasting ability of the model. The GESM prepared using the RF algorithm can aid decision-makers in targeting remedial actions for minimizing the damage caused by gully erosion.  相似文献   

7.
The aim of this study is to produce landslide susceptibility mapping by probabilistic likelihood ratio (PLR) and spatial multi-criteria evaluation (SMCE) models based on geographic information system (GIS) in the north of Tehran metropolitan, Iran. The landslide locations in the study area were identified by interpretation of aerial photographs, satellite images, and field surveys. In order to generate the necessary factors for the SMCE approach, remote sensing and GIS integrated techniques were applied in the study area. Conditioning factors such as slope degree, slope aspect, altitude, plan curvature, profile curvature, surface area ratio, topographic position index, topographic wetness index, stream power index, slope length, lithology, land use, normalized difference vegetation index, distance from faults, distance from rivers, distance from roads, and drainage density are used for landslide susceptibility mapping. Of 528 landslide locations, 70 % were used in landslide susceptibility mapping, and the remaining 30 % were used for validation of the maps. Using the above conditioning factors, landslide susceptibility was calculated using SMCE and PLR models, and the results were plotted in ILWIS-GIS. Finally, the two landslide susceptibility maps were validated using receiver operating characteristic curves and seed cell area index methods. The validation results showed that area under the curve for SMCE and PLR models is 76.16 and 80.98 %, respectively. The results obtained in this study also showed that the probabilistic likelihood ratio model performed slightly better than the spatial multi-criteria evaluation. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.  相似文献   

8.
The main goal of this study was to investigate the application of the weights-of-evidence and certainty factor approaches for producing landslide susceptibility maps of a landslide-prone area (Haraz) in Iran. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. The landslide conditioning factors considered for the study area were slope gradient, slope aspect, altitude, lithology, land use, distance from streams, distance from roads, distance from faults, topographic wetness index, stream power index, stream transport index and plan curvature. For validation of the produced landslide susceptibility maps, the results of the analyses were compared with the field-verified landslide locations. Additionally, the receiver operating characteristic curves for all the landslide susceptibility models were constructed and the areas under the curves were calculated. The landslide locations were used to validate results of the landslide susceptibility maps. The verification results showed that the weights-of-evidence model (79.87%) performed better than certainty factor (72.02%) model with a standard error of 0.0663 and 0.0756, respectively. According to the results of the area under curve evaluation, the map produced by weights-of-evidence exhibits satisfactory properties.  相似文献   

9.
The present study investigates a potential application of different resolution topographic data obtained from airborne LiDAR and an integrated ensemble weight-of-evidence and analytic hierarchy process (WoE–AHP) model to spatially predict slope failures. Previously failed slopes of the Pellizzano (Italy) were remotely mapped and divided into two subsets for training and testing purposes. 1, 2, 5, 10, 15, and 20 m topographic data were processed to extract nine terrain attributes identified as conditioning factors for landslides: slope degree, aspect, altitude, plan curvature, profile curvature, stream power index, topographic wetness index, sediment transport index, and topographic roughness index. Landslide (slope failure) susceptibility maps were produced using a single WoE (Model 1), an ensemble WoE–AHP model that used all conditioning factors (Model 2), and an ensemble WoE–AHP model that only used highly nominated conditioning factors (Model 3). The validation results proved the efficiency of high-resolution (≤ 5 m) topographic data and the ensemble model, particularly when all factors were used in the modeling process (Model 2). The average success rates and prediction rates for Model 2 that used ≤ 5 m resolution datasets were 84.26 and 82.78%, respectively. The finding presented in this paper can aid in planning more efficient LiDAR surveys and the handling of large datasets, and in gaining a better understanding of the nature of the predictive models.  相似文献   

10.
Digital terrain modeling is widely used in geological studies. In some cases, orthogonal and diagonal linear patterns appear on maps of local topographic variables. These patterns may be both portrayals of geological structures and artefacts. Some researchers speculated that possible anisotropy of operators of local topographic variables might be a cause of these artefacts. Using a principle for testing derivative operators in image processing, we gave proof to isotropy (rotation invariability) of operators of a majority of local topographic attributes of the complete system of curvatures (i.e., slope gradient, horizontal curvature, vertical curvature, mean curvature, Gaussian curvature, accumulation curvature, ring curvature, unsphericity curvature, difference curvature, minimum curvature, maximum curvature, horizontal excess curvature, and vertical excess curvature). Rotating an elevation function about z-axis and then applying these operators cannot lead to variations in both values of the topographic variables and patterns in their maps, comparing with results of applying these operators to an unrotated elevation function. This demonstrates that linear artefacts with preferable directions in maps of the topographic attributes specified cannot be caused by intrinsic properties of their operators. Other possible sources for false linear patterns in maps of topographic variables are briefly discussed: (a) errors in the compilation of digital elevation models (DEMs), (b) grid geometry of digital terrain models (DTMs), (c) errors in DEM interpolation, (d) imperfection of algorithms for DTM derivation, and (e) aliasing errors.  相似文献   

11.
The logistic regression and statistical index models are applied and verified for landslide susceptibility mapping in Daguan County, Yunnan Province, China, by means of the geographic information system (GIS). A detailed landslide inventory map was prepared by literatures, aerial photographs, and supported by field works. Fifteen landslide-conditioning factors were considered: slope angle, slope aspect, curvature, plan curvature, profile curvature, altitude, STI, SPI, and TWI were derived from digital elevation model; NDVI was extracted from Landsat ETM7; rainfall was obtained from local rainfall data; distance to faults, distance to roads, and distance to rivers were created from a 1:25,000 scale topographic map; the lithology was extracted from geological map. Using these factors, the landslide susceptibility maps were prepared by LR and SI models. The accuracy of the results was verified by using existing landslide locations. The statistical index model had a predictive rate of 81.02%, which is more accurate prediction in comparison with logistic regression model (80.29%). The models can be used to land-use planning in the study area.  相似文献   

12.
In the Three Gorges of China, there are frequent landslides, and the potential risk of landslides is tremendous. An efficient and accurate method of generating landslide susceptibility maps is very important to mitigate the loss of lives and properties caused by these landslides. This paper presents landslide susceptibility mapping on the Zigui-Badong of the Three Gorges, using rough sets and back-propagation neural networks (BPNNs). Landslide locations were obtained from a landslide inventory map, supported by field surveys. Twenty-two landslide-related factors were extracted from the 1:10,000-scale topographic maps, 1:50,000-scale geological maps, Landsat ETM + satellite images with a spatial resolution of 28.5 m, and HJ-A satellite images with a spatial resolution of 30 m. Twelve key environmental factors were selected as independent variables using the rough set and correlation coefficient analysis, including elevation, slope, profile curvature, catchment aspect, catchment height, distance from drainage, engineering rock group, distance from faults, slope structure, land cover, topographic wetness index, and normalized difference vegetation index. The initial, three-layered, and four-layered BPNN were trained and then used to map landslide susceptibility, respectively. To evaluate the models, the susceptibility maps were validated by comparing with the existing landslide locations according to the area under the curve. The four-layered BPNN outperforms the other two models with the best accuracy of 91.53 %. Approximately 91.37 % of landslides were classified as high and very high landslide-prone areas. The validation results show sufficient agreement between the obtained susceptibility maps and the existing landslide locations.  相似文献   

13.
坡面不同侵蚀沟断面特征及水力几何形态   总被引：3，自引：0，他引：3       下载免费PDF全文
通过整理和分析国内外大量野外调查数据,系统研究了裸露坡耕地壤土土质不同侵蚀沟的断面特征和水力几何形态,并给出了相应的判别标准。结果表明:随坡面侵蚀沟逐步发展,宽深比参数ζ逐渐减小,细沟、浅沟、切沟和冲沟适宜的ζ范围分别为5~17、2~5、0.4~1.8和0.1~0.6;同一侵蚀沟条件下,壤土类型对ζ值也有影响。土壤质地越细,ζ值越小。不同侵蚀沟水力几何形态指数也不相同,细沟的形态指数β1、β2和β3分别为0.3、0.4和0.3,浅沟和切沟为0.4、0.4和0.2,冲沟为0.5、0.4和0.1。验证结果表明给出的判别标准可以用来界定不同侵蚀沟类型。  相似文献   

14.
The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model(DEM)data.The unique terrain characteristics of a particular landscape are derived from DEM,which are responsible for initiation and development of ephemeral gullies.As the topographic features of an area significantly influences on the erosive power of the water flow,it is an important task the extraction of terrain features from DEM to properly research gully erosion.Alongside,topography is highly correlated with other geo-environmental factors i.e.geology,climate,soil types,vegetation density and floristic composition,runoff generation,which ultimately influences on gully occurrences.Therefore,terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility(GES)mapping.In this study,remote sensing-Geographic information system(GIS)tech-niques coupled with machine learning(ML)methods has been used for GES mapping in the parts of Semnan province,Iran.Current research focuses on the comparison of predicted GES result by using three types of DEM i.e.Advanced Land Observation satellite(ALOS),ALOS World 3D-30 m(AW3D30)and Advanced Space borne Thermal Emission and Reflection Radiometer(ASTER)in different resolutions.For further progress of our research work,here we have used thirteen suitable geo-environmental gully erosion conditioning factors(GECFs)based on the multi-collinearity analysis.ML methods of conditional inference forests(Cforest),Cubist model and Elastic net model have been chosen for modelling GES accordingly.Variable's importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods(Cforest=21.4,Cubist=19.65 and Elastic net=17.08),followed by lithology and slope.Validation of the model's result was performed through area under curve(AUC)and other statistical indices.The validation result of AUC has shown that Cforest is the most appropriate model for predicting the GES assessment in three different DEMs(AUC value of Cforest in ALOS DEM is 0.994,AW3D30 DEM is 0.989 and ASTER DEM is 0.982)used in this study,followed by elastic net and cubist model.The output result of GES maps will be used by decision-makers for sustainable development of degraded land in this study area.  相似文献   

15.
Soil erosion by water is a significant problem in arid and semi-arid areas of large parts of Iran. Water erosion is one of the most effective phenomena that leads to decreasing soil productivity and pollution of water resources; especially, in the Mazayjan watershed in the southwest of Fars Province gully erosion contributes to the sediment dynamics in a significant way. Consequently, the intention of this research is to identify the different types of soil erosion processes acting in the area and to assess the process dynamics in an integrative way. Therefore, we applied GIS and satellite image analysis techniques to derive input information for the numeric models. For sheet and rill erosion the Unit Stream Power-based Erosion Deposition Model (USPED) was utilized. The spatial distribution of gully erosion was assessed using a statistical approach, which used three variables (stream power index, slope, and flow accumulation) to predict the spatial distribution of gullies in the study area. The eroded gully volumes were estimated for a 7-year period by fieldwork and Google Earth high-resolution images. Finally the gully retreat rates were integrated into the USPED model. The results show that the integration of the SPI approach to quantify gully erosion with the USPED model is a suitable method to qualitatively and quantitatively assess water erosion processes. The application of GIS and stochastic model approaches to spatialize the USPED model input yields valuable results for the prediction of soil erosion in the Mazayjan catchment. The results of this research help to develop an appropriate management of soil and water resources in the southwestern parts of Iran.  相似文献   

16.
The aim of the research was to verify and compare the predictive power of different diagnostic areas in assessing landslide susceptibility with a multivariate approach. Scarps, landslide areas (the union between scarp and accumulation zones) and areas uphill from crowns, for rotational slides, source or scarp areas and landslide areas, for flows, have been tested. A multivariate approach was applied to assess the landslide susceptibility on the basis of three selected conditioning factors (lithology, slope angle, and topographic wetness index), which were combined in a Unique Condition Unit (UCU) layer. By intersecting the UCU layer with the vector layer of the diagnostic areas, landslide susceptibility models were produced, in which the susceptibility is assigned to each UCUs on the basis of the computed density function. In order to test the effects produced by selecting different diagnostic areas in the performance of the susceptibility models, validation procedures have been applied to evaluate and compare the performances of the derived predictive models. The validation results are estimated by comparing the prediction and the success rate curves, exploiting three morphometric indexes. A test area, the Guddemi river basin, was selected in the northern Sicilian Apennines chain, having a total area of nearly 25 km2 and being mainly characterized by the outcropping of clays, calcilutites, and marly limestones. Aerial analysis, integrated with a field survey, resulted in the recognition of 111 earth-flow and 145 earth-rotational slide landslides. Scarps, for rotational slides, and both source and landslide areas, for flows, produced very satisfactory validation results. For rotational slides, areas uphill from crowns and landslide areas are both responsible for lower predictive performances, characterized by validation curves close to being flat shaped, due to their incapability of identifying specific slope (UCU) conditions. Moreover, because of their limited size, the areas uphill from crowns seem to suffer from a relevant geostatistical “instability”, when a splitting is performed to produce the validation domains, so that an enhanced shift between success and prediction rate curves is produced. By comparing the relative susceptibility maps, the research allowed us to evaluate the key role played by the selection of the diagnostic areas; the validation of the models is proposed as a tool to quantify such differences in terms of predictive performance.  相似文献   

17.
Gully erosion is a very serious problem in the black soil region of northeast China. Gully filling is often adopted for controlling gully erosion by local farmers and thus causes more serious soil erosion. In this study an ephemeral gully (EG, 74 m) and a classical gully (CG, 52 m) connected at the gully’s headcut were selected as the study site. Two comparisons were made to explore the effects of gully erosion and the subsequent gully filling on soil depth and soybean yield: (1) soil depth between 81 sample points in the study site and 11 reference points along the same slope with the gully; (2) soybean yield between 81 sample points in the study site and 30 baseline locations near the study site. The results indicated that gully erosion caused the reduction of soil depth and soybean yield. Although filling gullies with soil from adjacent areas seemed to be an expedient way to remediate the gullies, it resulted in substantial soybean yield reduction. Gully erosion reduced the soil depth and soybean yield in 74.4 and 83.9 % of the study site, respectively. The soybean yield reduction ratio was 34.5 % for the whole study site and 2.6 % for the black soil region. Soil depth was the most important soil property indicator to reduce yield. Every 1 cm decrease in soil depth in the areas adjacent to gullies due to infilling activities resulted in a 2 % decrease in yield. More significant was the deposition of sediment from gully erosion, which completely eliminated soybean yield. Currently, effective soil and water conservation measures are not known and adopted by local farmers extensively. In the future, once some measures for preventing soil erosion, in particular gully erosion, were proved effective, these technologies should be disseminated among local farmers.  相似文献   

18.
The main purpose of this study is to define the main variables that contribute to the occurrence of landslides in Kimi, Euboea, Greece, and to produce a landslide susceptibility map using the weight of evidence method. For the developed model, a sensitivity analysis is carried out in order to understand the model’s behavior when small changes are introduced in the weight value of the landslide-related variables. Landslide locations were identified from field surveys and interpretation of aerial photographs which resulted in the construction of an inventory map with 132 landslide events, while eight contributing variables were identified and exploited. All landslide-related variables were converted into a 5?×?5-m float-type raster file. These input-raster layers included a lithological unit layer, an elevation layer, a slope angle layer, a slope aspect layer, a distance from tectonic features layer, a distance from hydrographic network layer, a topographic wetness index layer, and a curvature layer. The validation of the developed model was achieved by using a subset of unprocessed landslide data, showing a satisfactory agreement between the expected and existing landslide susceptibility level, with the area under the predictive rate curve estimated to be 0.808. The area under the success rate curve was estimated to be 0.828 indicating a very high classification rate for existing landslide areas. According to the results of the sensitivity analysis, the lithological unit “yellowish gray to white marls” was the most sensitive as it had the highest change in the relative frequency of observed landslides. The overall outcomes of the performed analysis provide crucial knowledge in successful land use planning and management practice and also in risk reduction projects.  相似文献   

19.
Aykut Akgun 《Landslides》2012,9(1):93-106
The main purpose of this study is to compare the use of logistic regression, multi-criteria decision analysis, and a likelihood ratio model to map landslide susceptibility in and around the city of İzmir in western Turkey. Parameters, such as lithology, slope gradient, slope aspect, faults, drainage lines, and roads, were considered. Landslide susceptibility maps were produced using each of the three methods and then compared and validated. Before the modeling and validation, the observed landslides were separated into two groups. The first group was for training, and the other group was for validation steps. The accuracy of models was measured by fitting them to a validation set of observed landslides. For validation process, the area under curvature (AUC) approach was applied. According to the AUC values of 0.810, 0.764, and 0.710 for logistic regression, likelihood ratio, and multi-criteria decision analysis, respectively, logistic regression was determined to be the most accurate method among the other used landslide susceptibility mapping methods. Based on these results, logistic regression and likelihood ratio models can be used to mitigate hazards related to landslides and to aid in land-use planning.  相似文献   

20.
Soil erosion is considered as the most widespread form of soil degradation which causes serious environmental problems. This study investigates the performance of the maximum entropy (ME) in mapping rill erosion susceptibility in the Golgol watershed, Ilam province, Iran. To this end, ten rill erosion conditioning factors were selected to be employed in the modelling process based on an investigation of the literature. These layers are: elevation, slope percent, aspect, stream power index, topographic wetness index, distance from streams, plan curvature, lithology, land use, and soil. Then, a training dataset of rill erosion locations was used for modelling this phenomenon. The area under receiver operating characteristics curve was used for evaluating the performance of the ME model. In addition, Modified Pacific South-West Inter Agency Committee (MPSIAC) framework was applied and sediment yield was determined for different hydrological units in the study area. At last, Jackknife test was implemented to show the contribution of the factors in the modelling process. The results depicted that area under ROC curve for training and validation datasets were 0.867, and 0.794, respectively. Therefore, this conclusion can be achieved that ME worked well and could be a good tool for generating rill erosion susceptibility maps and its output could be employed for soil conservation in similar areas.  相似文献   

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