Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study

Four statistical techniques for modelling landslide susceptibility were compared: multiple logistic regression (MLR), multivariate adaptive regression splines (MARS), classification and regression trees (CART), and maximum entropy (MAXENT). According to the literature, MARS and MAXENT have never been used in landslide susceptibility modelling, and CART has been used only twice. Twenty independent variables were used as predictors, including lithology as a categorical variable. Two sets of random samples were used, for a total of 90 model replicates (with and without lithology, and with different proportions of positive and negative data). The model performance was evaluated using the area under the receiver operating characteristic curve (AUC) statistic. The main results are (a) the inclusion of lithology improves the model performance; (b) the best AUC values for single models are MLR (0.76), MARS (0.76), CART (0.77), and MAXENT (0.78); (c) a smaller amount of negative data provides better results; (d) the models with the highest prediction capability are obtained with MAXENT and CART; and (e) the combination of different models is a way to evaluate the model reliability. We further discuss some key issues in landslide modelling, including the influence of the various methods that we used, the sample size, and the random replicate procedures.     

Large-scale natural disaster risk scenario analysis: a case study of Wenzhou City, China

Based on the analysis and calculation of the hazard intensity of typhoon rainstorms and floods as well as the vulnerability of flood receptors and the possibility of great losses, risk scenarios are proposed and presented in Wenzhou City, Zhejiang Province, China, using the Pearson-III model and ArcGIS spatial analyst tools. Results indicate that the elements of risk scenarios include time–space scenarios, disaster scenarios, and man-made scenarios. Ten-year and 100-year typhoon rainstorms and flood hazard areas are mainly concentrated in the coastal areas of Wenzhou City. The average rainfall across a 100-year frequency is 450 mm. The extreme water depth of a 100-year flood is 600 mm. High-vulnerability areas are located in Yueqing, Pingyang, Cangnan, and Wencheng counties. The average loss rate of a 100-year flood is more than 50%. The greatest possible loss of floods shows an obvious concentration-diffusion situation. There is an area of about 20–25% flood loss of 6–24 million Yuan RMB/km² in the Lucheng, Longwan and Ouhai districts. The average loss of a 100-year flood is 12 million Yuan RMB/km², and extreme loss reaches 49.33 million Yuan RMB/km². The classification of risk scenario may be used for the choice of risk response priorities. For the next 50 years, the 10-year typhoon rainstorm-flood disaster is the biggest risk scenario faced by most regions of Wenzhou City. For the Yueqing, Ruian, and Ouhai districts, it is best to cope with a 100-year disaster risk scenario and the accompanying losses.     

城市洪涝模拟应用场景及相应技术策略

城市洪涝模拟是当前国内外城市防洪减灾领域研究的热点。现有城市洪涝模拟方面的评述,主要依据城市洪涝过程或模拟计算方法进行分类讨论,缺乏基于应用需求的视角。随着应用需求日益深入,城市洪涝模拟应用场景日趋多样化和复杂化,不同模拟应用场景下,所关注的洪涝过程不同,采用的技术策略及其重点和难点也不同,脱离模拟应用场景很难辨析这些不同。依据模拟对象和关注变量,归纳总结出城市洪涝模拟的3种典型应用场景,即城市外洪模拟、城市雨洪模拟、城市内涝模拟;针对这3种典型模拟应用场景,分析相应的城市洪水演进模型、城市雨洪模型、半分布式暴雨内涝模型、全分布式暴雨内涝模型等4类模拟技术策略;辨析在不同模拟应用场景和技术策略下,不同模拟技术的组合方式及其特点与难点,以期从应用需求的角度对城市洪涝模拟技术进行全面的梳理,为城市洪涝模拟应用和研究提供一个新的视角。     

Flash flood susceptibility mapping using a novel deep learning model based on deep belief network,back propagation and genetic algorithm

Flash floods are responsible for loss of life and considerable property damage in many countries.Flood susceptibility maps contribute to flood risk reduction in areas that are prone to this hazard if appropriately used by landuse planners and emergency managers.The main objective of this study is to prepare an accurate flood susceptibility map for the Haraz watershed in Iran using a novel modeling approach(DBPGA) based on Deep Belief Network(DBN) with Back Propagation(BP) algorithm optimized by the Genetic Algorithm(GA).For this task, a database comprising ten conditioning factors and 194 flood locations was created using the One-R Attribute Evaluation(ORAE) technique.Various well-known machine learning and optimization algorithms were used as benchmarks to compare the prediction accuracy of the proposed model.Statistical metrics include sensitivity,specificity accuracy, root mean square error(RMSE), and area under the receiver operatic characteristic curve(AUC) were used to assess the validity of the proposed model.The result shows that the proposed model has the highest goodness-of-fit(AUC = 0.989) and prediction accuracy(AUC = 0.985), and based on the validation dataset it outperforms benchmark models including LR(0.885), LMT(0.934), BLR(0.936), ADT(0.976), NBT(0.974), REPTree(0.811), ANFIS-BAT(0.944), ANFIS-CA(0.921), ANFIS-IWO(0.939), ANFIS-ICA(0.947), and ANFIS-FA(0.917).We conclude that the DBPGA model is an excellent alternative tool for predicting flash flood susceptibility for other regions prone to flash floods.     

Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management?

Natural hazards are often studied in isolation.However,there is a great need to examine hazards holistically to better manage the complex of threats found in any region.Many regions of the world have complex hazard landscapes wherein risk from individual and/or multiple extreme events is omnipresent.Extensive parts of Iran experience a complex array of natural hazards-floods,earthquakes,landslides,forest fires,subsidence,and drought.The effectiveness of risk mitigation is in part a function of whether the complex of hazards can be collectively considered,visualized,and evaluated.This study develops and tests individual and collective multihazard risk maps for floods,landslides,and forest fires to visualize the spatial distribution of risk in Fars Province,southern Iran.To do this,two well-known machine-learning algorithms-SVM and MARS-are used to predict the distribution of these events.Past floods,landslides,and forest fires were surveyed and mapped.The locations of occurrence of these events(individually and collectively) were randomly separated into training(70%) and testing(30%) data sets.The conditioning factors(for floods,landslides,and forest fires) employed to model the risk distributions are aspect,elevation,drainage density,distance from faults,geology,LULC,profile curvature,annual mean rainfall,plan curvature,distance from man-made residential structures,distance from nearest river,distance from nearest road,slope gradient,soil types,mean annual temperature,and TWI.The outputs of the two models were assessed using receiver-operating-characteristic(ROC) curves,true-skill statistics(TSS),and the correlation and deviance values from each models for each hazard.The areas-under-the-curves(AUC) for the MARS model prediction were 76.0%,91.2%,and 90.1% for floods,landslides,and forest fires,respectively.Similarly,the AUCs for the SVM model were 75.5%,89.0%,and 91.5%.The TSS reveals that the MARS model was better able to predict landslide risk,but was less able to predict flood-risk patterns and forest-fire risk.Finally,the combination of flood,forest fire,and landslide risk maps yielded a multi-hazard susceptibility map for the province.The better predictive model indicated that 52.3% of the province was at-risk for at least one of these hazards.This multi-hazard map may yield valuable insight for land-use planning,sustainable development of infrastructure,and also integrated watershed management in Fars Province.     

气候变化情景下北江飞来峡水库极端入库洪水预估

以北江飞来峡水库上游为研究对象,构建了网格分辨率为0.25°×0.25°的VIC(Variable Infiltration Capacity)水文模型,应用CMIP5多模式输出的降尺度结果与VIC模型耦合,对RCP2.6、RCP4.5和RCP8.5情景下未来时期(2020-2050年)飞来峡水库的入库洪水进行预估,并根据IPCC第5次评估报告处理和表达不确定性的方法来描述预估结论的可信度。结果表明,2020-2050年飞来峡水库年最大洪峰流量和年最大7日、15日洪量在RCP2.6情景下"大约可能"呈增加趋势,在RCP4.5和RCP8.5情景下"较为可能"呈增加趋势,水库防洪安全风险增大。与历史时期(1970-2000年)相比,未来水库极端入库洪水增加的可能性从大到小依次为RCP4.5、RCP2.6和RCP8.5情景,其中设计洪水100年、50年和20年一遇的洪峰流量在3种排放情景下均呈上升趋势,100年、50年和20年一遇的最大7日、15日洪量在RCP4.5情景下以上升为主,而在RCP2.6和RCP8.5情景下则主要呈减少态势。     

Spatio-temporal dynamics in the flood exposure due to land use changes in the Alpine Lech Valley in Tyrol (Austria)

Flood risk is expected to increase in many regions of the world in the next decades with rising flood losses as a consequence. First and foremost, it can be attributed to the expansion of settlement and industrial areas into flood plains and the resulting accumulation of assets. For a future-oriented and a more robust flood risk management, it is therefore of importance not only to estimate potential impacts of climate change on the flood hazard, but also to analyze the spatio-temporal dynamics of flood exposure due to land use changes. In this study, carried out in the Alpine Lech Valley in Tyrol (Austria), various land use scenarios until 2030 were developed by means of a spatially explicit land use model, national spatial planning scenarios and current spatial policies. The combination of the simulated land use patterns with different inundation scenarios enabled us to derive statements about possible future changes in flood-exposed built-up areas. The results indicate that the potential assets at risk depend very much on the selected socioeconomic scenario. The important conditions affecting the potential assets at risk that differ between the scenarios are the demand for new built-up areas as well as on the types of conversions allowed to provide the necessary areas at certain locations. The range of potential changes in flood-exposed residential areas varies from no further change in the most moderate scenario ‘Overall Risk’ to 119 % increase in the most extreme scenario ‘Overall Growth’ (under current spatial policy) and 159 % increase when disregarding current building restrictions.     

Multi-criteria decision based geospatial mapping of flood susceptibility and temporal hydro-geomorphic changes in the Subarnarekha basin,India

The Subarnarekha River in east India experiences frequent high magnitude flooding in monsoon season.In this study, we present an in-depth analysis of flood hydrology and GIS-based flood susceptibility mapping of the entire catchment. About 40 years of annual peak discharge data, historical cross-sections of different gauging sites, and 12 flood conditioning factors were considered. Our flood susceptibility mapping followed an expert knowledge-based multi-parametric analytical hierarchy process(AHP) and optimized AHP-VIP methods. Peak hydrology data indicated more than 5 times higher discharge contrasted with the mean streamflow of the peak monsoon month in all hydro-monitoring stations that correspond to possible overbank flooding in the shallow semi-alluvial reaches of the Subarnarekha River. Widthdepth ratio revealed continuous changes on the channel cross-sections at decadal scale in all gauging sites. Predicted flood susceptibility map through optimized AHP-VIP method showed a great amount of areas(38%) have a high probability of flooding and demands earnest attention of administrative bodies.The AHP-VIP based flood susceptibility map was theoritically validated through AUC approach and it showed fairly high accuracy(AUC = 0.93). Our study offers an exceptionally cost and time effective solution to the flooding issues in the Subarnarekha basin.     

Integrated GIS and remote sensing for mapping groundwater potentiality in the Tulul al Ashaqif,Northeast Jordan

Jordan with its limited water resources is currently classified as one of the four water-poor countries worldwide. This study was initiated to explore groundwater potential areas in Tulul al Ashaqif area, Jordan, by integrating remote sensing, geographic information systems (GIS), and multicriteria evaluation techniques. Eight thematic layers were built in a GIS and assigned using multicriteria evaluation techniques suitable weights and ratings regarding their relative contribution in groundwater occurrence. These layers include lithology, geomorphology, lineaments density, drainage density, soil texture, rainfall, elevation, and slope. The final groundwater potentiality map generated by GIS consists of five groundwater potentiality classes: very high, high, moderate, low, and very low. The map showed that the study area is generally of moderate groundwater potentiality (76.35 %). The very high and high potential classes occupy 2.2 and 12.75 % of study area, respectively. The validity of results of this GIS-based model was carried out by superimposing existing hand dug wells on the final map. The single parameter sensitivity test was conducted to assess the influence of the assigned weights on the groundwater potential model, and new effective weights were derived. The resulted groundwater potentiality map showed that the area occupied by each of the groundwater potentiality classes has changed. However, the study area remains generally of moderate groundwater potentiality (70.93 % of the study area). The area occupied by the very high and high potential classes comprises 4.53 and 18.56 % of the study area, respectively.     

Coastal inundation and damage exposure estimation: a case study for Jakarta

Coastal flooding poses serious threats to coastal areas, and the vulnerability of coastal communities and economic sectors to flooding will increase in the coming decades due to environmental and socioeconomic changes. It is increasingly recognised that estimates of the vulnerability of cities are essential for planning adaptation measures. Jakarta is a case in point, since parts of the city are subjected to regular flooding on a near-monthly basis. In order to assess the current and future coastal flood hazard, we set up a GIS-based flood model of northern Jakarta to simulate inundated area and value of exposed assets. Under current conditions, estimated damage exposure to extreme coastal flood events with return periods of 100 and 1,000 years is high (€4.0 and €5.2 billion, respectively). Under the scenario for 2100, damage exposure associated with these events increases by a factor 4–5, with little difference between low/high sea-level rise scenarios. This increase is mainly due to rapid land subsidence and excludes socioeconomic developments. We also develop a detemporalised inundation scenario for assessing impacts associated with any coastal flood scenario. This allows for the identification of critical points above which large increases in damage exposure can be expected and also for the assessment of adaptation options against hypothetical user-defined levels of change, rather than being bound to a discrete set of a priori scenarios. The study highlights the need for urgent attention to the land subsidence problem; a continuation of the current rate would result in catastrophic increases in damage exposure.     

Groundwater potentiality mapping in the Sinai Peninsula, Egypt, using remote sensing and GIS-watershed-based modeling

Systematic planning for groundwater exploration using modern techniques is essential for the proper utilization, protection and management of this vital resource. Enhanced Thematic Mapper Plus (ETM+) images, a geographic information system (GIS), a watershed modeling system (WMS) and weighted spatial probability modeling (WSPM) were integrated to identify the groundwater potential areas in the Sinai Peninsula, Egypt. Eight pertinent thematic layers were built in a GIS and assigned appropriate rankings. Layers considered were: rainfall, net groundwater recharge, lithology or infiltration, lineament density, slope, drainage density, depth to groundwater, and water quality. All these themes were assigned weights according to their relative importance to groundwater potentiality and their corresponding normalized weights were obtained based on their effectiveness factors. The groundwater potentiality map was finally produced by WSPM. This map comprises five gradational groundwater potentiality classes ranging from very high to very low. The validity of this unbiased GIS-based model was tested by correlating its results with the published hydrogeological map of Egypt and the actual borehole yields, where a concordant justification was reached. The map declared that the Sinai Peninsula is generally of moderate groundwater potentiality, where this class encompasses an area of 33,120?km² which represents 52% of its total area.     

Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin,Asir Region,Saudi Arabia

The current study aimed at evaluating the capabilities of seven advanced machine learning techniques(MLTs),including,Support Vector Machine(SVM),Random Forest(RF),Multivariate Adaptive Regression Spline(MARS),Artificial Neural Network(ANN),Quadratic Discriminant Analysis(QDA),Linear Discriminant Analysis(LDA),and Naive Bayes(NB),for landslide susceptibility modeling and comparison of their performances.Coupling machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important issue.This study was carried out using GIS and R open source software at Abha Basin,Asir Region,Saudi Arabia.First,a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data sources.All the landslide areas were randomly separated into two groups with a ratio of 70%for training and 30%for validating purposes.Twelve landslide-variables were generated for landslide susceptibility modeling,which include altitude,lithology,distance to faults,normalized difference vegetation index(NDVI),landuse/landcover(LULC),distance to roads,slope angle,distance to streams,profile curvature,plan curvature,slope length(LS),and slope-aspect.The area under curve(AUC-ROC)approach has been applied to evaluate,validate,and compare the MLTs performance.The results indicated that AUC values for seven MLTs range from 89.0%for QDA to 95.1%for RF.Our findings showed that the RF(AUC=95.1%)and LDA(AUC=941.7%)have produced the best performances in comparison to other MLTs.The outcome of this study and the landslide susceptibility maps would be useful for environmental protection.     

Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO,BAT and COA algorithms

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

Improved flood susceptibility mapping using a best first decision tree integrated with ensemble learning techniques

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

A hybrid multiple criteria decision-making model for the sustainable management of aquifers

In this study, a hybrid multiple criteria decision-making (HMCDM) model was proposed for prioritizing scenarios for managing groundwater use from an aquifer. Three scenarios, including the construction of subsurface dams, the use of artificial recharge and reducing groundwater use by 5% and 10% were considered to assess the most sustainable development approach. The examined MCDM models were: simple additive weighting (SAW); and MTAHP which is a hybridization of the modified TOPSIS and the analytic hierarchy process models. The criteria proposed for determining the order preference of the scenarios included the sustainable development index (I_U) and a modified water exploitation index as well as economic, social and environmental indices. To assess the technical and economic impacts of the management scenarios, modeling of the aquifer was simulated for a 3-year period using these scenarios. The results of the assessment indicated that the scenario of water withdrawal reduction by 10% was the best scenario determined in MTAHP followed by a reduction in groundwater withdrawal by 5%, the use of artificial recharge and the construction of a subsurface dam, respectively. The difference between the results of MTAHP and SAW models was in their first and third ranks, in such a way artificial recharge scored the first rank in SAW model and the third rank in MTAHP model, also withdrawal reduction by 10% scored third rank in SAW model and first rank in MTAHP model. The results of these two models have demonstrated that the construction of a subsurface dam in Shahrekord aquifer is not an appropriate management option. According to the results of this study, MTAHP models can be applied for ranking feasible management scenarios in aquifers using the redefined sustainable development and modified groundwater exploitation indices introduced in this study.     

Evaluating the impact of management scenarios and land use changes on annual surface runoff and sediment yield using the GeoWEPP: a case study from the Lighvanchai watershed,Iran

This study was undertaken to evaluate land use change impact and management scenarios on annual average surface runoff (SR) and sediment yield (SY) using the GeoWEPP tool in the Lighvanchai watershed (located in northwestern Iran). Following a sensitivity analysis, the WEPP model was calibrated (2005–2007) and validated (2008–2010) against monthly observed SY and SR. The coefficient of determination (R ²), Nash–Sutcliffe efficiency (NSE), mean bias error (MBE), and root-mean-square error (RMSE) were applied to quantitatively evaluate the WEPP model. The results indicate a satisfactory model performance with R ² > 0.80 and NSE > 0.60. Therefore, the model for current land use (scenario 1) was run for a 30-year time period (1982–2011). The annual average of SR and sediment load were predicted as 93,584 m³/year and 4340 ton/year, respectively. To reduce the annual average surface runoff and sediment yield at the watershed scale, the second scenario (alfalfa cultivation with suitable tillage) and the third scenario (grassland development) as two management scenarios of land use changes were defined by identifying the critical hillslopes. The rate of SR and sediment load in the second scenario were 42,096 m³/year and 429 ton/year, respectively. For the third scenario, the model predictions were 30,239 m³/year and 226 ton/year, respectively. Compared to the first scenario, the reduction rates in annual average of sediment load were about 90 and 94%, respectively. Moreover, for the second and third management scenarios, the reduction rates in annual average of SR were about 55 and 67%, respectively.     

不同水资源情景下干旱区未来土地利用/覆盖变化模拟——以黑河中上游张掖市为例

应用回归分析方法确定对研究区土地利用/覆盖变化有重要贡献的10种驱动因子,用线性规划方法确定模型输入文件中模拟期间每年的各种土地利用类型面积.用ArcView空间分析的方法建立驱动力文件,利用SPSS13.0软件分析每种驱动力的权重(β值),建立Logistic方程,并将其作为模型输入文件.建立可利用水资源总量分别为18.0×10⁸m³,26.5×10⁸m³,35.0×10⁸m³的3种情景假设,应用CLUE-S模型模拟张掖市2001-2020年土地利用/覆盖变化变化.模拟结果显示:1)三种情景下,耕地的面积都在减少,耕地面积的减少量与可利用水资源总量呈负相关;2)林草地面积在3种情景下均增加,林草地面积的变化与可利用水资源总量呈正相关;3)在3种情景假设下,水域面积的变化都不明显;4)城镇用地面积变化与水量也呈正相关;5)未利用地面积持续减少.     

Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling�CNarayanghat road section in Nepal Himalaya

Landslide susceptibility maps are vital for disaster management and for planning development activities in the mountainous country like Nepal. In the present study, landslide susceptibility assessment of Mugling?CNarayanghat road and its surrounding area is made using bivariate (certainty factor and index of entropy) and multivariate (logistic regression) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field survey. As a result, 321 landslides were mapped and out of which 241 (75?%) were randomly selected for building landslide susceptibility models, while the remaining 80 (25?%) were used for validating the models. The effectiveness of landslide susceptibility assessment using GIS and statistics is based on appropriate selection of the factors which play a dominant role in slope stability. In this case study, the following landslide conditioning factors were evaluated: slope gradient; slope aspect; altitude; plan curvature; lithology; land use; distance from faults, rivers and roads; topographic wetness index; stream power index; and sediment transport index. These factors were prepared from topographic map, drainage map, road map, and the geological map. Finally, the validation of landslide susceptibility map was carried out using receiver operating characteristic (ROC) curves. The ROC plot estimation results showed that the susceptibility map using index of entropy model with AUC value of 0.9016 has highest prediction accuracy of 90.16?%. Similarly, the susceptibility maps produced using logistic regression model and certainty factor model showed 86.29 and 83.57?% of prediction accuracy, respectively. Furthermore, the ROC plot showed that the success rate of all the three models performed more than 80?% accuracy (i.e. 89.15?% for IOE model, 89.10?% for LR model and 87.21?% for CF model). Hence, it is concluded that all the models employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of Mugling?CNarayanghat road section. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.     

Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances

The current research presents a detailed landslide susceptibility mapping study by binary logistic regression, analytical hierarchy process, and statistical index models and an assessment of their performances. The study area covers the north of Tehran metropolitan, Iran. When conducting the study, in the first stage, a landslide inventory map with a total of 528 landslide locations was compiled from various sources such as aerial photographs, satellite images, and field surveys. Then, the landslide inventory was randomly split into a testing dataset 70 % (370 landslide locations) for training the models, and the remaining 30 % (158 landslides locations) was used for validation purpose. Twelve landslide conditioning factors such as slope degree, slope aspect, altitude, plan curvature, normalized difference vegetation index, land use, lithology, distance from rivers, distance from roads, distance from faults, stream power index, and slope-length were considered during the present study. Subsequently, landslide susceptibility maps were produced using binary logistic regression (BLR), analytical hierarchy process (AHP), and statistical index (SI) models in ArcGIS. The validation dataset, which was not used in the modeling process, was considered to validate the landslide susceptibility maps using the receiver operating characteristic curves and frequency ratio plot. The validation results showed that the area under the curve (AUC) for three mentioned models vary from 0.7570 to 0.8520 $ ({\text{AUC}}_{\text{AHP}} = 75.70\;\% ,\;{\text{AUC}}_{\text{SI}} = 80.37\;\% ,\;{\text{and}}\;{\text{AUC}}_{\text{BLR}} = 85.20\;\% ) $ ( AUC AHP = 75.70 % , AUC SI = 80.37 % , and AUC BLR = 85.20 % ) . Also, plot of the frequency ratio for the four landslide susceptibility classes of the three landslide susceptibility models was validated our results. Hence, it is concluded that the binary logistic regression model employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of study area. Meanwhile, the results obtained in this study also showed that the statistical index model can be used as a simple tool in the assessment of landslide susceptibility when a sufficient number of data are obtained.     

Mapping landslide susceptibility with frequency ratio,statistical index,and weights of evidence models: a case study in northern Iran

The main objective of this study is to investigate potential application of frequency ratio (FR), weights of evidence (WoE), and statistical index (SI) models for landslide susceptibility mapping in a part of Mazandaran Province, Iran. First, a landslide inventory map was constructed from various sources. The landslide inventory map was then randomly divided in a ratio of 70/30 for training and validation of the models, respectively. Second, 13 landslide conditioning factors including slope degree, slope aspect, altitude, plan curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, lithology, distance from streams, faults, roads, and land use type were prepared, and the relationships between these factors and the landslide inventory map were extracted by using the mentioned models. Subsequently, the multi-class weighted factors were used to generate landslide susceptibility maps. Finally, the susceptibility maps were verified and compared using several methods including receiver operating characteristic curve with the areas under the curve (AUC), landslide density, and spatially agreed area analyses. The success rate curve showed that the AUC for FR, WoE, and SI models was 81.51, 79.43, and 81.27, respectively. The prediction rate curve demonstrated that the AUC achieved by the three models was 80.44, 77.94, and 79.55, respectively. Although the sensitivity analysis using the FR model revealed that the modeling process was sensitive to input factors, the accuracy results suggest that the three models used in this study can be effective approaches for landslide susceptibility mapping in Mazandaran Province, and the resultant susceptibility maps are trustworthy for hazard mitigation strategies.