Geospatial approach in mapping soil erodibility using CartoDEM – A case study in hilly watershed of Lower Himalayan Range

Soil erodibility is one of the most important factors used in spatial soil erosion risk assessment. Soil information derived from soil map is used to generate soil erodibility factor map. Soil maps are not available at appropriate scale. In general, soil maps at small scale are used in deriving soil erodibility map that largely generalized spatial variability and it largely ignores the spatial variability since soil map units are discrete polygons. The present study was attempted to generate soil erodibilty map using terrain indices derived from DTM and surface soil sample data. Soil variability in the hilly landscape is largely controlled by topography represented by DTM. The CartoDEM (30 m) was used to derive terrain indices such as terrain wetness index (TWI), stream power index (SPI), sediment transport index (STI) and slope parameters. A total of 95 surface soil samples were collected to compute soil erodibility factor (K) values. The K values ranged from 0.23 to 0.81 t ha^?1R^?1 in the watershed. Correlation analysis among K-factor and terrain parameters showed highest correlation of soil erodibilty with TWI (r ²= 0.561) followed by slope (r ²= 0.33). A multiple linear regression model was developed to derive soil erodibilty using terrain parameters. A set of 20 soil sample points were used to assess the accuracy of the model. The coefficient of determination (r ²) and RMSE were computed to be 0.76 and 0.07 t ha^?1R^?1 respectively. The proposed methodology is quite useful in generating soil erodibilty factor map using digital elevation model (DEM) for any hilly terrain areas. The equation/model need to be established for the particular hilly terrain under the study. The developed model was used to generate spatial soil erodibility factor (K) map of the watershed in the lower Himalayan range.     

Prediction of spatial soil organic carbon distribution using Sentinel-2A and field inventory data in Sariska Tiger Reserve

Dynamic and vigorous top soil is the source for healthy flora, fauna, and humans, and soil organic matters are the underpinning for healthy and productive soils. Organic components in the soil play significant role in stimulating soil productivity processes and vegetation development. This article deals with the scientific demand for estimating soil organic carbon (SOC) in forest using geospatial techniques. We assessed distribution of SOC using field and satellite data in Sariska Tiger Reserve located in the Aravalli Hill Range, India. This study utilized the visible and near-infrared reflectance data of Sentinel-2A satellite. Three predictor variables namely Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, and Renormalized Difference Vegetation Index were derived to examine the relationship between soil and SOC and to identify the biophysical characteristic of soil. Relationship between SOC (ground and predicted) and leaf area index (LAI) measured through satellite data was examined through regression analysis. Coefficient of correlation (R ²) was found to be 0.95 (p value < 0.05) for predicted SOC and satellite measured LAI. Thus, LAI can effectively be used for extracting SOC using remote sensing data. Soil organic carbon stock map generated through Kriging model for Landsat 8 OLI data demonstrated variation in spatial SOC stocks distribution. The model with 89% accuracy has proved to be an effective tool for predicting spatial distribution of SOC stocks in the study area. Thus, optical remote sensing data have immense potential for predicting SOC at larger scale.     

Using Stable Isotopes to Assess the Contribution of Terrestrial and Riverine Organic Matter to Diets of Nearshore Marine Consumers in a Glacially Influenced Estuary

Terrestrial and marine ecosystems in Southeast Alaska are linked by the flow of freshwater from precipitation and glacial runoff, which transports nutrients and organic matter (OM) downstream to estuaries. We examined the contribution of terrestrial-riverine and marine OM to diets of fishes (N = 257, four species) and invertebrates (N = 90, six species) collected from glacially influenced estuaries in Southeast Alaska using multiple stable isotopes (δ¹³C, δ¹⁵N, and δ³⁴S). Multivariate analysis of similarity (ANOSIM) was used to quantify variation in stable isotope composition of consumers across 6 months and three sites with watersheds that differed in their glacier and forest composition. Fishes showed weak differences (ANOSIM R = 0.141) in stable isotope composition among sampling months, moderate differences (ANOSIM R = 0.375) among sites, and strong differences (ANOSIM R = 0.583) among species. Invertebrates showed moderate differences (ANOSIM R = 0.352) in stable isotope composition among sampling months and strong differences among sites (ANOSIM R = 0.710) and species (ANOSIM R = 0.858). We found the greatest differences in stable isotope composition between the two estuary sites with watersheds containing the highest and lowest glacial coverage, indicating that the contribution of allochthonous OM to consumer diets varies across watershed types. Invertebrates collected from the site with the lowest glacial coverage in the watershed were more depleted in δ¹³C and δ³⁴S, indicating higher use of terrestrial-riverine OM, than those at sites with higher watershed glacial coverage. High variation in stable isotope composition among species, months, and sites underscores the complexity of estuary food web responses to future glacier loss.     

An expert system based on hybrid ICA-ANN technique to estimate macerals contents of Indian coals

Coal, as an initial source of energy, requires a detailed investigation in terms of ultimate analysis, proximate analysis, and its biological constituents (macerals). The rank and calorific value of each type of coal are managed by the mentioned properties. In contrast to ultimate and proximate analyses, determining the macerals in coal requires sophisticated microscopic instrumentation and expertise. This study emphasizes the estimation of the concentration of macerals of Indian coals based on a hybrid imperialism competitive algorithm (ICA)–artificial neural network (ANN). Here, ICA is utilized to adjust the weight and bias of ANNs for enhancing their performance capacity. For comparison purposes, a pre-developed ANN model is also proposed. Checking the performance prediction of the developed models is performed through several performance indices, i.e., coefficient of determination (R ²), root mean square error and variance account for. The obtained results revealed higher accuracy of the proposed hybrid ICA-ANN model in estimating macerals contents of Indian coals compared to the pre-developed ANN technique. Results of the developed ANN model based on R ² values of training datasets were obtained as 0.961, 0.955, and 0.961 for predicting vitrinite, liptinite, and inertinite, respectively, whereas these values were achieved as 0.948, 0.947, and 0.957, respectively, for testing datasets. Similarly, R ² values of 0.988, 0.983, and 0.991 for training datasets and 0.989, 0.982, and 0.985 for testing datasets were obtained from developed ICA-ANN model.     

Modelling soil erosion from a watershed using cubic splines

Erosion in a watershed exhibits spatial and temporal variability, and its determination is fundamental to determining sediment yield which is a key to proper watershed management. In this study, we propose a relationship between the curve number (SCS 1956) and Sediment Yield Index (SYI) using cubic splines. The method is illustrated with a case study of one watershed of Narmada Basin located in Mandla district of Madhya Pradesh, India. Cubic splines are found to perform satisfactorily with Nash efficiency of 63.64%, absolute prediction error of 2.64%, integral square error of 1.22%, coefficient of correlation of 93.78% and degree of agreement of 0.99%. The relation between observed and computed SYI values is correlated with a coefficient of determination (R ²) of 0.87. Such a relationship can be used to determine SYI from the available CN value, which may be quite useful in field applications.     

A comparative study of shallow groundwater level simulation with WA–ANN and ITS model in a coastal island of south China

Accurate and reliable prediction of shallow groundwater level is a critical component in water resources management. Two nonlinear models, WA–ANN method based on discrete wavelet transform (WA) and artificial neural network (ANN) and integrated time series (ITS) model, were developed to predict groundwater level fluctuations of a shallow coastal aquifer (Fujian Province, China). The two models were testified with the monitored groundwater level from 2000 to 2011. Two representative wells are selected with different locations within the study area. The error criteria were estimated using the coefficient of determination (R ²), Nash–Sutcliffe model efficiency coefficient (E), and root-mean-square error (RMSE). The best model was determined based on the RMSE of prediction using independent test data set. The WA–ANN models were found to provide more accurate monthly average groundwater level forecasts compared to the ITS models. The results of the study indicate the potential of WA–ANN models in forecasting groundwater levels. It is recommended that additional studies explore this proposed method, which can be used in turn to facilitate the development and implementation of more effective and sustainable groundwater management strategies.     

The morphological characteristics of gully systems and watersheds in Dry-Hot Valley,SW China

Gully systems and watersheds are geomorphic units with clear boundaries that are relatively independent of basin landscapes and play an important role in natural geography. In order to explore the morphological characteristics of gully systems and watersheds in the Dry-Hot Valley [South West (SW) China], gullies are interpreted from online Google images with high resolution and watersheds are extracted from digital elevation model at a scale of 1:50,000. The results show that: (1) There are 17,382 gullies (with a total area of 1141.66 km²) and 42 watersheds in the study area. (2) The average gully density of the study area (D) is 4.29 km/km², gully frequency (F) is 14.39 gullies/km², the branching ratio (B) is 5.13, the length ratio (L) is 3.12, and the coefficient of the main and tributary gullies (M) is 0.06. The degree of gully erosion is strong to extremely strong, the main development intensity of gully erosion ranges from intense to moderate, and the type of gully system is tributary. (3) The watershed areas (A) are between 0.39 and 96.43 km², the relief ratio (R) is from 0.10 to 0.19, the circularity ratio (C) is from 0.30 to 0.83, the texture ratio (T) is from 0.82 to 39.35, and the dominant geomorphological texture type is fine. (4) There is a quantitative relationship between F and D:F?=?0.624D² (R?=0.84) and T is closely related to D, F, M (R²?>?0.7). A, R and C are related to M (R²?>?0.5). The development of gully systems is the result of coupling effects between multiple factors. In this area, the degree of erosion and the condition of the main and tributary gullies can be controlled by the degree of topographic breakage in the watershed, which provides some theoretical basis for the evaluation of gully erosion by the latter. In addition, the scale, relief, and shape have a significant impact on the locations of the main and tributary gullies. For tributary gullies, attention should be paid to the interception and control of runoff and sediment in the small confluence branches in order to prevent gully expansion and head advance. These features can inform the development of targeted measures for the control of soil erosion.     

Mathematical expression of discharge capacity of compound open channels using MARS technique

In this paper, analytical methods, artificial neural network (ANN) and multivariate adaptive regression splines (MARS) techniques were utilised to estimate the discharge capacity of compound open channels (COC). To this end, related datasets were collected from literature. The results showed that the divided channel method with a coefficient of determination (R ²) value of 0.76 and root mean square error (RMSE) value of 0.162 has the best performance, among the various analytical methods tested. The performance of applied soft computing models with R ²=0.97 and RMSE = 0.03 was found to be more accurate than analytical approaches. Comparison of MARS with the ANN model, in terms of developed discrepancy ratio (DDR) index, showed that the accuracy of MARS model was better than that of MLP model. Reviewing the structure of the derived MARS model showed that the longitudinal slope of the channel (S), relative flow depth (H _r ) and relative area (A _r ) have a high impact on modelling and forecasting the discharge capacity of COCs.     

Data-driven nonlinear modeling studies on removal of Acid Yellow 59 using Si-doped multi-walled carbon nanotubes

Data-driven modeling of removal of color index name of Acid Yellow 59 from aqueous solutions using multi-walled carbon nanotubes by multiple (non)linear regression and artificial neural networks (ANN) models based on leave-one-out cross-validation to predict the adsorbed dye amount per unit mass of adsorbent (mg g^?1) and performance evaluation of the proposed multiple (non)linear regression and ANN models is the main novel contributor of the present study. Initial dye concentration, adsorbent concentration, reaction time, and temperature were determined as explanatory variables and input neurons for multiple (non)linear regression and ANN models, respectively. The total number of experiments was determined as 1280 statistically. The results showed that multilayer perception ANN model (\(R^{2}_{\text{training}}\) = 0.9997, \(R^{2}_{\text{testing}}\) = 0.9993, RMSE = 0.7678, MAE of 0.0007) predicted q _t better than multiple (non)linear regression model (\(R^{2}_{\text{adj}}\) = 0.9645, \(R^{2}_{\text{pred}}\) = 0.9633, SE = 9.55) and MLR (R ² = 0.9543, SE = 10.87) models. The results justified the accuracy of ANN in prediction of q _t , significantly.     

Mapping heatwave vulnerability in Korea

Analysis of event-based soil erosion magnitude with special return periods is essential to appropriately design strategies and adopt soil conservation practices. However, the spatiotemporal variations of soil erosion with different return periods, especially at national level, have not been adequately considered. Therefore, the present study aimed to zone rainfall erosivity index (R factor) as the most dynamic factor affecting variability of soil erosion rate, with different return periods in monthly, seasonal and annual time scales in Iran. Toward this attempt, the kinetic energy and maximum 30-min intensity (I ₃₀) over 12,000 available and accessible events of 70 stations were calculated during the common period of 1984–2004 and the corresponding R factor of the Universal Soil Loss Equation was then computed. Subsequently, the best-fitted frequency distributions were determined in all stations in three time scales using the EasyFit Software. The R factor was accordingly estimated for 2-, 5-, 10-, 25- and 50-year return periods. In addition, the inverse distance weighting technique was employed to determine and analyze the spatial variability patterns of R factor in different time scales using geographic information system. The results indicated that the frequency distributions fitted to study data were different in study time scales due to variability of spatiotemporal patterns of R factor. In addition, no specific spatial pattern of R factor could be recognized for different return periods and time scales. The average annual R factor was also found 1.41 MJ mm ha^?1 h^?1, whereas the respective R factor for different respective return periods of 2, 5, 10, 25 and 50 years was obtained 1.47, 2.62, 3.35, 4.48 and 5.54 MJ mm ha^?1 h^?1. These findings can be used for suitable decision making and effective environmental planning for land management Iran countrywide.     

Spatial information of soil hydraulic conductivity and performance of cokriging over kriging in a semi-arid basin scale

Unlike the studies in small parcels by systematic measurements, the spatial variability of soil properties is expected to increase in those over relatively large areas or scales. Spatial variability of soil hydraulic conductivity (K _h) is of significance for the environmental processes, such as soil erosion, plant growth, transport of the plant nutrients in a soil profile and ground water levels. However, its variability is not much and sufficiently known at basin scale. A study of testing the performance of cokriging of K _h compared with that of kriging was conducted in the catchment area of Sarayköy II Irrigation Dam in Cank?r?, Turkey. A total of 300 soil surface samples (0–10 cm) were collected from the catchment with irregular intervals. Of the selected soil properties, because the water-stable aggregates (WSA) indicated the highest relationship with the hydraulic conductivity by the Pearson correlation analysis, it is used as an auxiliary variable to predict K _h by the cokriging procedure. In addition, the sampling density was reduced randomly to n = 175, n = 150, n = 75 and n = 50 for K _h to determine if the superiority of cokriging over kriging would exist. Statistically, the results showed that all reduced K _h was as good as the complete K _h when its auxiliary relations with WSA were used in cokriging. Particularly, the results of the “Relative Reduction in MSE” (RMSE) revealed that the reduced data set of n = 75 produced the most accurate map than the others. In this basin-scaled study, there was a clear superiority of the cokriging procedure by the reduction in data although a very undulating topography and topographically different aspects, two different land uses with non-uniform vegetation density, different parent materials and soil textures were present in the area. Hence, using the statistically significant auxiliary relationship between K _h and WSA might bring about a very useful data set for watershed hydrological researches.     

Prediction of an environmental issue of mine blasting: an imperialistic competitive algorithm-based fuzzy system

Ground vibration resulting from blasting is one of the most important environmental problems at open-cast mines. Therefore, accurately approximating the blast-induced ground vibration is very significant. By reviewing the previous investigations, many attempts have been done to create the empirical models for estimating ground vibration. Nevertheless, the performance of the empirical models is not good enough. In this research work, a new hybrid model of fuzzy system (FS) designed by imperialistic competitive algorithm (ICA) is proposed for approximating ground vibration resulting from blasting at Miduk copper mine, Iran. For comparison aims, various empirical models were also utilized. Results from different predictor models were compared by using coefficient of multiple determination (R ²), variance account for and root-mean-square error between measured and predicted values of the PPVs. Results prove that the FS–ICA model outperforms the other empirical models in terms of the prediction accuracy. In other words, the FS–ICA model with R ² of 0.942 can forecast PPV better than the USBM with R ² of 0.634, Ambraseys–Hendron with R ² of 0.638, Langefors–Kihlstrom with R ² of 0.637 and Indian Standard with R ² of 0.519.     

Prediction of local scour around complex piers using GEP and M5-Tree

Local scour around piers is one of the main causes of bridge failures. In this study, three robust techniques, artificial neural networks (ANNs), M5-Tree, and Gene Expression Programming (GEP), were employed for prediction of scour depth around complex piers. The clear water condition was chosen for all experimental tests. The results indicated that pier diameter (b _c) and foundation level (Y) are the main parameters for local scour. Furthermore, the minimum scour depth occurs in range of Y/b _c = 1.1~1.3. In next step, to evaluate the mentioned techniques, a wide range of dataset was collected from the present study and literature. The radial base function (RBF) with R ² = 0.945 and RMSE = 0.031 provides better prediction in comparison with conventional equations, M5-Tree (R ² = 0.883, RMSE = 0.292) and the GEP techniques (R ² = 0.811 and RMSE = 0.263). The equations developed by M5-Tree and GEP are more useful for practical purposes and can be easily employed to predict the depth of scour at complex piers.     

Predicting longitudinal dispersion coefficient using ANN with metaheuristic training algorithms

A reliable prediction of dispersion coefficient can provide valuable information for environmental scientists and river engineers as well. The main objective of this study is to apply intelligence techniques for predicting longitudinal dispersion coefficient in rivers. In this regard, artificial neural network (ANN) models were developed. Four different metaheuristic algorithms including genetic algorithm (GA), imperialist competitive algorithm (ICA), bee algorithm (BA) and cuckoo search (CS) algorithm were employed to train the ANN models. The results obtained through the optimization algorithms were compared with the Levenberg–Marquardt (LM) algorithm (conventional algorithm for training ANN). Overall, a relatively high correlation between measured and predicted values of dispersion coefficient was observed when the ANN models trained with the optimization algorithms. This study demonstrates that the metaheuristic algorithms can be successfully applied to make an improvement on the performance of the conventional ANN models. Also, the CS, ICA and BA algorithms remarkably outperform the GA and LM algorithms to train the ANN model. The results show superiority of the performance of the proposed model over the previous equations in terms of DR, R ² and RMSE.     

Phosphorus dynamics and corn growth under applications of corn stalks biochar in a clay soil

Biochar prepared from corn stalks is used as a source of phosphorus in this study. The hypotheses were to investigate effects of biochar applications in clay soil on availability, changes of phosphorus pools and maximum adsorption of phosphorus as well as corn growth. The soil was placed in plastic pots with each contains 3 kg of this soil. Biochar was added at levels of 0 (control), 6.5 (B₁), 19 (B₂), and 38 (B₃) g pot^?1. In this experiment, the pot was planted with corn (Zea mays). The results of this study revealed that the biochar application enhanced available phosphorus (Olsen-P) from 11.51 to 17.10 mg kg^?1. Adding biochar significantly increased the amount of NH₄Cl-P, NaHCO₃-P_o, and NaOH I-P_o fractions (p?≤?0.05), but it significantly decreased HCl-P_i fraction (p?≤?0.05). Addition of biochar at the highest level increased the fresh and dry matter productions by up to about 75 and 48.7%, respectively, compared to the control. The phosphorus uptake by corn plants significantly increased with increasing levels of biochar. The removal efficiency (% sorption) and maximum adsorption (b) of phosphorus increased with increasing level of biochar addition compared to control. Consequently, it is recommended to add biochar produced from corn stalks to the soil in order to substitute phosphate fertilizers.     

Groundwater potential recharge estimation in bare soil using three soil moisture accounting models: field evaluation for a semi-arid foothill region

Performances of conventional and improved soil moisture balance as well as locally calibrated empirical models were evaluated in simulating potential recharge (R) and soil moisture content for a semi-arid foothill region. Models comparison with observed values using lysimeter data during [(2011–2012), (2012–2013)] reveal poor performance of conventional soil moisture balance model, underestimating annual R values. Improved soil moisture balance model provided acceptable estimation of annual R for 2011–2012 by considering the wetting of the near surface soil storage. However, it produced the worst simulation for daily soil moisture content once rainy season was over. Sensitivity analysis revealed that the precision degree of initial soil moisture deficit value would strongly influence estimation of R by improved soil moisture balance model, which can be viewed as a limiting factor. Additionally, locally calibrated model produced the best estimation of annual R and daily soil moisture content, which is suggested for the study region.     

SWAT manual calibration and parameters sensitivity analysis in a semi-arid watershed in North-western Morocco

Being a laborious approach, manual calibration of hydrologic model in a semi-arid context requires in-depth knowledge of the watershed and as much as possible field input data to obtain reliable simulations. In this study, manual calibration and relative sensitivity analysis approaches of the SWAT model (Soil and Water Assessment Tool) were applied for water balance in a 1993 km² watershed (on the R’dom river) located in North-western Morocco. The watershed is located in a semi-arid area dominated by agro-forestry activities. The objectives of this study were (i) to perform a local sensitivity analysis of the SWAT model taking into consideration the watershed characteristics and (ii) to implement a detailed methodology of manual calibration and validation of the model in a semi-arid context. Sensitivity analysis has been carried out on 12 different SWAT input parameters, and has revealed that 4 input parameters only were the most influential ones on flow components of the R’dom watershed. Model manual calibration was conducted along 2006 and 2007 by comparing measured and predicted monthly and daily discharges and taking Nash-Sutcliffe coefficient (NSE), determination coefficient (R ²), and percent bias (PBIAS) as goodness-of-fit indicators. Validation has been performed by the same approach through 2008 and 2009 period. All final NSE values were above 0.5, R ² values exceeded 0.7, and PBIAS lower than 25% demonstrating satisfactory model performances over the study watershed conditions. The SWAT model set-up with measured input data, manually calibrated and validated, reflects well the real hydrologic processes occurring in the R’dom watershed and can be used to assess current and future conditions and to evaluate alternative management practices.     

Characterization of fresh and weathered petroleum for potential impacts to soil fertility

In many petroleum-producing regions, there are not adequate controls to prevent pipeline breaks and spills, and thus soil is frequently contaminated with petroleum hydrocarbons. Different petroleum oil compounds may produce negative impacts on soil fertility. In this study, four fresh crudes, a weathered petroleum, and oils from bioremediated and burned sites were investigated (specific gravities 0.83–1.27). Fourier transform infrared spectroscopy revealed three predominant polar functional groups to be more plentiful in the heavier crudes. The relative abundance of these groups was used to calculate an index that was directly correlated with specific gravity (R ² = 0.9960) and the percent of asphaltene plus (polars + resins) fractions in the oil (R ² = 0.9643). This index correlated exponentially to the water repellency caused by petroleum in an alluvial soil (R ² = 0.9928). Furthermore, extra-heavy oil at a concentration of 10,000 ppm, the maximum allowable oil concentration in the soil that is within regulatory norms in many US states and other countries, and with a specific gravity >1.002, showed severe water repellency. This study presents an alternative for determining soil remediation criteria based on the API gravity of the oil rather than the C-range of the hydrocarbon mixtures, simplifying analytical methods and systematically studying the interaction between the kinds of petroleum mixtures and potential impacts to soil fertility.     

Orthogonal test and regression analysis of the strain on silty soil in Shanghai under metro loading

Cyclic triaxial test by means of the geotechnical digital system is conducted for the soil near the Guoquan Road Station of Metro Line 10 in Shanghai to analyze the strain characteristics and the variation law of saturated silty soil under subway loading. Orthogonal design method is used to arrange the experiment, considering the following factors: frequency ratio f _R, cyclic stress ratio σ _R, vibration time ratio N _R, and the interaction function among them. Results show that the cyclic stress ratio σ _R, the frequency ratio f _R, the vibration time ratio N _R, and the interaction between the cyclic stress ratio σ _R and the vibration time ratio N _R have a significant effect on the axial strain of the subway tunnel. The effect of the interaction between the cyclic stress ratio σ _R and the vibration time ratio N _R is also significant. From the analysis of variance and regression theory, the nonlinear regression equation of the cumulative plastic strain of silty soil under subway loading is established. Residual analysis proves that the equation is ideal and credible. The results have important value for the design of subway tunnels.     

A novel fuzzy <Emphasis Type="Italic">K</Emphasis>-nearest neighbor inference model with differential evolution for spatial prediction of rainfall-induced shallow landslides in a tropical hilly area using GIS

This research represents a novel soft computing approach that combines the fuzzy k-nearest neighbor algorithm (fuzzy k-NN) and the differential evolution (DE) optimization for spatial prediction of rainfall-induced shallow landslides at a tropical hilly area of Quy Hop, Vietnam. According to current literature, the fuzzy k-NN and the DE optimization are current state-of-the-art techniques in data mining that have not been used for prediction of landslide. First, a spatial database was constructed, including 129 landslide locations and 12 influencing factors, i.e., slope, slope length, aspect, curvature, valley depth, stream power index (SPI), sediment transport index (STI), topographic ruggedness index (TRI), topographic wetness index (TWI), Normalized Difference Vegetation Index (NDVI), lithology, and soil type. Second, 70 % landslide locations were randomly generated for building the landslide model whereas the remaining 30 % landslide locations was for validating the model. Third, to construct the landslide model, the DE optimization was used to search the optimal values for fuzzy strength (fs) and number of nearest neighbors (k) that are the two required parameters for the fuzzy k-NN. Then, the training process was performed to obtain the fuzzy k-NN model. Value of membership degree of the landslide class for each pixel was extracted to be used as landslide susceptibility index. Finally, the performance and prediction capability of the landslide model were assessed using classification accuracy, the area under the ROC curve (AUC), kappa statistics, and other evaluation metrics. The result shows that the fuzzy k-NN model has high performance in the training dataset (AUC?=?0.944) and validation dataset (AUC?=?0.841). The result was compared with those obtained from benchmark methods, support vector machines and J48 decision trees. Overall, the fuzzy k-NN model performs better than the support vector machines and the J48 decision trees models. Therefore, we conclude that the fuzzy k-NN model is a promising prediction tool that should be used for susceptibility mapping in landslide-prone areas.