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A new artificial intelligence (AI) model, called Bagging-LMT - a combination of bagging ensemble and Logistic Model Tree (LMT) - is introduced for mapping flood susceptibility. A spatial database was generated for the Haraz watershed, northern Iran, that included a flood inventory map and eleven flood conditioning factors based on the Information Gain Ratio (IGR). The model was evaluated using precision, sensitivity, specificity, accuracy, Root Mean Square Error, Mean Absolute Error, Kappa and area under the receiver operating characteristic curve criteria. The model was also compared with four state-of-the-art benchmark soft computing models, including LMT, logistic regression, Bayesian logistic regression, and random forest. Results revealed that the proposed model outperformed all these models and indicate that the proposed model can be used for sustainable management of flood-prone areas. [ABSTRACT FROM AUTHOR]

Floods are one of the most damaging natural hazards causing huge loss of property, infrastructure and lives. Prediction of occurrence of flash flood locations is very difficult due to sudden change in climatic condition and manmade factors. However, prior identification of flood susceptible areas can be done with the help of machine learning techniques for proper timely management of flood hazards. In this study, we tested four decision trees based machine learning models namely Logistic Model Trees (LMT), Reduced Error Pruning Trees (REPT), Naïve Bayes Trees (NBT), and Alternating Decision Trees (ADT) for flash flood susceptibility mapping at the Haraz Watershed in the northern part of Iran. For this, a spatial database was constructed with 201 present and past flood locations and eleven flood-influencing factors namely ground slope, altitude, curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), land use, rainfall, river density, distance from river, lithology, and Normalized Difference Vegetation Index (NDVI) . Statistical evaluation measures, the Receiver Operating Characteristic (ROC) curve, and Freidman and Wilcoxon signed-rank tests were used to validate and compare the prediction capability of the models. Results show that the ADT model has the highest prediction capability for flash flood susceptibility assessment, followed by the NBT, the LMT, and the REPT, respectively. These techniques have proven successful in quickly determining flood susceptible areas. [ABSTRACT FROM AUTHOR]

This study attempts to compare popular statistical methods (linear, logarithmic, quadratic, power and exponential functions) with machine learning methods (multi-layer perceptron (MLP), radial base function (RBF), adaptive neural-based fuzzy inference system (ANFIS) and support vector machine (SVM)) for simulating the volume of landslides based on their surface area (VL ~ AL) in the Kurdistan province, Iran. Performances of the models were validated using some commonly error functions including the Adjusted R 2 , F-test and AIC (Akaike Information Criteria). The results showed that the power model demonstrates the best performance compared to other statistical methods whereas the ANFIS model outperforms other machine learning approaches. Furthermore, the comparative results showed that machine learning methods indicate better performances than simple statistical methods for simulating the volume of landslides in the study area. In practice, the outputs of this research can help managers and investigators decrease the cost of field surveys and measurements of volumes of landslides in landslide hazard management projects. [ABSTRACT FROM AUTHOR]

A comprehensive integrated framework was designed to categorize individual streams and watersheds based on their temporal variation, and to determine the key drivers controlling watershed hydrology in the Qorveh-Dehgolan Basin. Four stream networks were extracted from multi-temporal Pan-Sharpened Landsat 7 and Landsat 8 imageries. Nineteen geomorphometric indices are measured and classified into the linear aspect, aerial aspect, and relief aspect. The results revealed a significant decline in stream networks from 2000 to 2017, reducing basin order. The factor analysis identified three principal factors including 'factor of basin magnitude' (affecting low-altitude watersheds), 'factor of steepness' (influencing high-altitude watersheds), and 'factor of texture'. The focal mechanism highlighted the complex fault mechanisms in the study area. Finally, RadViz clustering showed that for the lower order streams in the high-altitudes watersheds, stream width showed higher temporal variability in comparison to other parameters. In contrast, flow accumulation showed high variance in higher-order streams. [ABSTRACT FROM AUTHOR]

The application of multivariate statistical analysis (MSA) in analyzing drainage basin morphometry, in conjunction with seismic events (focal mechanism) and associated tectonic influences, has been discussed less in the framework of existing geomorphometric studies. Hence, in this study, an integrated approach was designed combining the geomorphometric analysis with MSA, focal mechanism and associated structural lineaments (using a remote sensing and GIS framework) to address the role of active tectonism on the drainage network characteristics in Qorveh-Dehgolan basin located in western part of Iran (Kurdistan province). Eight watersheds were delineated from burnt ASTER DEM using SWAT model and categorized into high-altitude watersheds and low-altitude watersheds. Four-sets of the drainage network were extracted from satellite imageries (Landsat 7 and 8) over a period of 17 years (2000–2017) to monitor the temporal stream network behavior. Overall, nineteen morphometric parameters were calculated (and categorized into linear, aerial, and relief aspects) exhibiting significant changes in the drainage network from 2000 to 2017 (the stream order declined from 5th to 4th order). The higher-altitude watersheds showed various anomalies in the calculated indices (indicating the potential influence of tectonic activity), while the lower altitude watersheds did not exhibit any specific anomalies. In addition, higher concentration of longer lineaments was observed in areas where the relatively resistant, fractured bedrock lithology (meta-volcanic and meta-sedimentary rocks) provided favorable conditions for origin and propagation of the lineaments. Further analysis of focal mechanism solutions (FMS, beachball diagrams) showed that the study area is primarily influenced by moderate earthquake events (3 ≤ Mw ≤ 6) and is influenced by a complex fault mechanism (primarily normal faults and strike slip faults). The morphometric parameters were further subjected to the factor analysis (principal axis factoring using Varimax rotation transformation) resulting in derivation of "factor of basin magnitude", "factor of steepness" and "factor of texture" as three principal factors impacting the drainage network. Finally, it was concluded that "factor of steepness" played a decisive role in defining hydrology of higher-altitude watersheds (an indication of tectonic influences in these watersheds) and thereby defining the overall hydrology of the study area as well. Overall, this study highlighted the significant role of the tectonic activity associated with structural lineaments and the seismic events in defining the nature and behavior of the drainage network and certain anomalies caused in the basin hydrology under these structural controls. [ABSTRACT FROM AUTHOR]

Landslide is a natural hazard which causes huge loss of properties and human life in many places of the world. Mapping of landslide susceptibility is an important task for preventing and combating the landslides problems. Main objective of this study is to use different artificial intelligence methods namely support vector machines (SVM), artificial neural networks (ANN), logistic regression (LR), and reduced error-pruning tree (REPT) in the development of models for landslide susceptibility mapping of Muong Lay district of Vietnam. In total data of 217 landslide locations of the study area was used for the development and evaluation of the models. Nine landslide-conditioning factors were used for generating the datasets for training and validating the models. Results show that the SVM outperformed all other methods namely ANN, LR and REPT. Thus, it can be suggested that the SVM method is more useful in developing accurate and robust landslide prediction model. [ABSTRACT FROM AUTHOR]

In this study, morphotectonic analysis was performed to investigate the influence of tectonic activity on drainage network in Qorve-Dehgolan basin, located in Sanandaj - Sirjan Zone (SSZ). Eight watersheds were delineated using burnt DEM and eleven morphotectonic indices including stream length gradient index (SL); drainage basin asymmetry (Af); hypsometric curve (HC); hypsometric integral (HI); roughness (R); elongation ratio (Re); mountain front sinuosity (Smf); basin shape (Bs); curvature of surface (C), basin slope (Sb) and river sinuosity index (K) were calculated. The higher altitude watersheds specifically showed various anomalies in the calculated indices. These indices when subjected to factor analysis (using Varimax rotation) resulted in extraction of "steepness factor" and "asymmetric factor" as two principal factors influencing the drainage network. The integrated analysis revealed that steepness factor, indicating ongoing tectonism, plays a significant role in the hydrology of higher altitude watersheds, whereas asymmetric factor is more dominant in lower altitude watersheds. [ABSTRACT FROM AUTHOR]

In the current study, a combination of automated lineament extraction from Landsat 8 satellite imagery and 3D interactive visual interpretation (using DEM) alongside image processing techniques (Gram-Schmidt pan-sharpening, convolution directional filter) was carried out to investigate expression and influence of tectonic activity on drainage morphometry within Qorveh-Dehgolan basin. The watersheds, derived from burned DEM using SWAT, were categorized into high and low-altitude watersheds depending on the associated relief. Analysis of spatial relationship of lineament density with drainage density and basin slope revealed that structural lineaments have a decisive control over drainage density distribution particularly at higher basin slopes of certain watersheds. These high-altitude watersheds, associated with more-resistant lithology, were also found to cover higher percentage of total lineaments compared to the lower-altitude watersheds associated with less-resistant lithology. The analysis presented significant insights into the influence of tectonic activity on drainage network in a relatively unexplored area and provided vital baseline information for future investigations. [ABSTRACT FROM AUTHOR]

A novel artificial intelligence approach of Bayesian Logistic Regression (BLR) and its ensembles [Random Subspace (RS), Adaboost (AB), Multiboost (MB) and Bagging] was introduced for landslide susceptibility mapping in a part of Kamyaran city in Kurdistan Province, Iran. A spatial database was generated which includes a total of 60 landslide locations and a set of conditioning factors tested by the Information Gain Ratio technique. Performance of these models was evaluated using the area under the ROC curve (AUROC) and statistical index-based methods. Results showed that the hybrid ensemble models could significantly improve the performance of the base classifier of BLR (AUROC = 0.930). However, RS model (AUROC = 0.975) had the highest performance in comparison to other landslide ensemble models, followed by Bagging (AUROC = 0.972), MB (AUROC = 0.970) and AB (AUROC = 0.957) models, respectively. [ABSTRACT FROM AUTHOR]

In this study, the main goal is to compare the predictive capability of Support Vector Machines (SVM) with four Bayesian algorithms namely Naïve Bayes Tree (NBT), Bayes network (BN), Naïve Bayes (NB), Decision Table Naïve Bayes (DTNB) for identifying landslide susceptibility zones in Pauri Garhwal district (India). First, landslide inventory map was built using 1295 historical landslide data, then in total sixteen influencing factors were selected and tested for landslide susceptibility modelling. Performance of the model was evaluated and compared using Statistical based index methods, Area under the Receiver Operating Characteristic (ROC) curve named AUC, and Chi-square method. Analysis results show that that the SVM has the highest prediction capability, followed by the NBT, DTNBT, BN and NB, respectively. Thus, this study confirms that the SVM is one of the benchmark models for the assessment of susceptibility of landslides. [ABSTRACT FROM AUTHOR]

Abstract Understanding landslide characteristics such as their locations, dimensions, and spatial distribution is of highly importance in landslide modeling and prediction. The main objective of this study was to assess the effect of different sample sizes and raster resolutions in landslide susceptibility modeling and prediction accuracy of shallow landslides. In this regard, the Bijar region of the Kurdistan province (Iran) was selected as a case study. Accordingly, a total of 20 landslide conditioning factors were considered with six different raster resolutions (10 m, 15 m, 20 m, 30 m, 50 m, and 100 m) and four different sample sizes (60/40%, 70/30%, 80/20%, and 90/10%) were investigated. The merit of each conditioning factors was assessed using the Information Gain Ratio (IGR) technique, whereas Alternating decision tree (ADTree), which has been rarely explored for landslide modeling, was used for building models. Performance of the models was assessed using the area under the ROC curve (AUROC), sensitivity, specificity, accuracy, kappa and RMSE criteria. The results show that with increasing the number of training pixels in the modeling process, the accuracy is increased. Findings also indicate that for the sample sizes of 60/40% (AUROC = 0.800) and 70/30% (AUROC = 0.899), the highest prediction accuracy is derived with the raster resolution of 10 m. With the raster resolution of 20 m, the highest prediction accuracy for the sample size of 80/20% (AUROC = 0.871) and 90/10% (AUROC = 0.864). These outcomes provide a guideline for future research enabling researchers to select an optimal data resolution for landslide hazard modeling. Highlights • Prediction accuracy of landslide modeling under various scenarios was assessed. • Best combination of sample size and raster resolution for preparing LSMs was introduced. • Best sample size for the raster resolution of 10 m was acquired as 60/40% and 70/30%. • Best sample size for the raster resolution of 20 m was obtained as 80/20% and 90/10%. • Raster resolution of 10 m with sample size of 70/30% obtained the highest accuracy. [ABSTRACT FROM AUTHOR]

• MCDM and machine learning models were compared for flood modelling. • Results show that machine learning is more potential for flood modelling. • Out of machine learning methods, NB outperforms NBT. • Out of MCDM methods, SAW and VIKOR outperform TOPSIS. Floods around the world are having devastating effects on human life and property. In this paper, three Multi-Criteria Decision-Making (MCDM) analysis techniques (VIKOR, TOPSIS and SAW), along with two machine learning methods (NBT and NB), were tested for their ability to model flood susceptibility in one of China's most flood-prone areas, the Ningdu Catchment. Twelve flood conditioning factors were used as input parameters: Normalized Difference Vegetation Index (NDVI), lithology, land use, distance from river, curvature, altitude, Stream Transport Index (STI), Topographic Wetness Index (TWI), Stream Power Index (SPI), soil type, slope and rainfall. The predictive capacity of the models was evaluated and validated using the Area Under the Receiver Operating Characteristic curve (AUC). While all models showed a strong flood prediction capability (AUC > 0.95), the NBT model performed best (AUC = 0.98), suggesting that, among the models studied, the NBT model is a promising tool for the assessment of flood-prone areas and can allow for proper planning and management of flood hazards. [ABSTRACT FROM AUTHOR]

Abstract Landslides are major hazards for human activities often causing great damage to human lives and infrastructure. Therefore, the main aim of the present study is to evaluate and compare three machine learning algorithms (MLAs) including Naïve Bayes (NB), radial basis function (RBF) Classifier, and RBF Network for landslide susceptibility mapping (LSM) at Longhai area in China. A total of 14 landslide conditioning factors were obtained from various data sources, then the frequency ratio (FR) and support vector machine (SVM) methods were used for the correlation and selection the most important factors for modelling process, respectively. Subsequently, the resulting three models were validated and compared using some statistical metrics including area under the receiver operating characteristics (AUROC) curve, and Friedman and Wilcoxon signed-rank tests The results indicated that the RBF Classifier model had the highest goodness-of-fit and performance based on the training and validation datasets. The results concluded that the RBF Classifier model outperformed and outclassed (AUROC = 0.881), the NB (AUROC = 0.872) and the RBF Network (AUROC = 0.854) models. The obtained results pointed out that the RBF Classifier model is a promising method for spatial prediction of landslide over the world. Graphical abstract Unlabelled Image Highlights • The NB, RBF Classifier and RBF Network models were applied for landslide modelling. • Support vector machine (SVM) was used for conditioning factor selection process. • Evaluation and comparison of MLAs were performed by some statistical metrics. • The RBF Classifier model outperformed the NB and the RBF Network models. • The proposed model was suggested for other landslide prone areas over the world. [ABSTRACT FROM AUTHOR]

Abstract Coefficient of consolidation (C v) is a measure of compressibility of soil. This coefficient is an important parameter which is used in the design of foundation of civil engineering structures and also in the slope stability analysis. In this study, the main objective is to propose a hybrid machine learning approach named MLP-BBO for the prediction of the coefficient of consolidation of soft soil. This method is based on Multi-layer Perceptron Neural Network (MLP) and Biogeography-based Optimization (BBO). For this, a total number of 164 soil samples were collected from the Tan Vu–Lach Huyen highway project and Ha Noi–Hai Phong highway project sites. In the modeling, input parameters used include clay (%), moisture content (%), liquid limit (%), plastic limit (%), plasticity index (%), and liquidity index (%). To validate performance of the models, standard methods such as Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE), and correlation coefficient (r) were used. For the comparison of performance of the models, popular machine learning methods were used namely Backpropagation Multi-layer Perceptron Neural Networks (Bp-MLP Neural Nets), Radial Basis Functions Neural Networks (RBF-Neural Nets), Gaussian Process (GP), M5 Tree, and Support Vector Regression (SVR). Results of the model study indicate that the proposed method MLP-BBO has the highest predictive capability (RMSE = 0.397, MAE = 0.302 and r = 0.827), followed by the SVR (RMSE = 0.403, MAE = 0.299 and r = 0.819), the Bp-MLP Neural Nets (RMSE = 0.478, MAE = 0.398 and r = 0.805), the RBF-Neural Nets (RMSE = 0.412, MAE = 0.281 and r = 0.804), the GP (RMSE = 0.440, MAE = 0.301 and r = 0.777), and the M5 Tree (RMSE = 0.494, MAE = 0.322 and r = 0.728), in the prediction of C v, respectively. Therefore, it can be concluded that though performance of all applied models is good in the prediction of the coefficient of consolidation of soil but performance of the MLP-BBO is the best. Highlights • Coefficient of consolidation (C v) is an important geotechnical factor of the soil. • Hybrid machine learning approach MLP-BBO was developed for prediction of the C v. • Machine learning named SVR, ANN, GP, and M5 Tree were selected for comparison. • Results show that the MLP-BBO is the best compared with others. [ABSTRACT FROM AUTHOR]

In this research, eight individual machine learning and statistical models are implemented and compared, and based on their results, seven ensemble models for flood susceptibility assessment are introduced. The individual models included artificial neural networks, classification and regression trees, flexible discriminant analysis, generalized linear model, generalized additive model, boosted regression trees, multivariate adaptive regression splines, and maximum entropy, and the ensemble models were Ensemble Model committee averaging (EMca), Ensemble Model confidence interval Inferior (EMciInf), Ensemble Model confidence interval Superior (EMciSup), Ensemble Model to estimate the coefficient of variation (EMcv), Ensemble Model to estimate the mean (EMmean), Ensemble Model to estimate the median (EMmedian), and Ensemble Model based on weighted mean (EMwmean). The data set covered 201 flood events in the Haraz watershed (Mazandaran province in Iran) and 10,000 randomly selected non-occurrence points. Among the individual models, the Area Under the Receiver Operating Characteristic (AUROC), which showed the highest value, belonged to boosted regression trees (0.975) and the lowest value was recorded for generalized linear model (0.642). On the other hand, the proposed EMmedian resulted in the highest accuracy (0.976) among all models. In spite of the outstanding performance of some models, nevertheless, variability among the prediction of individual models was considerable. Therefore, to reduce uncertainty, creating more generalizable, more stable, and less sensitive models, ensemble forecasting approaches and in particular the EMmedian is recommended for flood susceptibility assessment. [ABSTRACT FROM AUTHOR]

Karst springs of the Zagros Mountains contribute a significant amount to agricultural and human water demands of western and south-western Iran. For an adequate management of available water resources in semi-arid and arid regions, sufficient hydrological monitoring is needed, and hydro-chemical and isotope hydrological data provide important additional information. About 350 water samples were collected from precipitation, river water, and karst springs of the upper part of the Karkheh River Basin (20,895 km2) located between 33°35′and 34°55′North and 46°22′and 49°10′East with elevations ranging from 928 to 3563 m above sea level. Sampling was conducted in monthly time resolution from August 2011 to July 2012. All samples were analysed for hydro-chemical parameters (pH, electrical conductivity, and major ions) and stable isotopes (deuterium, oxygen-18). Isotope values of precipitation indicate a local meteoric water line (Zagros MWL δ2H=6.8 δ18O+10.1;R2=0.99) situated between the Mediterranean MWL and Global MWL. Spring and river water isotope values vary between−7.1 and−4.1 ‰, and−38 and−25 ‰ for δ18O and δ2H, respectively, responding to winter snowmelt and evaporation. This work implements stable isotopes and hydro-chemical information of springs and river water to understand hydrological and hydro-geological interrelations in karstic semi-arid areas and helps to improve the current water resources management practices of western Iran. [ABSTRACT FROM AUTHOR]

Floods are some of the most dangerous and most frequent natural disasters occurring in the northern region of Iran. Flooding in this area frequently leads to major urban, financial, anthropogenic, and environmental impacts. Therefore, the development of flood susceptibility maps used to identify flood zones in the catchment is necessary for improved flood management and decision making. The main objective of this study was to evaluate the performance of an Evidential Belief Function (EBF) model, both as an individual model and in combination with Logistic Regression (LR) methods, in preparing flood susceptibility maps for the Haraz Catchment in the Mazandaran Province, Iran. The spatial database created consisted of a flood inventory, altitude, slope angle, plan curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance from river, rainfall, geology, land use, and Normalized Difference Vegetation Index (NDVI) for the region. After obtaining the required information from various sources, 151 of 211 recorded flooding points were used for model training and preparation of the flood susceptibility maps. For validation, the results of the models were compared to the 60 remaining flooding points. The Receiver Operating Characteristic (ROC) curve was drawn, and the Area Under the Curve (AUC) was calculated to obtain the accuracy of the flood susceptibility maps prepared through success rates (using training data) and prediction rates (using validation data). The AUC results indicated that the EBF, EBF from LR, EBF-LR (enter), and EBF-LR (stepwise) success rates were 94.61%, 67.94%, 86.45%, and 56.31%, respectively, and the prediction rates were 94.55%, 66.41%, 83.19%, and 52.98%, respectively. The results showed that the EBF model had the highest accuracy in predicting flood susceptibility within the catchment, in which 15% of the total areas were located in high and very high susceptibility classes, and 62% were located in low and very low susceptibility classes. These results can be used for the planning and management of areas vulnerable to floods in order to prevent flood-induced damage; the results may also be useful for natural disaster assessment. [ABSTRACT FROM AUTHOR]

In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811). [ABSTRACT FROM AUTHOR]

We used a novel hybrid functional machine learning algorithm to predict the spatial distribution of landslides in the Sarkhoon watershed, Iran. We developed a new ensemble model which is a combination of a functional algorithm, stochastic gradient descent (SGD) and an AdaBoost (AB) Meta classifier namely ABSGD model to predict the landslides. The model incorporates 20 landslide conditioning factors, which we ranked using the least-square support vector machine (LSSVM) technique. For the modeling, we considered 98 landslide locations, of which 70% (79) were used for training and 30% (19) for validation processes. Model validation was performed using sensitivity, specificity, accuracy, the root mean square error (RMSE) and the area under the receiver operatic characteristic (AUC) curve. We also used soft computing benchmark models, including SGD, logistic regression (LR), logistic model tree (LMT) and functional tree (FT) algorithms for model validation and comparison. The selected conditioning factors were significant in landslide occurrence but distance to road was found to be the most important factor. The ABSGD model (AUC= 0.860) outperformed the LR (0.797), SGD (0.776), LMT (0.740) and FT (0.734) models. Our results confirm that the combined use of a functional algorithm and a Meta classifier prevents over-fitting, reduces noise and enhances the power prediction of the individual SGD algorithm for the spatial prediction of landslides. [ABSTRACT FROM AUTHOR]

The main objective of this research was to introduce a novel machine learning algorithm of alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation forest (RF) and random subspace (RS) ensemble algorithms under two scenarios of different sample sizes and raster resolutions for spatial prediction of shallow landslides around Bijar City, Kurdistan Province, Iran. The evaluation of modeling process was checked by some statistical measures and area under the receiver operating characteristic curve (AUROC). Results show that, for combination of sample sizes of 60%/40% and 70%/30% with a raster resolution of 10 m, the RS model, while, for 80%/20% and 90%/10% with a raster resolution of 20 m, the MB model obtained a high goodness-of-fit and prediction accuracy. The RS-ADTree and MB-ADTree ensemble models outperformed the ADTree model in two scenarios. Overall, MB-ADTree in sample size of 80%/20% with a resolution of 20 m (area under the curve (AUC) = 0.942) and sample size of 60%/40% with a resolution of 10 m (AUC = 0.845) had the highest and lowest prediction accuracy, respectively. The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas. [ABSTRACT FROM AUTHOR]

Adaptive neuro-fuzzy inference system (ANFIS) includes two novel GIS-based ensemble artificial intelligence approaches called imperialistic competitive algorithm (ICA) and firefly algorithm (FA). This combination could result in ANFIS-ICA and ANFIS-FA models, which were applied to flood spatial modelling and its mapping in the Haraz watershed in Northern Province of Mazandaran, Iran. Ten influential factors including slope angle, elevation, stream power index (SPI), curvature, topographic wetness index (TWI), lithology, rainfall, land use, stream density, and the distance to river were selected for flood modelling. The validity of the models was assessed using statistical error-indices (RMSE and MSE), statistical tests (Friedman and Wilcoxon signed-rank tests), and the area under the curve (AUC) of success. The prediction accuracy of the models was compared to some new state-of-the-art sophisticated machine learning techniques that had previously been successfully tested in the study area. The results confirmed the goodness of fit and appropriate prediction accuracy of the two ensemble models. However, the ANFIS-ICA model (AUC = 0.947) had a better performance in comparison to the Bagging-LMT (AUC = 0.940), BLR (AUC = 0.936), LMT (AUC = 0.934), ANFIS-FA (AUC = 0.917), LR (AUC = 0.885) and RF (AUC = 0.806) models. Therefore, the ANFIS-ICA model can be introduced as a promising method for the sustainable management of flood-prone areas. [ABSTRACT FROM AUTHOR]

This research aims at proposing a new artificial intelligence approach (namely RVM-ICA) which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive Algorithm (ICA) optimization for landslide susceptibility modeling. A Geographic Information System (GIS) spatial database was generated from Lang Son city in Lang Son province (Vietnam). This GIS database includes a landslide inventory map and fourteen landslide conditioning factors. The suitability of these factors for landslide susceptibility modeling in the study area was verified by the Information Gain Ratio (IGR) technique. A landslide susceptibility prediction model based on RVM-ICA and the GIS database was established by training and prediction phases. The predictive capability of the new approach was evaluated by calculations of sensitivity, specificity, accuracy, and the area under the Receiver Operating Characteristic curve (AUC). In addition, to assess the applicability of the proposed model, two state-of-the-art soft computing techniques including the support vector machine (SVM) and logistic regression (LR) were used as benchmark methods. The results of this study show that RVM-ICA with AUC = 0.92 achieved a high goodness-of-fit based on both the training and testing datasets. The predictive capability of RVM-ICA outperformed those of SVM with AUC = 0.91 and LR with AUC = 0.87. The experimental results confirm that the newly proposed model is a very promising alternative to assist planners and decision makers in the task of managing landslide prone areas. [ABSTRACT FROM AUTHOR]

Since landslide detection using the combination of AIRSAR data and GIS-based susceptibility mapping has been rarely conducted in tropical environments, the aim of this study is to compare and validate support vector machine (SVM) and index of entropy (IOE) methods for landslide susceptibility assessment in Cameron Highlands area, Malaysia. For this purpose, ten conditioning factors and observed landslides were detected by AIRSAR data, WorldView-1 and SPOT 5 satellite images. A spatial database was generated including a total of 92 landslide locations encompassing the same number of observed and detected landslides, which was divided into training (80%; 74 landslide locations) and validation (20%; 18 landslide locations) datasets. Results of the difference between observed and detected landslides using root mean square error (RMSE) indicated that only 16.3% error exists, which is fairly acceptable. The validation process was performed using statistical-based measures and the area under the receiver operating characteristic (AUROC) curves. Results of validation process indicated that the SVM model has the highest values of sensitivity (88.9%), specificity (77.8%), accuracy (83.3%), Kappa (0.663) and AUROC (84.5%), followed by the IOE model. Overall, the SVM model applied to detected landslides is considered to be a promising technique that could be tested and utilized for landslide susceptibility assessment in tropical environments. [ABSTRACT FROM AUTHOR]

In this study, land subsidence susceptibility was assessed for a study area in South Korea by using four machine learning models including Bayesian Logistic Regression (BLR), Support Vector Machine (SVM), Logistic Model Tree (LMT) and Alternate Decision Tree (ADTree). Eight conditioning factors were distinguished as the most important affecting factors on land subsidence of Jeong-am area, including slope angle, distance to drift, drift density, geology, distance to lineament, lineament density, land use and rock-mass rating (RMR) were applied to modelling. About 24 previously occurred land subsidence were surveyed and used as training dataset (70% of data) and validation dataset (30% of data) in the modelling process. Each studied model generated a land subsidence susceptibility map (LSSM). The maps were verified using several appropriate tools including statistical indices, the area under the receiver operating characteristic (AUROC) and success rate (SR) and prediction rate (PR) curves. The results of this study indicated that the BLR model produced LSSM with higher acceptable accuracy and reliability compared to the other applied models, even though the other models also had reasonable results. [ABSTRACT FROM AUTHOR]