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Flash-flood is considered to be one of the most destructive natural hazards in the world, which is difficult to accurately model and predict. The objective of the present research is to propose new ensembles of bivariate statistics and artificial intelligences and to introduce a comprehensive methodology for predicting flood susceptibility. The Putna river catchment of Romania is selected as a case study. In this regard, a total of six ensemble models were proposed and verified: Multilayer Perceptron neural network-Frequency Ratio (MLP-FR), Multilayer Perceptron neural network -Weights of Evidence (MLP-WOE), Rotation Forest-Frequency Ratio (RF-FR), Rotation Forest-Weights of Evidence (RF-WOE), Classification and Regression Tree-Frequency Ratio (CART-FR), and Classification and Regression Tree-Weights of Evidence (CART-WOE). In a first step, a geospatial database was created for the study area. This database includes 132 flood locations and 14 conditioning factors (lithology, slope angle, plan curvature, hydrological soil group, topographic wetness index, landuse, convergence index, elevation, distance from river, profile curvature, rainfall, aspect, stream power index, and topographic position index). In the next step, the Information Gain Ratio was used to evaluate the predictive ability of these factors. Subsequently, the database was used to train and validate the six ensemble models. The Receiver operating characteristic (ROC) curve, area under the curve (AUC), and statistical measures were used to evaluate the performance of the models. The results show that the prediction capability of the proposed ensemble models varied from 86.8% (the RF-FR model) to 93.9% (the RF-WOE model). These values indicate a high prediction performance for all the models. Therefore, we can state that the proposed ensemble models are new reliable tools which can be used for flood susceptibility modelling. Unlabelled Image • New six artificial intelligence ensemble models were proposed for food modelling. • 14 conditioning factors were considered as predictors for flood potential. • All ensemble models have high prediction performance. • MLP-WOE and RF-FR have the best prediction performance (>91%). • Slope is the most important factor for flood occurrence. [ABSTRACT FROM AUTHOR]

• PSO-MARS is proposed for flash-flood modeling and prediction. • PSO-MARS has high performance on the training and validation datasets. • PSO-MARS outperforms benchmark models i.e. BPANN, SVM, and decision tree. The main objective of this research work was to propose and verify a new soft computing approach based on Multivariate Adaptive Regression Splines (MARS) and Particle Swarm Optimization (PSO) for spatial prediction of flash flood susceptible areas. A high frequency tropical typhoon area located on Northwest of Vietnam was selected as a case study. For this purpose, a GIS database for the study areas was prepared, including 654 flash-flood inundations and 12 influencing variables (elevation, slope, curvature, toposhade, aspect, topographic wetness index, stream power index, stream density, Normalized Difference Vegetation Index, soil type, lithology, and rainfall), which were compiled from various sources. The database was used to build and verify the prediction model. We assessed the model's performance through various indices including Classification Accuracy Rate, Area under the Curve (AUC), Precision, and Recall. We also compared the model's usability with five state-of-the-art machine learning techniques including the Backpropagation Neural Network, Support Vector Machine, and Classification Tree. The results revealed that the hybrid PSO-MARS model outperformed other benchmark models in all the employed statistical measures. We conclude that the proposed model can be particularly suited for flash flood forecasting problems at high frequency tropical typhoon area. [ABSTRACT FROM AUTHOR]

Abstract Understanding spatial patterns of forest fire is of key important for fire danger management and ecological implication. This aim of this study was to propose a new machine learning methodology for analyzing and predicting spatial patterns of forest fire danger with a case study of tropical forest fire at Lao Cai province (Vietnam). For this purpose, a Geographical Information System (GIS) database for the study area was established, including ten influencing factors (slope, aspect, elevation, land use, distance to road, normalized difference vegetation index, rainfall, temperature, wind speed, and humidity) and 257 fire locations. The relevance level of these factors with the forest fire was analyzed and assessed using the Mutual Information algorithm. Then, a new hybrid artificial intelligence model named as MARS-DFP, which was Multivariate Adaptive Regression Splines (MARS) optimized by Differential Flower Pollination (DFP), was proposed and used construct forest fire model for generating spatial patterns of forest fire. MARS is employed to build the forest fire model for generalizing a classification boundary that distinguishes fire and non-fire areas, whereas DFP, a metaheuristic approach, was utilized to optimize the model. Finally, global prediction performance of the model was assessed using Area Under the curve (AUC), Classification Accuracy Rate (CAR), Wilcoxon signed-rank test, and various statistical indices. The result demonstrated that the predictive performance of the MARS-DFP model was high (AUC = 0.91 and CAR = 86.57%) and better to those of other benchmark methods, Backpropagation Artificial Neural Network, Adaptive neuro fuzzy inference system, Radial Basis Function Neural Network. This fact confirms that the newly constructed MARS-DFP model is a promising alternative for spatial prediction of forest fire susceptibility. Highlights • MARS-DFP is proposed for analyzing and predicting spatial patterns of forest fire risk. • MARS-DFP has high performance, providing >90% prediction accuracy for future forest fire. • MARS-DFP outperforms benchmark models i.e. ANN and ANFIS. [ABSTRACT FROM AUTHOR]

Abstract In many engineering projects, the soil compression coefficient is an important parameter used for estimating the settlement of soil layers. The common practice of determining the soil compression coefficient via the oedometer test is time-consuming and expensive. This study proposes a machine learning solution to replace the conventional tests used for obtaining the coefficient of soil compression. The new approach is an integration of the Multi-Layer Perceptron Neural Network (MLP Neural Nets) and Particle Swarm Optimization (PSO). These two computational intelligence methods work synergistically to establish a prediction model of soil compression coefficient. The PSO metaheuristic is employed to optimize the MLP Neural Nets model structure. To train and validate the proposed method, named as PSO-MLP Neural Nets, a dataset of 154 soil samples featuring 12 influencing factors has been collected from the geotechnical investigation process of a high-rise building project. Experimental results show that the proposed PSO-MLP Neural Nets has attained the most accurate prediction of the soil compression coefficient performance with RMSE = 0.0267, MAE = 0.0145, and R2 = 0.884. The result of the proposed model is significantly better than those obtained from other benchmark methods including the backpropagation neural network, the radial basis function neural network, the support vector regression, the random forest, and the Gaussian process. Based on the experimental results, the newly constructed PSO-MLP Neural Nets is very potential to be a new alternative to assist geotechnical engineers in design phase of civil engineering projects. [ABSTRACT FROM AUTHOR]

Horizontal displacement of hydropower dams is a typical nonlinear time-varying behavior that is difficult to forecast with high accuracy. This paper proposes a novel hybrid artificial intelligent approach, namely swarm optimized neural fuzzy inference system (SONFIS), for modeling and forecasting of the horizontal displacement of hydropower dams. In the proposed model, neural fuzzy inference system is used to create a regression model whereas Particle swarm optimization is employed to search the best parameters for the model. In this work, time series monitoring data (horizontal displacement, air temperature, upstream reservoir water level, and dam aging) measured for 11 years (1999-2010) of the Hoa Binh hydropower dam were selected as a case study. The data were then split into a ratio of 70:30 for developing and validating the hybrid model. The performance of the resulting model was assessed using RMSE, MAE, and R2. Experimental results show that the proposed SONFIS model performed well on both the training and validation datasets. The results were then compared with those derived from current state-of-the-art benchmark methods using the same data, such as support vector regression, multilayer perceptron neural networks, Gaussian processes, and Random forests. In addition, results from a Different evolution-based neural fuzzy model are included. Since the performance of the SONFIS model outperforms these benchmark models with the monitoring data at hand, the proposed model, therefore, is a promising tool for modeling horizontal displacement of hydropower dams. [ABSTRACT FROM AUTHOR]

In this paper, decision trees machine learning algorithms, namely Random Forest (RF), Alternating Decision Tree (ADT), and Logistic Model Tree (LMT), were applied for modelling of susceptibility of landslides at the Luc Yen district, Northern Vietnam. These methods were evaluated to compare the performance of models and for selection of the best model for landslide susceptibility mapping and prediction. In this study, data of 95 landslides events were analysed with 10 landslide affecting factors using the Correlation-Based Feature Selection (CFS). These factors are land use, elevation, slope, distance to roads, aspect, curvature, distance to faults, rainfall, lithology, and distance to rivers. Receiver Operating Characteristic (ROC) curve, statistical indices (sensitivity, specificity, and kappa), and Chi-square test were utilised for validating and comparing the models performance. The modelling results show that the performance of RF model (AUC = 0.839) is the best with the data at hand compared to the ADT model (0.827) and the LMT (0.809) model. The RF should be applied for the better landslide susceptibility mapping and management. [ABSTRACT FROM AUTHOR]

The objective of this study is to make a comparison of the prediction performance of three techniques, Functional Trees (FT), Multilayer Perceptron Neural Networks (MLP Neural Nets), and Naïve Bayes (NB) for landslide susceptibility assessment at the Uttarakhand Area (India). Firstly, a landslide inventory map with 430 landslide locations in the study area was constructed from various sources. Landslide locations were then randomly split into two parts (i) 70 % landslide locations being used for training models (ii) 30 % landslide locations being employed for validation process. Secondly, a total of eleven landslide conditioning factors including slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to lineaments, distance to rivers, and rainfall were used in the analysis to elucidate the spatial relationship between these factors and landslide occurrences. Feature selection of Linear Support Vector Machine (LSVM) algorithm was employed to assess the prediction capability of these conditioning factors on landslide models. Subsequently, the NB, MLP Neural Nets, and FT models were constructed using training dataset. Finally, success rate and predictive rate curves were employed to validate and compare the predictive capability of three used models. Overall, all the three models performed very well for landslide susceptibility assessment. Out of these models, the MLP Neural Nets and the FT models had almost the same predictive capability whereas the MLP Neural Nets (AUC = 0.850) was slightly better than the FT model (AUC = 0.849). The NB model (AUC = 0.838) had the lowest predictive capability compared to other models. Landslide susceptibility maps were final developed using these three models. These maps would be helpful to planners and engineers for the development activities and land-use planning. [ABSTRACT FROM AUTHOR]

The main objective of this study is to produce a landslide susceptibility map for the Lao Cai area (Vietnam) using a new hybrid intelligent method based on least squares support vector machines (LSSVM) and artificial bee colony (ABC) optimization, namely LSSVM-BC. LSSVM and ABC are state-of-the-art soft computing techniques that have been rarely utilized in landslide susceptibility assessment. LSSVM is adopted to develop landslide prediction model whereas ABC was used to optimize the prediction model by identifying an appropriate set of the LSSVM hyper-parameters. To establish the hybrid intelligent method, a GIS database with ten landslide-influencing factors and 340 landslide locations that occurred mainly during the last 20-years was constructed. These historical landslide locations were collected from the existing inventories that sourced from (i) five landslide projects carried out in this study areas before and (ii) interpretations of SPOT satellite images with resolution of 2.5 m. The study area was geographically split into two different parts, with landslides located in the first part was used for building models whereas the other landslides in the second part was used for the model validation. Performance of the LSSVM-BC model was assessed using the receiver operating characteristic (ROC) curve and area under the curve (AUC). Result shows that the prediction power of the model is good with the area under the curve (AUC) = 0.900. Experiments have pointed out the prediction power of the LSSVM-BC is better than that obtained from the popular support vector machines. Therefore, the proposed model is a promising tool for spatial prediction of landslides at the study area. The landslide susceptibility map is useful for landuse planning for the Lao Cai area. [ABSTRACT FROM AUTHOR]

This paper proposes and validates a novel hybrid artificial intelligent approach, named as Particle Swarm Optimized Neural Fuzzy (PSO-NF), for spatial modeling of tropical forest fire susceptibility. In the proposed approach, a Neural Fuzzy inference system (NF) was used to establish the forest fire model whereas Particle Swarm Optimization (PSO) was adopted to investigate the best values for the model parameters. Tropical forest at the province of Lam Dong (Central Highland of Vietnam) was used as a case study. For this purpose, historic forest fires and ten ignition factors (slope, aspect, elevation, land use, Normalized Difference Vegetation Index, distance to road, distance to residence area, temperature, wind speed, and rainfall) were collected from various sources to construct a GIS database, and then, the database was used to develop and validate the proposed model. The performance of the forest model was assessed using the Receiver Operating Characteristic curve, area under the curve (AUC), and several statistical measures. The results showed that the proposed model performs well, both on the training dataset (AUC = 0.932) and the validation dataset (AUC = 0.916). The usability of the proposed model was further assessed through comparisons with those derived from two benchmark state-of-the art machine learning methods, Random Forests (RF) and Support Vector Machine (SVM). Because the performance of the proposed model is better than the two benchmark models, we concluded that the PSO-NF model is a valid alternative tool that should be considered for tropical forest fire susceptibility modeling. The result in this study is useful for forest planning and management in forest fire prone areas. [ABSTRACT FROM AUTHOR]

The main objective of this study is to evaluate and compare the performance of landslide models using machine learning ensemble technique for landslide susceptibility assessment. This technique is a combination of ensemble methods (AdaBoost, Bagging, Dagging, MultiBoost, Rotation Forest, and Random SubSpace) and the base classifier of Multiple Perceptron Neural Networks (MLP Neural Nets). Ensemble techniques have been widely applied in other fields; however, their application is still rare in the assessment of landslide problems. Meanwhile, MLP Neural Nets, which is known as an artificial neural network, has been applied widely and efficiently in landslide problems. In the present study, landslide models of part Himalayan area (India) have been constructed and validated. For the evaluation and comparison of these models, receiver operating characteristic curve and Chi Square test methods have been applied. Overall, all landslide models performed well in landslide susuceptibility assessment but the performance of the MultiBoost model is the highest (AUC = 0.886), followed by Dagging model (AUC = 0.885), the Rotation Forest model (AUC = 0.882), the Bagging and Random SubSpace models (AUC = 0.881), and the AdaBoost model (AUC = 0.876), respectively. Moreover, machine learning ensemble models have improved significantly the performance of the base classifier of MLP Neural Nets (AUC = 0.874). Analysis of results indicates that landslide models using machine learning ensemble frameworks are promising methods which can be used as alternatives of individual base classifiers for landslide susceptibility assessment of other prone areas. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study represents a hybrid intelligence approach based on the differential evolution optimization and Least-Squares Support Vector Machines for shallow landslide prediction, named as DE–LSSVMSLP. The LSSVM is used to establish a landslide prediction model whereas the DE is adopted to search the optimal tuning parameters of the LSSVM model. In this research, a GIS database with 129 historical landslide records in the Quy Hop area (Central Vietnam) has been collected to establish the hybrid model. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to assess the performance of the newly constructed model. Experimental results show that the proposed model has high performances with approximately 82% of AUCs on both training and validating datasets. The model’s results were compared with those obtained from other methods, Support Vector Machines, Multilayer Perceptron Neural Networks, and J48 Decision Trees. The result comparison demonstrates that the DE–LSSVMSLPdeems best suited for the dataset at hand; therefore, the proposed model can be a promising tool for spatial prediction of rainfall-induced shallow landslides for the study area. [ABSTRACT FROM PUBLISHER]

This paper proposes a new artificial intelligence approach based on neural fuzzy inference system and metaheuristic optimization for flood susceptibility modeling, namely MONF. In the new approach, the neural fuzzy inference system was used to create an initial flood susceptibility model and then the model was optimized using two metaheuristic algorithms, Evolutionary Genetic and Particle Swarm Optimization. A high-frequency tropical cyclone area of the Tuong Duong district in Central Vietnam was used as a case study. First, a GIS database for the study area was constructed. The database that includes 76 historical flood inundated areas and ten flood influencing factors was used to develop and validate the proposed model. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Receiver Operating Characteristic (ROC) curve, and area under the ROC curve (AUC) were used to assess the model performance and its prediction capability. Experimental results showed that the proposed model has high performance on both the training (RMSE = 0.306, MAE = 0.094, AUC = 0.962) and validation dataset (RMSE = 0.362, MAE = 0.130, AUC = 0.911). The usability of the proposed model was evaluated by comparing with those obtained from state-of-the art benchmark soft computing techniques such as J48 Decision Tree, Random Forest, Multi-layer Perceptron Neural Network, Support Vector Machine, and Adaptive Neuro Fuzzy Inference System. The results show that the proposed MONF model outperforms the above benchmark models; we conclude that the MONF model is a new alternative tool that should be used in flood susceptibility mapping. The result in this study is useful for planners and decision makers for sustainable management of flood-prone areas. [ABSTRACT FROM AUTHOR]

Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an efficient manner. [ABSTRACT FROM AUTHOR]

Floods remain one of the most devastating weather-induced disasters worldwide, resulting in numerous fatalities each year and severely impacting socio-economic development and the environment. Therefore, the ability to predict flood-prone areas in advance is crucial for effective risk management. The objective of this research is to assess and compare three convolutional neural networks, U-Net, WU-Net, and U-Net++, for spatial prediction of pluvial flood with a case study at a tropical area in the north of Vietnam. They are relative new convolution algorithms developed based on U-shaped architectures. For this task, a geospatial database with 796 historical flood locations and 12 flood indicators was prepared. For training the models, the binary cross-entropy was employed as the loss function, while the Adaptive moment estimation (ADAM) algorithm was used for the optimization of the model parameters, whereas, F1-score and classification accuracy (Acc) were used to assess the performance of the models. The results unequivocally highlight the high performance of the three models, achieving an impressive accuracy rate of 96.01%. The flood susceptibility maps derived from this research possess considerable utility for local authorities, providing valuable insights and information to enhance decision-making processes and facilitate the implementation of effective risk management strategies. [ABSTRACT FROM AUTHOR]

Abstract: This study investigates the potential application of artificial neural networks in landslide susceptibility mapping in the Hoa Binh province of Vietnam. A landslide inventory map of the study area was prepared by combining landslide locations investigated through three projects during the last 10years. Some recent landslide locations were identified based on SPOT satellite images, field surveys, and existing literature. The images have a spatial resolution of 2.5m. Ten landslide conditioning factors were utilized in the multilayer feed-forward neural network analysis: slope, aspect, relief amplitude, lithology, land use, soil type, rainfall, distance to roads, distance to rivers and distance to faults. Two back-propagation training algorithms, Levenberg–Marquardt and Bayesian regularization, were utilized to determine synoptic weights using a training dataset. Relative importance of each landslide conditioning factor was assessed using the above mentioned synoptic weights. The final connection weights obtained in the training phase were applied to the entire study area to produce landslide susceptibility indexes. The results were then imported to a GIS and landslide susceptibility maps were constructed. Landslide locations not used in the training phase were used to verify and compare the results of the landslide susceptibility maps. Finally, the two landslide susceptibility maps were validated using the prediction-rate method. Subsequently, areas under the prediction curves were assessed. The prediction accuracy of landslide susceptibility maps produced by the Bayesian regularization neural network and the Levenberg–Marquardt neural network were 90.3% and 86.1% respectively. These results indicate that the two models seem to have good predictive capability. The Bayesian regularization network model appears more robust and efficient than the Levenberg–Marquardt network model for landslide susceptibility mapping. [Copyright &y& Elsevier]

Abstract: The main objective of this study is to evaluate and compare the results of evidential belief functions and fuzzy logic models for spatial prediction of landslide hazards in the Hoa Binh province of Vietnam, using geographic information systems. First, a landslide inventory map showing the locations of 118 landslides that have occurred during the last ten years was constructed using data from various sources. Then, the landslide inventory was randomly partitioned into training and validation datasets (70% of the known landslide locations were used for training and building the landslide models and the remaining 30% for the model validation). Secondly, nine landslide conditioning factors were selected (i.e., slope, aspect, relief amplitude, lithology, landuse, soil type, distance to roads, distance to rivers and distance to faults). Using these factors, landslide susceptibility index values were calculated using evidential belief functions and fuzzy logic models. Finally, landslide susceptibility maps were validated and compared using the validation dataset that was not used in the model building. The prediction-rate curves and area under the curves were calculated to assess prediction capability. The results show that all the models have good prediction capabilities. The model derived using evidential belief functions has the highest prediction capability. The model derived using fuzzy SUM has the lowest prediction capability. The fuzzy PRODUCT and fuzzy GAMMA models have almost the same prediction capabilities. In general, all the models yield reasonable results that may be used for preliminary landuse planning purposes. [Copyright &y& Elsevier]

Abstract: The objective of this study is to investigate a potential application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Geographic Information System (GIS) as a relatively new approach for landslide susceptibility mapping in the Hoa Binh province of Vietnam. Firstly, a landslide inventory map with a total of 118 landslide locations was constructed from various sources. Then the landslide inventory was randomly split into a testing dataset 70% (82 landslide locations) for training the models and the remaining 30% (36 landslides locations) was used for validation purpose. Ten landslide conditioning factors such as slope, aspect, curvature, lithology, land use, soil type, rainfall, distance to roads, distance to rivers, and distance to faults were considered in the analysis. The hybrid learning algorithm and six different membership functions (Gaussmf, Gauss2mf, Gbellmf, Sigmf, Dsigmf, Psigmf) were applied to generate the landslide susceptibility maps. The validation dataset, which was not considered in the ANFIS modeling process, was used to validate the landslide susceptibility maps using the prediction rate method. The validation results showed that the area under the curve (AUC) for six ANFIS models vary from 0.739 to 0.848. It indicates that the prediction capability depends on the membership functions used in the ANFIS. The models with Sigmf (0.848) and Gaussmf (0.825) have shown the highest prediction capability. The results of this study show that landslide susceptibility mapping in the Hoa Binh province of Vietnam using the ANFIS approach is viable. As far as the performance of the ANFIS approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones of relative susceptibility. [Copyright &y& Elsevier]

In the present paper, three of the main seismic parameters, maximum magnitude -Mmax, b-value, and annual rate -AR, have been studied for the Pyrenees range in southwest Europe by a Geographic Information System (GIS). The main aim of this work is to calculate, represent continuously, and analyze some of the most crucial seismic indicators for this belt. To this end, an updated and homogenized Poissonian earthquake catalog has been generated, where the National Geographic Institute of Spain earthquake catalog has been considered as a starting point. Herein, the details about the catalog compilation, the magnitude homogenization, the declustering of the catalog, and the analysis of the completeness, are exposed. When the catalog has been produced, a GIS tool has been used to drive the parameters' calculations and representations properly. Different grids (0.5 × 0.5° and 1 × 1°) have been created to depict a continuous map of these parameters. The b-value and AR have been obtained that take into account different pairs of magnitude–year of completeness. Mmax has been discretely obtained (by cells). The analysis of the results shows that the Central Pyrenees (mainly from Arudy to Bagnères de Bigorre) present the most pronounced seismicity in the range. [ABSTRACT FROM AUTHOR]

Taking into account the exponential growth of the number of flash-floods events worldwide, the detection of areas prone to these natural hazards is one of the main activities taken in order to mitigate the negative effects of these risk phenomena. In the present paper, new modeling approaches, Alternating Decision Tree (ADT) integrated with IOE (ADT-IOE) and ADT integrated with AHP (ADT-AHP), were proposed for flash-flood susceptibility mapping across the Suha river catchment (Romania). Besides, two stand-alone methods, Index of Entropy (IOE) and Analytical Hierarchy Process (AHP), were also investigated. For this regard, 111 torrential points and 111 non-torrential points along with 8 flash-flood conditioning factors have been involved in the training process of the four models. The quality of the flash-flood models was checked by using the ROC Curve method, classification accuracy (CLA), and Kappa index. The result shows that the two ensemble models, the ADT-IOE (AUC = 0.972, CLC = 86.37%, Kappa statistics = 0.727) and the ADT-AHP (AUC = 0.926, CLA = 87.88%, Kappa statistics = 0.758), have high prediction performance and outperform the other models. Therefore, ADT-IOE and ADT-AHP are new and promising tools for flash-flood susceptibility modeling. Unlabelled Image • Flash-flood potential mapping was done through 2 stand-alone models and 2 ensembles. • AHP, IOE, ADT, and their ensembles were considered flash-flood susceptibility. • Torrential and non-torrential pixels were considered as dependent variables. • ADT-AHP and ADT-AHP ensemble models yield high prediction performance. • The ensemble models are new tools for flash-flood susceptibility mapping. [ABSTRACT FROM AUTHOR]

Unlabelled Image • The performance of DLNN was assessed for flood susceptibility mapping. • DLNN was compared with the MLP-NN and SVM in terms of their performance. • DLNN with ADAM optimization is robust and outperformed other models. • DLNN is a new promising tool for predicting flash flood in prone areas. This research proposes and evaluates a new approach for flash flood susceptibility mapping based on Deep Learning Neural Network (DLNN)) algorithm, with a case study at a high-frequency tropical storm area in the northwest mountainous region of Vietnam. Accordingly, a DLNN structure with 192 neurons in 3 hidden layers was proposed to construct an inference model that predicts different levels of susceptibility to flash flood. The Rectified Linear Unit (ReLU) and the sigmoid were selected as the activate function and the transfer function, respectively, whereas the Adaptive moment estimation (Adam) was used to update and optimize the weights of the DLNN. A database for the study area, which includes factors of elevation, slope, curvature, aspect, stream density, NDVI, soil type, lithology, and rainfall, was established to train and validate the proposed model. Feature selection was carried out for these factors using the Information gain ratio. The results show that the DLNN attains a good prediction accuracy with Classification Accuracy Rate = 92.05%, Positive Predictive Value = 94.55% and Negative Predictive Value = 89.55%. Compared to benchmarks, Multilayer Perceptron Neural Network and Support Vector Machine, the DLNN performs better; therefore, it could be concluded that the proposed hybridization of GIS and deep learning can be a promising tool to assist the government authorities and involving parties in flash flood mitigation and land-use planning. [ABSTRACT FROM AUTHOR]

This editorial summarizes the performance of the special issue entitled Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management, which was published at MDPI's Remote Sensing journal. The special issue took place in years 2018 and 2019 and accepted a total of nine papers from authors of thirteen different countries. So far, these papers have dealt with 116 cites. Earthquakes, landslides, floods, wildfire and soil salinity were the topics analyzed. New methods were introduced, with applications of the utmost relevance. [ABSTRACT FROM AUTHOR]

By the assist of remotely sensed data, this study examines the viability of slope stability monitoring using two novel conventional models. The proposed models are considered to be the combination of neuro-fuzzy (NF) system along with invasive weed optimization (IWO) and elephant herding optimization (EHO) evolutionary techniques. Considering the conditioning factors of land use, lithology, soil type, rainfall, distance to the road, distance to the river, slope degree, elevation, slope aspect, profile curvature, plan curvature, stream power index (SPI), and topographic wetness index (TWI), it is aimed to achieve a reliable approximation of landslide occurrence likelihood for unseen environmental conditions. To this end, after training the proposed EHO-NF and IWO-NF ensembles using training landslide events, their generalization power is evaluated by receiving operating characteristic curves. The results demonstrated around 75% accuracy of prediction for both models; however, the IWO-NF achieved a better understanding of landslide distribution pattern. Due to the successful performance of the implemented models, they could be promising alternatives to mathematical and analytical approaches being used for discerning the relationship between the slope failure and environmental parameters. [ABSTRACT FROM AUTHOR]

• Proposed a hybrid method for landslide susceptibility mapping. • LSSVC is employed for pattern recognition. • Bat Algorithm is used to optimize the model performance. • A GIS database in Lang Son province (Vietnam) is employed. • The hybrid model has a good prediction performance (CAR = 90.44%). This study develops a machine learning method that hybridizes the Least Squares Support Vector Classification (LSSVC) and Bat Algorithm (BA), named as BA-LSSVC, for spatial prediction of shallow landslide. To construct and verify the hybrid method, a Geographic Information System (GIS) database for the study area of Lang Son province (Vietnam) has been employed. LSSVC is used to separate data samples in the GIS database into two categories of non-landslide (negative class) and landslide (positive class). The BA metaheuristic is employed to assist the LSSVC model selection process by fine-tuning its hyper-parameters: the regularization coefficient and the kernel function parameter. Experimental results point out that the hybrid BA-LSSVC can help to achieve a desired prediction with an accuracy rate of more than 90%. The performance of BA-LSSVC is also better than those of benchmark methods, including the Convolutional Neural Network, Relevance Vector Machine, Artificial Neural Network, and Logistic Regression. Hence, the newly developed model is a capable tool to assist local authority in landslide hazard mitigation and management. [ABSTRACT FROM AUTHOR]

The main goal of this study is to estimate the pullout forces by developing various modelling technique like feedforward neural network (FFNN), radial basis functions neural networks (RBNN), general regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS). A hybrid learning algorithm, including a back-propagation and least square estimation, is utilized to train ANFIS in MATLAB (software). Accordingly, 432 samples have been applied, through which 300 samples have been considered as training dataset with 132 ones for testing dataset. All results have been analyzed by ANFIS, in which the reliability has been confirmed through the comparing of the results. Consequently, regarding FFNN, RBNN, GRNN, and ANFIS, statistical indexes of coefficient of determination (R2), variance account for (VAF) and root mean square error (RMSE) in the values of (0.957, 0.968, 0.939, 0.902, 0.998), (95.677, 96.814, 93.884, 90.131, 97.442) and (2.176, 1.608, 3.001, 4.39, 0.058) have been achieved for training datasets and the values of (0.951, 0.913, 0.729, 0.685 and 0.995), (95.04, 91.13, 72.745, 66.228, 96.247) and (2.433, 4.032, 8.005, 10.188 and 1.252) are for testing datasets indicating a satisfied reliability of ANFIS in estimating of pullout behavior of belled piles. [ABSTRACT FROM AUTHOR]

In this paper, a neuro particle-based optimization of the artificial neural network (ANN) is investigated for slope stability calculation. The results are also compared to another artificial intelligence technique of a conventional ANN and adaptive neuro-fuzzy inference system (ANFIS) training solutions. The database used with 504 training datasets (e.g., a range of 80%) and testing dataset consists of 126 items (e.g., 20% of the whole dataset). Moreover, variables of the ANN method (for example, nodes number for each hidden layer) and the algorithm of PSO-like swarm size and inertia weight are improved by utilizing a total of 28 (i.e., for the PSO-ANN) trial and error approaches. The key properties were fed as input, which were utilized via the analysis of OptumG2 finite element modelling (FEM), containing undrained cohesion stability of the baseline soil (Cu), angle of the original slope (β), and setback distance ratio (b/B) where the target is selected factor of safety. The estimated data for datasets of ANN, ANFIS, and PSO-ANN models were examined based on three determined statistical indexes. Namely, root mean square error (RMSE) and the coefficient of determination (R2). After accomplishing the analysis of sensitivity, considering 72 trials and errors of the neurons number, the optimized architecture of 4 × 6 × 1 was determined to the structure of the ANN model. As an outcome, the employed methods presented excellent efficiency, but based on the ranking method, the PSO-ANN approach might have slightly better efficiency in comparison to the algorithms of ANN and ANFIS. According to statistics, for the proper structure of PSO-ANN, the indexes of R2 and RMSE values of 0.9996, and 0.0123, as well as 0.9994 and 0.0157, were calculated for the training and testing networks. Nevertheless, having the ANN model with six neurons for each hidden layer was formulized for further practical use. This study demonstrates the efficiency of the proposed neuro model of PSO-ANN in estimating the factor of safety compared to other conventional techniques. [ABSTRACT FROM AUTHOR]

In this study, we employed various machine learning-based techniques in predicting factor of safety against slope failures. Different regression methods namely, multi-layer perceptron (MLP), Gaussian process regression (GPR), multiple linear regression (MLR), simple linear regression (SLR), support vector regression (SVR) were used. Traditional methods of slope analysis (e.g., first established in the first half of the twentieth century) used widely as engineering design tools. Offering more progressive design tools, such as machine learning-based predictive algorithms, they draw the attention of many researchers. The main objective of the current study is to evaluate and optimize various machine learning-based and multilinear regression models predicting the safety factor. To prepare training and testing datasets for the predictive models, 630 finite limit equilibrium analysis modelling (i.e., a database including 504 training datasets and 126 testing datasets) were employed on a single-layered cohesive soil layer. The estimated results for the presented database from GPR, MLR, MLP, SLR, and SVR were assessed by various methods. Firstly, the efficiency of applied models was calculated employing various statistical indices. As a result, obtained total scores 20, 35, 50, 10, and 35, respectively for GPR, MLR, MLP, SLR, and SVR, revealed that the MLP outperformed other machine learning-based models. In addition, SVR and MLR presented an almost equal accuracy in estimation, for both training and testing phases. Note that, an acceptable degree of efficiency was obtained for GPR and SLR models. However, GPR showed more precision. Following this, the equation of applied MLP and MLR models (i.e., in their optimal condition) was derived, due to the reliability of their results, to be used in similar slope stability problems. [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]

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]

Fluvial floods endure as one of the most catastrophic weather-induced disasters worldwide, leading to numerous fatalities each year and significantly impacting socio-economic development and the environment. Hence, the research and development of new methods and algorithms focused on improving fluvial flood prediction and devising robust flood management strategies are essential. This study explores and assesses the potential application of 1D-Convolution Neural Networks (1D-CNN) for spatial prediction of fluvial flood in the Quang Nam province, a high-frequency tropical cyclone area in central Vietnam. To this end, a geospatial database with 4156 fluvial flood locations and 12 flood indicators was considered. The ADAM algorithm and the MSE loss function were used to train the 1D-CNN model, whereas popular performance metrics, such as Accuracy (Acc), Kappa, and AUC, were used to measure the performance. The results indicated remarkable performance by the 1D-CNN model, achieving high prediction accuracy with metrics such as Acc = 90.7%, Kappa = 0.814, and AUC = 0.963. Notably, the proposed 1D-CNN model outperformed benchmark models, including DeepNN, SVM, and LR. This achievement underscores the promise and innovation brought by 1D-CNN in the realm of susceptibility mapping for fluvial floods. [ABSTRACT FROM AUTHOR]

Identifying areas with high groundwater spring potential is crucial as it enables better decision-making concerning water supply, sustainable development, and the protection of sensitive ecosystems; therefore, it is necessary to predict the groundwater spring potential with highly accurate models. This study aims to assess and compare the effectiveness of deep neural networks (DeepNNs) and swarm-optimized random forests (SwarmRFs) in predicting groundwater spring potential. This study focuses on a case study conducted in the Gia Lai province, located in the Central Highland of Vietnam. To accomplish this objective, a comprehensive groundwater database was compiled, comprising 938 groundwater spring locations and 12 influential variables, namely land use and land cover (LULC), geology, distance to fault, distance to river, rainfall, normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), normalized difference water index (NDWI), slope, aspect, elevation, and curvature. The DeepNN model was trained and fine-tuned using the Adaptive Moment Estimation (ADAM) optimizer, while the SwarmRF model employed the Harris Hawks Optimizer (HHO) to search for optimal parameters. The results indicate that both the DeepNN model (accuracy = 77.9%, F-score = 0.783, kappa = 0.559, and AUC = 0.820) and the SwarmRF model (accuracy = 80.2%, F-score = 0.798, kappa = 0.605, and AUC = 0.854) exhibit robust predictive capabilities. The SwarmRF model displays a slight advantage over the DeepNN model in terms of performance. Among the 12 influential factors, geology emerges as the most significant determinant of groundwater spring potential. The groundwater spring potential maps generated through this research can offer valuable information for local authorities to facilitate effective water resource management and support sustainable development planning. [ABSTRACT FROM AUTHOR]

The objective of this research is to propose and confirm a new machine learning approach of Best-First tree (BFtree), AdaBoost (AB), MultiBoosting (MB), and Bagging (Bag) ensembles for potential groundwater mapping and assessing role of influencing factors. The Yasuj-Dena area (Iran) is selected as a case study. For this regard, a Yasuj-Dena database was established with 362 springs locations and 12 groundwater-influencing factors (slope, aspect, elevation, stream power index (SPI), length of slope (LS), topographic wetness index (TWI), topographic position index (TPI), land use, lithology, distance from fault, distance from river, and rainfall). The database was employed to train and validate the proposed groundwater models. The area under the curve (AUC) and statistical metrics were employed to check and confirm the quality of the models. The result shows that the BFTree-Bag model (AUC = 0.810, kappa = 0.495) has the highest prediction performance, followed by the BFTree-MB model (AUC = 0.785, kappa = 0.477), and the BFTree-MB model (AUC = 0.745, kappa = 0.422). Compared to the benchmark of Random Forests, the BFTree-Bag model performs better; therefore, we conclude that the BFtree-Bag is a new tool should be used for modeling of groundwater potential. [ABSTRACT FROM AUTHOR]

• A methodology for seafloor topography mapping is proposed. • Data used are satellite gravity anomaly, shipborne gravity anomarly, and shipborne depth. • The accuracy of the seafloor depth determination is better than the available depth models. Seafloor topography mapping has a vital role in territorial management, geological investigation, natural hazard research, and marine construction; therefore, improving the accuracy of seafloor topography mapping is essential. The main objective of this research is to assess the possibility of improving the accuracy of seafloor topographic mapping for the central sea area of Vietnam and its surroundings based on the combination of satellite-derived gravity anomaly data and shipborne data. Firstly, the best satellite-derived gravity anomaly model for the study area is determined by comparing its gravity with the shipborne gravity anomaly. Then, the systematic deviation in the satellite-derived gravity anomaly is eliminated and fitted. Then, the substance density contrast (SDC) between the seabed and the seawater is derived. Subsequently, the seafloor depth was computed using the Gravity Geological method. In the next step, the computed depth is fitted with the shipborne depth to improve the accuracy and reduce the systematic deviation. The result shows that the best satellite-derived gravity anomaly model for the study area is the DTU17GRAV, with an accuracy of ± 5.06 mGal. The most suitable SDC in the study area is 1.40 g/cm3. The final seafloor topographic map with 1′×1′ grid is obtained with the root mean square deviation (RMSD) of ± 83.96 m, which is lower than that of the global terrain model of GEBCO 2022 (RMSD = ±152.88 m). Therefore, it is concluded that the combination of satellite-derived gravity anomaly and shipborne data can significantly improve the accuracy of seafloor mapping for complex topographic areas. The results of this research are helpful for seabed applications and territorial management in the region. [ABSTRACT FROM AUTHOR]

The main objective of this research is to assess the impact of intra-class spectral variation on the accuracy of soft classification and super-resolution mapping. The accuracy of both analyses was negatively related to the degree of intra-class spectral variation, but the effect could be reduced through the use of spectral sub-classes. The latter is illustrated in mapping the shoreline at a sub-pixel scale from Landsat ETM+ data. Reducing the degree of intra-class spectral variation increased the accuracy of soft classification, with the correlation between predicted and actual class coverage rising from 0.87 to 0.94, and super-resolution mapping, with the RMSE in shoreline location decreasing from 41.13 m to 35.22 m. [ABSTRACT FROM AUTHOR]

Abstract Landslides represent a part of the cascade of geological hazards in a wide range of geo-environments. In this study, we aim to investigate and compare the performance of two state-of-the-art machine learning models, i.e., decision tree (DT) and random forest (RF) approaches to model the massive rainfall-triggered landslide occurrences in the Izu-Oshima Volcanic Island, Japan at a regional scale. At first, a landslide inventory map is prepared consisting of 44 landslide polygons (10,444 pixels) from aerial photo-interpretation and field surveys. To estimate the robustness of the models, we randomly adapted two different samples (S1 and S2), comprising of both positive and negative cells (70% of total landslides - 7293 pixels) for training and remaining (30%–3151 pixels) for validation. Twelve causative factors including altitude, slope angle, slope aspect, plan curvature, total curvature, compound topographic index, stream power index, distance to drainage network, drainage density, distance to geological boundaries, lithology and cumulative rainfall were selected as predictors to implement the landslide susceptibility model. The area under the receiver operating characteristics (ROC) curves (AUC) and other statistical signifiers were used to verify the model accuracies. The result shows that the DT and RF models achieved remarkable predictive performance (AUC > 0.9), producing near accurate susceptibility maps. The overall efficiency of RF (AUC = 0.956) is found significantly higher than the DT (AUC = 0.928) results. Additionally, we noticed that the performance of RF for modeling landslide susceptibility is very robust even though the training and validation samples are altered. Considering the performances, we suggest that both RF and DT models can be used in other similar non-eruption-related landslide studies in the tephra-deposited rich volcanoes, as they are capable of rapidly generating accurate and stable LSM maps for risk mitigation, management practices, and decision-making. Moreover, the RF-based model is promising and enough to be recommended as a method to map regional landslide susceptibility. Graphical abstract Unlabelled Image Highlights • Decision tree and random forest models applied to map landslide-prone areas in a volcanic Island. • Two sample set (S1, and S2) for computing the robustness of the model • LSM maps were compared using different assessment principles. • Random forest performs better on both samples with AUC > 0.9. [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid ensemble modeling method called BBO-DE-STreeEns for land-slide susceptibility mapping in Than Uyen district, Vietnam. The method uses subbagging and random subspacing to generate subdatasets for constituent classifiers of the ensemble model, and a split-point and attribute reduced classifier (SPAARC) decision tree algorithm to build each classifier. To optimize hyperparameters of the ensemble model, a hybridization of biogeography-based optimization (BBO) and differential evolution (DE) algorithms is adopted. The land-slide database for the study area includes 114 landslide locations, 114 non-landslide locations, and ten influencing factors: elevation, slope, curvature, aspect, relief amplitude, soil type, geology, distance to faults, distance to roads, and distance to rivers. The database was used to build and verify the BBO-DE-StreeEns model, and standard statistical metrics, namely, positive predictive value (PPV), negative predictive value (NPV), sensitivity (Sen), specificity (Spe), accuracy (Acc), Fscore, Cohen's Kappa, and the area under the ROC curve (AUC), were calculated to evaluate prediction power. Logistic regression, multi-layer perceptron neural network, support vector machine, and SPAARC were used as benchmark models. The results show that the proposed model outperforms the benchmarks with a high prediction power (PPV = 90.3%, NPV = 83.8%, Sen = 82.4%, Spe = 91.2%, Acc = 86.8%, Fscore = 0.862, Kappa = 0.735, and AUC = 0.940). Therefore, the BBO-DE-StreeEns method is a promising tool for landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

A hybrid machine learning approach of Random Subspace (RSS) and Classification And Regression Trees (CART) is proposed to develop a model named RSSCART for spatial prediction of landslides. This model is a combination of the RSS method which is known as an efficient ensemble technique and the CART which is a state of the art classifier. The Luc Yen district of Yen Bai province, a prominent landslide prone area of Viet Nam, was selected for the model development. Performance of the RSSCART model was evaluated through the Receiver Operating Characteristic (ROC) curve, statistical analysis methods, and the Chi Square test. Results were compared with other benchmark landslide models namely Support Vector Machines (SVM), single CART, Naïve Bayes Trees (NBT), and Logistic Regression (LR). In the development of model, ten important landslide affecting factors related with geomorphology, geology and geo-environment were considered namely slope angles, elevation, slope aspect, curvature, lithology, distance to faults, distance to rivers, distance to roads, and rainfall. Performance of the RSSCART model ( AUC = 0.841) is the best compared with other popular landslide models namely SVM (0.835), single CART (0.822), NBT (0.821), and LR (0.723). These results indicate that performance of the RSSCART is a promising method for spatial landslide prediction. [ABSTRACT FROM AUTHOR]

Landslide susceptibility assessment of Uttarakhand area of India has been done by applying five machine learning methods namely Support Vector Machines (SVM), Logistic Regression (LR), Fisher's Linear Discriminant Analysis (FLDA), Bayesian Network (BN), and Naïve Bayes (NB). Performance of these methods has been evaluated using the ROC curve and statistical index based methods. Analysis and comparison of the results show that all five landslide models performed well for landslide susceptibility assessment (AUC = 0.910–0.950). However, it has been observed that the SVM model (AUC = 0.950) has the best performance in comparison to other landslide models, followed by the LR model (AUC = 0.922), the FLDA model (AUC = 0.921), the BN model (AUC = 0.915), and the NB model (AUC = 0.910), respectively. [ABSTRACT FROM AUTHOR]

In this study, a new hybridized machine learning algorithm for urban flood susceptibility mapping, named MultiB-MLPNN, was developed using a multi-boosting technique and MLPNN. The model was tested in Amol City, Iran, a data-scarce city in an ungauged area which is prone to severe flood inundation events and currently lacks flood prevention infrastructure. Performance of the hybridized model was compared with that of a standalone MLPNN model, random forest and boosted regression trees. Area under the curve, efficiency, true skill statistic, Matthews correlation coefficient, misclassification rate, sensitivity and specificity were used to evaluate model performance. In validation, the MultiB-MLPNN model showed the best predictive performance. The hybridized MultiB-MLPNN model is thus useful for generating realistic flood susceptibility maps for data-scarce urban areas. The maps can be used to develop risk-reduction measures to protect urban areas from devastating floods, particularly where available data are insufficient to support physically based hydrological or hydraulic models. [ABSTRACT FROM AUTHOR]

Although the prediction of debris flow-prone areas represents a key step towards reducing damages, modeling debris flow susceptibility is complicated. In addition, the role of debris flow causal drivers in forested mountain landscapes is still poorly understood. To gain a holistic view of the causes of debris flows in the Umyeonsan, Seoul, South Korea region, we coupled the convolutional neural network (CNN) with two evolutionary optimization algorithms – grey wolf optimization (GWO) and cuckoo optimization algorithm (COA). Applying geoinformatics to debris flow factors, debris-flow susceptibility maps were generated and their validities were assessed with receiver operating characteristic (ROC) curves. The results reveal that three causative factors seem to contribute most to debris flows in the study area. The evolutionary optimization algorithms achieved higher goodness-of-fit and predictive power than the standalone CNN model. The goodness-of-fit and predictive skill measures of the CNN susceptibility map were 0.76 and 0.73. The values of CNN hybridized with GWO were 0.81 and 0.81 and hybridized with COA were 0.83 and 0.82. Slope degree, tree age, stream power index, geographical class, and soil drainage were the factors most affecting debris flow likelihood. The CNN-COA is the superior model and it predicted that 40.6% of the study area (i.e., 1844.96 km2) is highly and very highly susceptible to debris flows. The methodology can be applied for analysis of other region to improve risk management and guide development and land use planning. [ABSTRACT FROM AUTHOR]

The iGeoTrans is an iOS application designed for navigation purposes for iPhone, iPad and iPod Touch. This application uses Global Positioning System (GPS), Assisted GPS system, GLONASS, Wi-Fi and Cellular Network for positioning. The iGeoTrans has included datum transformations and map projections that enable users to convert the collected data between different coordinate systems for almost all areas in the world. In addition, other features are also included such as distance, area measurements, and GPS results could be exported to the AutoCad dxf format for GIS softwares. The average horizontal and vertical root mean square errors (RMSEs) for the static test are around 4.11 and 3.51 m, respectively. The horizontal RMSE for the dynamic outdoor test is around 2.72 m. The iGeoTrans application can be used to support surveying, mapping and geosciences fieldworks for any area in the world. [ABSTRACT FROM PUBLISHER]

Marine gravity anomalies are essential data for determining coastal geoid , investigating tectonics and crustal structures, and offshore explorations. The objective of this study is to present a methodology for estimating marine gravity anomalies from CryoSat-2 and Saral/AltiKa satellite altimeter data for the Gulf of Tonkin of Vietnam with a high-resolution 2′ × 2′ grid. A total of 15,665 sea surface height (SSH) grid points, including derived from the Cryosat-2 (6842 grid points) and Saral/AltiKa (8823 grid points) satellite altimeter data were used. Then, the remove-restore technique and the crossover adjustment algorithm were used to remove the long-wavelength geoid height, the mean dynamic topography h MDT , and time-varying sea-surface topography h t . The residual geoid heights Δ N were used to determine the residual gravity anomalies δ g using the Least-Squares Collocation method, whereas the Earth Geopotential Model was employed to restore the long-wavelength gravity anomalies Δ g EIGEN . GPS/leveling and tidal gauge of 31 tidal stations were used for assessing and choosing the best Earth Geopotential Model and Mean Dynamic Topography models for the study area (EIGEN6C4 and DTU15MDT models). The accuracy of the final marine gravity anomaly result was assessed using 56,978 marine gravity points, which were distributed in the study area. The result showed that the standard deviation between the satellite-derived gravity anomalies and checked points is ± 3.36 mGal, indicating good accuracy. After improving with ship-measured gravity anomalies, the accuracy of satellite-derived marine gravity anomaly improves to ± 2.63 mGal. The results of this research are useful for geodetic and geophysical applications in the region. [ABSTRACT FROM AUTHOR]

Floods in urban environments often result in loss of life and destruction of property, with many negative socio-economic effects. However, the application of most flood prediction models still remains challenging due to data scarcity. This creates a need to develop novel hybridized models based on historical urban flood events, using, e.g., metaheuristic optimization algorithms and wavelet analysis. The hybridized models examined in this study (Wavelet-SVR-Bat and Wavelet-SVR-GWO), designed as intelligent systems, consist of a support vector regression (SVR), integrated with a combination of wavelet transform and metaheuristic optimization algorithms, including the grey wolf optimizer (GWO), and the bat optimizer (Bat). The efficiency of the novel hybridized and standalone SVR models for spatial modeling of urban flood inundation was evaluated using different cutoff-dependent and cutoff-independent evaluation criteria, including area under the receiver operating characteristic curve (AUC), Accuracy (A), Matthews Correlation Coefficient (MCC), Misclassification Rate (MR), and F-score. The results demonstrated that both hybridized models had very high performance (Wavelet-SVR-GWO: AUC = 0.981, A = 0.92, MCC = 0.86, MR = 0.07; Wavelet-SVR-Bat: AUC = 0.972, A = 0.88, MCC = 0.76, MR = 0.11) compared with the standalone SVR (AUC = 0.917, A = 0.85, MCC = 0.7, MR = 0.15). Therefore, these hybridized models are a promising, cost-effective method for spatial modeling of urban flood susceptibility and for providing in-depth insights to guide flood preparedness and emergency response services. [ABSTRACT FROM AUTHOR]

• Flood susceptibility was derived through IOE, FAHP, FAHP-IOE, SVM and SVM-IOE models. • 205 flood pixels were the dependent variable into stand-alone and ensemble models. • Between 13% and 20% of the study area has a high and very high flood susceptibility. • SVM-IOE was the most performant model. The global warming and climate changes determined a considerable increase in the frequency of floods and their related damages. Therefore, the high accuracy prediction of flood susceptible areas plays a key role in flood warnings and risk reduction. The main objective of this study is to propose novel hybridizations of fuzzy Analytical Hierarchy Process (FAHP), Index of Entropy (IoE), and Support Vector Machine (SVM) for predicting the areas susceptible to floods. Buzău river catchment (Romania) was the area on which the present study was focused. In this regard, a database with 205 flooded locations, 205 non-flood locations and 12 flood predictors was established and used to train and validate the flood susceptibility models. The performance of the proposed models was evaluated using the Receiver Operating Characteristic (ROC) Curve and statistical metrics. The results show that all the hybrid models have a high prediction performance and outperform the stand-alone models. Among them, the SVM-IoE model (AUC = 0.979) has the highest performance, followed by the FAHP-IoE (AUC = 0.97), IoE (AUC = 0.969), SVM (AUC = 0.966) and FAHP (AUC = 0.947). These results highlight a very high efficiency of all the applied models. The application of the models mentioned above revealed that a percentage between 12.5% (FPI IoE) and 21.2% (FPI FAHP) of the study area is characterized by high and very high exposure to these hydrological hazards. [ABSTRACT FROM AUTHOR]

Regular optimization techniques have been widely used in landslide-related problems. This paper outlines two novel optimizations of artificial neural network (ANN) using grey wolf optimization (GWO) and biogeography-based optimization (BBO) metaheuristic algorithms in the Ardabil province, Iran. To this end, these algorithms are synthesized with a multi-layer perceptron (MLP) neural network for optimizing its computational parameters. The used spatial database consists of fourteen landslide conditioning factors, namely elevation, slope aspect, land use, plan curvature, profile curvature, soil type, distance to river, distance to road, distance to fault, rainfall, slope degree, stream power index (SPI), topographic wetness index (TWI) and lithology. 70% of the identified landslides are randomly selected to train the proposed models and the remaining 30% is used to evaluate the accuracy of them. Also, the frequency ratio theory is used to analyze the spatial interaction between the landslide and conditioning factors. Obtained values of area under the receiver operating characteristic curve, as well as mean square error and mean absolute error showed that both GWO and BBO hybrid algorithms could efficiently improve the learning capability of the MLP. Besides, the BBO-based ensemble surpasses other implemented models. [ABSTRACT FROM AUTHOR]

Nowadays, the crime rate increases dramatically in every country. Therefore, it is an urgent need for governments and social associations to produce persistent solutions and disincentive penalties to prevent crime issues. Specifically, social media plays an important role in crime rate detection; thus, reducing crime rates significantly. It would be a good medium for the desired task. In this paper, we analyze Twitter data collected from Twitter accounts for seven different locations (Ghaziabad, Chennai, Bangaluru, Chandigarh, Jammu, Gujarat, and Hyderabad) from January 2014 to November 2018 in a case study of India, which is opted to illustrate the efficiency of the proposed work. Sentiment analysis has been used to analyze users' behavior and psychology through the tweets of people to track crime actions. Twitter part-of-speech tagger, which is a Markov Model of first-order entropy, has been used for part-of-speech in online conversational text. Brown clustering is used for a long set of unlabeled tweets. Comparisons are verified with real crime rates from an authorized source of information according to different locations. We also measure the latest crime trends for the highest (Ghaziabad, Uttar Pradesh) and lowest crime cities (Jammu) in India. It has been found that the latest crime trends have been recorded for the time duration of 7 days (23, January 2019 to 30, January 2019). The analyses demonstrate that the obtained results match with the real crime rate data. We believe that these types of studies will help to detect the real-time crime rate for different locations and detect the crime pattern easily. [ABSTRACT FROM AUTHOR]