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It is clear that flash flood events are on the rise, and the detection of flash flood-prone areas has now become a top priority for governments throughout the world. Flash-flood potential mapping can provide very useful information that can be used in order to adopt the most appropriate measures to reduce the risk of these phenomena. A precise model is needed in order to predict flash flood potential, which can help to improve flood management strategies, as it can be used to develop more effective flood management methods. In this present research, Index of Entropy (IOE), Logistic Regression (LR), four machine learning models (, Support Vector Machine (SVM), Classification and Regression Tree (CART), Naive Bayes (NB), k-Nearest Neighbour (k-NN)) and their 5 stacking ensembles were used to construct a new GIS-based framework for flash flood-potential mapping in the Suha River Basin, Romania. For the purposes of running the models, 8 topographical and environmental factors (slope, land use, plan curvature, profile curvature, convergence index, aspect, hydrological soil groups and tpi), along with 111 torrential locations were used to run the five applied models. In order to facilitate training and validation purposes, the entire dataset was divided into 70:30 parts. ROC Curve and statistical measures were employed in order to verify the model reliability and also validate the results. It is noteworthy here that in the training procedure, the stacking ensemble constructed through the combination of all machine learning and statistical learning models (LCkNS) was better than all the other models with a maximum accuracy of 0.972. The models used, revealed that between 10% and 22% of Suha river basin is occupied by high and very high flash-flood potential. As a result, the data generated by this research provides reliable results that can assist policymakers at the local and national levels in developing a concrete strategic plan to reduce flood frequency in their area by utilizing a flood warning system. [ABSTRACT FROM AUTHOR]

Gully erosion is one of the advanced forms of water erosion. Identifying the effective factors and gully erosion predicting is one of the important tools to control and manage such phenomenon. The main purpose of this study is to evaluate the effect of four different resampling algorithms including cross-validation (5-fold and 10-fold) and bootstrapping (Bootstrap and Optimism bootstrap) on boosted regression tree (BRT), support vector machine (SVM), and random forest (RF) models in spatial modeling and evaluation of head-cut gully erosion in Konduran watershed. For this purpose, based on an extensive field survey, the points of the head-cut of the gully erosion were identified first, and a map of the distribution of head-cut gully erosion in the study area was prepared. Then 18 variable identify and prepare as factors affecting the occurrence of head-cut gully erosion. To assess the efficiency of the models, receiver operating characteristics (ROC) and area under the curve (AUC) were used. The results of the assessment indicated that the use of resampling algorithms increases the efficiency of the models. The integrated optimism-bootstrap-BRT, optimism-bootstrap-SVM, and Optimism-Bootstrap-RF models with AUC 0.85, 0.823 and 0.89 respectively, outperformed the cross-validation 5fold (BRT, SVM, RF), Cross-validation 10fold (BRT, SVM, RF) and Bootstrap (BRT, SVM, RF) integrated algorithms. [ABSTRACT FROM AUTHOR]

Heavy rainfall in mountainous terrain can trigger numerous landslides in hill slopes. These landslides can be deadly to the community living downslope with their fast pace, turning failures into catastrophic debris flows and avalanches. Active tectonics coupled with rugged topography in a complex geoenvironment multiplies this likelihood. The available hazard maps are usually helpful in mitigating disasters. However, fool-proof predicting landslide susceptibility identification remains a challenge in landslide discipline. Recently, ensemble machine learning (ML) techniques have proved the potential to provide a more accurate and efficient solution in spatial modeling. The main purposes of the current study are to examine and evaluate the predictive capability of support vector machine hybrid ensemble ML algorithms, i.e., the bagging, boosting, and stacking for modeling the catastrophic rainfall-induced landslide occurrences in the Northern parts of Kyushu Island, at the watershed scale in Japan. In this study, a landslide inventory map containing 265 landslide polygons was first interpreted from the aerial photographs and fieldwork after the September 2017 rainfall event. The raw data were randomly separated into two parts using a 70/30 sampling strategy for training and validating the landslide models. Then, 13 predisposing factors were prepared as predictors and dependent variable. The landslide susceptibility maps (LSM) were validated by the area under the receiver operating characteristic curve (AUC). The results of validation showed that the AUC values of the four models (SVM-Stacking, SVM, SVM-Bagging, and SVM-Boosting) varied from 0.74 to 0.91. The SVM-boosting model outperformed the other models, while SVM-stacking model has found to be the lowest performance. The outcome suggests that an ensemble ML model does not necessarily mean good performance. It is always preferable to select an appropriate model, such as the one proposed the hybrid novel ensemble SVM-boosting model, which could significantly improve the accuracies of LSM. Also, from Information Gain Ratio (IGR) we found that the rainfall factor mainly affects the results, that agrees with the analogy of present study. [ABSTRACT FROM AUTHOR]

The mass accumulation of population in the larger cities of India has led to accelerated and unprecedented peripheral urban expansion over the last few decades. This rapid peripheral growth is characterized by an uncontrolled, low density, fragmented and haphazard patchwork of development popularly known as urban sprawl. The Kolkata Metropolitan Area (KMA) has been one of the fastest-growing metropolitan areas in India and is experiencing rampant suburbanization and peripheral expansion. Hence, understanding urban growth and its dynamics in these rapidly changing environments is critical for city planners and resource managers. Furthermore, understanding urban expansion and urban growth patterns are essential for achieving inclusive and sustainable urbanization as defined by the United Nations in the Sustainable Development Goals (e.g., SDGs, 11.3). The present research attempts to quantify and model the urban growth dynamics of large and diverse metropolitan areas with a distinct methodology considering the case of KMA. In the study, land use and land cover (LULC) maps of KMA were prepared for three different years (i.e., for 1996, 2006, and 2016) through the classification of Landsat imagery using a support vector machine (SVM) classification approach. Then, change detection analysis, landscape metrics, a concentric zone approach, and Shannon's entropy approach were applied for spatiotemporal assessment and quantification of urban growth in KMA. The achieved classification accuracies were found to be 89.75%, 92.00%, and 92.75%, with corresponding Kappa values of 0.879, 0.904, and 0.912 for 1996, 2006, and 2016, respectively. It is concluded that KMA has been experiencing typical urban sprawl. The peri-urban areas (i.e., KMA-rural) are growing rapidly, and are characterized by leapfrogging and fragmented built-up area development, compared to the central KMA (i.e., KMA-urban), which has become more compact in recent years. [ABSTRACT FROM AUTHOR]

• Effectiveness of trajectory models was examined for storm surge susceptibility. • Historical storm surge inventory map was prepared for using it as a training dataset. • Causative parameters were integrated into frequency ratio models to prepare maps. • Susceptibility maps were validated through support vector machine model. • Support vector machine and modified frequency ratio were found to be effective models. The Sundarban Biosphere Reserve (SBR), which is one of the important coastal regions of India, is vulnerable to storm surge hazards. It experiences storm surge flood of varying magnitudes every year causing immense loss to life and property. Thus, an accurate storm surge flood susceptibility assessment in the Reserve is essential for safeguarding the coastal communities. The present study attempts to explore the effectiveness of the conventional frequency ratio, modified frequency ratio and support vector machine (SVM) models in storm surge flood susceptibility analysis. Core areas of the SBR and some areas along rivers experience very high and high susceptibility. Moderate susceptibility was prevalent in the north, north-western and some areas in the south-western parts of the Reserve while low susceptibility was observed in the western part of the Reserve (located away from the coast). The maps were validated using the receiver operator characteristic (ROC), the seed cell area index (SCAI), and the spatially agreed area approach. The area under the success rate (0.8347) and prediction rate (0.8221) curves was highest for the SVM model. Frequency ratio and modified frequency ratio models showed 71.2% spatially agreed area of susceptibility. The spatially agreed area of susceptibility was lower (65.1%) in case of modified frequency ratio and the SVM model while it was highest (83.2%) between the modified frequency ratio and SVM models. Thus, the SVM model was found to be the best fit model for analyzing storm surge flood susceptibility in SBR. [ABSTRACT FROM AUTHOR]