Snow avalanche hazard prediction using machine learning methods

Snow avalanches are among the most destructive natural hazards threatening human life, ecosystems, built structures, and landscapes in mountainous regions. The complexity of snow avalanche modelling has been discussed in many studies, but its modelling is not well-documented. Snow avalanche modeling in this study was done using three main categories of data, including avalanche occurrence locations, meteorological factors, and terrain characteristics. Two machine learning models, namely support vector machine (SVM) and multivariate discriminant analysis (MDA), were employed. A ratio of 70 to 30 of data was considered for calibrating and validating the models. Results indicated that both models had an excellent performance in snow avalanche modeling (area under curve, AUC > 90), although hits and misses analysis demonstrated the superior perfor-mance of MDA. Sensitivity analysis indicated that the topographic position index, slope, precipitation, and topographic wetness index were the most effective variables for modeling. A snow avalanche map indicated that the high snow avalanche hazard zone was mostly near the streams and was matched with hillsides around the water pathways. Findings of study can be helpful for land use planning, to control snow avalanche paths, and to prevent the probable hazards induced by it, and it can be a good reference for future studies on modeling snow avalanche hazards

An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines

Floods, as a catastrophic phenomenon, have a profound impact on ecosystems and human life. Modeling flood susceptibility in watersheds and reducing the damages caused by flooding is an important component of environmental and water management. The current study employs two new algorithms for the first time in flood susceptibility analysis, namely multivariate discriminant analysis (MDA), and classification and regression trees (CART), incorporated with a widely used algorithm, the support vector machine (SVM), to create a flood susceptibility map using an ensemble modeling approach. A flood susceptibility map was developed using these models along with a flood inventory map and flood conditioning factors (including altitude, slope, aspect, curvature, distance from river, topographic wetness index, drainage density, soil depth, soil hydrological groups, land use, and lithology). The case study area was the Khiyav-Chai watershed in Iran. To ensure a more accurate ensemble model, this study proposed a framework for flood susceptibility assessment where only those models with an accuracy of >80% were permissible for use in ensemble modeling. The relative importance of factors was determined using the Jackknife test. Results indicated that the MDA model had the highest predictive accuracy (89%), followed by the SVM (88%) and CART (0.83%) models. Sensitivity analysis showed that slope percent, drainage density, and distance from river were the most important factors in flood susceptibility mapping. The ensemble modeling approach indicated that residential areas at the outlet of the watershed were very susceptible to flooding, and that these areas should, therefore, be prioritized for the prevention and remediation of floods

Flash-flood hazard assessment using Ensembles and Bayesian-based machine learning models:application of the simulated annealing feature selection method

Abstract Flash-floods are increasingly recognized as a frequent natural hazard worldwide. Iran has been among the most devastated regions affected by the major floods. While the temporal flash-flood forecasting models are mainly developed for warning systems, the models for assessing hazardous areas can greatly contribute to adaptation and mitigation policy-making and disaster risk reduction. Former researches in the flash-flood hazard mapping have heightened the urge for the advancement of more accurate models. Thus, the current research proposes the state-of-the-art ensemble models of boosted generalized linear model (GLMBoost) and random forest (RF), and Bayesian generalized linear model (BayesGLM) methods for higher performance modeling. Furthermore, a pre-processing method, namely simulated annealing (SA), is used to eliminate redundant variables from the modeling process. Results of the modeling based on the hit and miss analysis indicates high performance for both models (accuracy= 90−92%, Kappa= 79−84%, Success ratio= 94−96%, Threat score= 80−84%, and Heidke skill score= 79−84%). The variables of distance from the stream, vegetation, drainage density, land use, and elevation have shown more contribution among others for modeling the flash-flood. The results of this study can significantly facilitate mapping the hazardous areas and further assist watershed managers to control and remediate induced damages of flood in the data-scarce regions

Land degradation risk mapping using topographic, human-induced, and geo-environmental variables and machine learning algorithms, for the Pole-Doab watershed, Iran

Abstract Land degradation (LD) is a complex process affected by both anthropogenic and natural driving variables, and its prevention has become an essential task globally. The aim of the present study was to develop a new quantitative LD mapping approach using machine learning techniques, benchmark models, and human-induced and socio-environmental variables. We employed four machine learning algorithms [Support Vector Machine (SVM), Multivariate Adaptive Regression Splines (MARS), Generalized Linear Model (GLM), and Dragonfly Algorithm (DA)] for LD risk mapping, based on topographic (n = 7), human-induced (n = 5), and geo-environmental (n = 6) variables, and field measurements of degradation in the Pole-Doab watershed, Iran. We assessed the performance of different algorithms using receiver operating characteristic, Kappa index, and Taylor diagram. The results revealed that the main topographic, geoenvironmental, and human-induced variable was slope, geology, and land use change, respectively. Assessments of model performance indicated that DA had the highest accuracy and efficiency, with the greatest learning and prediction power in LD risk mapping. In LD risk maps produced using SVM, GLM, MARS, and DA, 19.16%, 19.29%, 21.76%, and 22.40%, respectively, of total area in the Pole-Doab watershed had a very high degradation risk. The results of this study demonstrate that in LD risk mapping for a region, topographic, and geological factors (static conditions) and human activities (dynamic conditions, e.g., residential and industrial area expansion) should be considered together, for best protection at watershed scale. These findings can help policymakers prioritize land and water conservation efforts