Urban flood susceptibility evaluation and prediction during 2010–2030 in the southern watersheds of Mashhad city, Iran

Background Urban flood susceptibility evaluation (FSE) can utilize empirical and rational procedures to focus on the urban flood evaluation using physical coefficients and land-use change ratios. The main aim of the present paper was to evaluate a flood susceptibility model in the southern watersheds of Mashhad city, in Iran, for 2010, 2020, and 2030. The construction of the model depended on the utilization of some global datasets to estimate the runoff coefficients of the watersheds, peak flood discharges, and flood susceptibility evaluations. Results and conclusions Based on the climatic precipitation and urban sprawl variation, our results revealed the mean values of the runoff coefficient (Cr) from 0.50 (2010) to 0.65 (2030), where the highest values of Cr (> 0.70) belonged to the watersheds with real estate cover, soil unit of the Mollisols, and the slope ranges over 5–15%. The averagely cumulative flood discharges were estimated from 2.04 m3/s (2010) to 5.76 m3/s (2030), revealing an increase of the flood susceptibility equal 3.2 times with at least requirement of an outlet cross-section by > 46 m2 in 2030. The ROC curves for the model validity explained AUC values averagely over 0.8, exposing the very good performance of the model and excellent sensitivity.

Landslide Susceptibility Assessment Based on Different MaChine Learning Methods in Zhaoping County of Eastern Guangxi

Regarding the ever increasing and frequent occurrence of serious landslide disaster in eastern Guangxi, the current study was implemented to adopt support vector machines (SVM), particle swarm optimization support vector machines (PSO-SVM), random forest (RF), and particle swarm optimization random forest (PSO-RF) methods to assess landslide susceptibility in Zhaoping County. To this end, 10 landslide disaster-related variables including digital elevation model (DEM)-derived, meteorology-derived, Landsat8-derived, geology-derived, and human activities factors were provided. Of 345 landslide disaster locations found, 70% were used to train the models, and the rest of them were performed for model verification. The aforementioned four models were run, and landslide susceptibility evaluation maps were produced. Then, receiver operating characteristics (ROC) curves, statistical analysis, and field investigation were performed to test and verify the efficiency of these models. Analysis and comparison of the results denoted that all four landslide models performed well for the landslide susceptibility evaluation as indicated by the area under curve (AUC) values of ROC curves from 0.863 to 0.934. Among them, it has been shown that the PSO-RF model has the highest accuracy in comparison to other landslide models, followed by the PSO-SVM model, the RF model, and the SVM model. Moreover, the results also showed that the PSO algorithm has a good effect on SVM and RF models. Furthermore, the landslide models devolved in the present study are promising methods that could be transferred to other regions for landslide susceptibility evaluation. In addition, the evaluation results can provide suggestions for disaster reduction and prevention in Zhaoping County of eastern Guangxi.

Susceptibility Assessment of Single Gully Debris Flow Based on AHP and Extension Method

Debris flow mainly happens in mountainous areas all around the world with deadly social and economic impacts. With the speedy development of the mountainous economy, the debris flow susceptibility evaluation in the mountainous areas is of crucial importance for the safety of mountainous life and economy. Yunnan province of China is one of the worst hitting areas by debris flow in the world. In this paper, debris flow susceptibility assessment of Datong and Taicun gully near the first bend of Jinsha River has been done with the help of site investigation and GIS and remote sensing techniques. Eight causative factors, including slope, topographic wetness index, sediments transport index, ground roughness, basin area, bending coefficient, source material, and normalised difference vegetation index, have been selected for debris flow susceptibility evaluation. Analytical hierarchy process combined with Extension method has been used to calculate the susceptibility level of Datong and Taicun gullies. The evaluation result shows that both the gullies have a moderate susceptibility to debris flow. The result suggests that all the ongoing engineering projects such as mining and road construction work should be done with all precautionary measures, and the excavated material should adequately store in the gullies. Doi: 10.28991/cej-2021-03091702 Full Text: PDF

Optimization of Random Forest Model for Assessing and Predicting Geological Hazards Susceptibility in Lingyun County

The random forest (RF) model is improved by the optimization of unbalanced geological hazards dataset, differentiation of continuous geological hazards evaluation factors, sample similarity calculation, and iterative method for finding optimal random characteristics by calculating out-of-bagger errors. The geological hazards susceptibility evaluation model based on optimized RF (OPRF) was established and used to assess the susceptibility for Lingyun County. Then, ROC curve and field investigation were performed to verify the efficiency for different geological hazards susceptibility assessment models. The AUC values for five models were estimated as 0.766, 0.814, 0.842, 0.846 and 0.934, respectively, which indicated that the prediction accuracy of the OPRF model can be as high as 93.4%. This result demonstrated that the geological hazards susceptibility assessment model based on OPRF has the highest prediction accuracy. Furthermore, the OPRF model could be extended to other regions with similar geological environment backgrounds for geological hazards susceptibility assessment and prediction.

Risk management and response process of moraine lakes GLOF in southwestern Tibet (China)

<p>The southwestern part of Tibet in China is one of the hardest-hit areas where Glacier Lake Outburst Flood (GLOF) occurs frequently in the Moraine Lakes of Himalayas. In the face of the increasingly severe GLOF threat of Moraine Lakes, it is urgent to build a risk management and response process of moraine lakes GLOF in this region.  Therefore, we propose a multi-module, process-oriented approach to GLOF risk response (Monitoring-Evaluation-Simulation), which integrates remote sensing, field surveys, Geographic Information Science (GIS), mathematical evaluation models, and hydrodynamic models to carry out the monitoring and analysis of GLOF, susceptibility evaluation, and numerical simulation work in Moraine Lakes.  In the monitoring section (remote sensing and field surveys), we find that typical Moraine Lakes in southwestern Tibet continue to expand in area and are prone to GLOF, which is mainly due to significant area expansion, large-scale ice/avalanches and landslides, and overflow or seepage at the terminal moraine dam. In the assessment part, based on the susceptibility evaluation factor of the glacial lake obtained by monitoring. We creatively use the grey correlation model to filter the GLOF susceptibility evaluation factors, so that the constructed GLOF susceptibility evaluation model has achieved good results (the model evaluation accuracy rate reached 84%, and the AUC value reached 0.874). In the modeling part, the GLOF modeling was carried out for the glacial lakes with high GLOF susceptibility determined by the assessment. It is also the first time that the FLO-2D model is used to construct the GLOF process of a typical Moraine Lake in the Himalayas. The simulation results show the effective simulation capability of the FLO-2D model (the simulated flow depth and flow velocity errors are both within 10%). In short, realizing the organic combination of monitoring, evaluation and simulation are one of the main advantages of the "Monitoring-Evaluation-Simulation" method. This approach effectively supports the prevention and control of GLOF in Moraine Lakes in southwestern Tibet and provides a new application idea for the risk management and response of GLOF in regional Moraine Lakes.</p><p> </p>

A combined procedure to assess rainfall-induced shallow landslide detachment, transit and runout susceptibility using Machine Learning and GIS techniques

<p>Rainfall-induced landslides are notoriously dangerous phenomena which can cause a notable death toll as well as major economic losses globally. Usually, shallow landslides are triggered by prolonged or severe rainfalls and frequently may evolve into potentially catastrophic flow-like movements. Shallow failures are typical in hilly and mountainous areas due to the combination of several predisposing factors such as slope morphology, geological and structural setting, mechanical properties of soils, hydrological and hydrogeological conditions, land-use changes and wildfires. Because of the ability of these phenomena to travel long distances, buildings and infrastructures located in areas improperly deemed safe can be affected.</p><p>Spatial and temporal hazard posed by flow-like movements is due to both source characteristics (e.g., location and volume) and the successive runout dynamics (e.g., travelled paths and distances). Hence, the assessment of shallow landslide susceptibility has to take into account not only the recognition of the most probable landslide source areas, but also  landslide runout (i.e., travel distance). In recent years, a meaningful improvement in landslide detachment susceptibility evaluation has been gained through robust scientific advances, especially by using statistical approaches. Furthermore, various techniques are available for landslide runout susceptibility assessment in quantitative terms. The combination of landslide detachment and runout dynamics has been admitted by many researchers as a suitable and complete procedure for landslide susceptibility evaluation. However, despite its significance, runout assessment is not as widespread in literature as landslide detachment assessment and still remains a challenge for researchers. Currently, only a few studies focus on the assement of both landslide detachment susceptibility (LDS) and landslide runout susceptibility (LRS).</p><p>In this study, the adoption of a combined approach allowed to estimate shallow landslide susceptibility to both detachment and potential runout. Such procedure is based on the integration between LDS assessment via Machine Learning techniques (applying the Ensemble approach) and LRS assessment through GIS-based tools (using the “reach angle” method). This methodology has been applied to the Cinque Terre National Park (Liguria, north-west Italy), where risk posed by flow-like movements is very high. Nine predisposing factors were chosen, while a database of about 300 rainfall-induced shallow landslides was used as input. In particular, the obtained map may be useful for urban and regional planning, as well as for decision-makers and stakeholders, to predict areas that may be affected by rainfall-induced shallow landslides  in the future and to identify areas where risk mitigation measures are needed.</p>