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Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China
The main goal of this study was to use the synthetic minority oversampling technique (SMOTE) to expand the quantity of landslide samples for machine learning methods (i.e., support vector machine (SVM), logistic regression (LR), artificial neural network (ANN), and random forest (RF)) to produce high-quality landslide susceptibility maps for Lishui City in Zhejiang Province, China. Landslide-related factors were extracted from topographic maps, geological maps, and satellite images. Twelve factors were selected as independent variables using correlation coefficient analysis and the neighborhood rough set (NRS) method. In total, 288 soil landslides were mapped using field surveys, historical records, and satellite images. The landslides were randomly divided into two datasets: 70% of all landslides were selected as the original training dataset and 30% were used for validation. Then, SMOTE was employed to generate datasets with sizes ranging from two to thirty times that of the training dataset to establish and compare the four machine learning methods for landslide susceptibility mapping. In addition, we used slope units to subdivide the terrain to determine the landslide susceptibility. Finally, the landslide susceptibility maps were validated using statistical indexes and the area under the curve (AUC). The results indicated that the performances of the four machine learning methods showed different levels of improvement as the sample sizes increased. The RF model exhibited a more substantial improvement (AUC improved by 24.12%) than did the ANN (18.94%), SVM (17.77%), and LR (3.00%) models. Furthermore, the ANN model achieved the highest predictive ability (AUC = 0.98), followed by the RF (AUC = 0.96), SVM (AUC = 0.94), and LR (AUC = 0.79) models. This approach significantly improves the performance of machine learning techniques for landslide susceptibility mapping, thereby providing a better tool for reducing the impacts of landslide disasters.
Journal Article
Successive landsliding and damming of the Jinsha River in eastern Tibet, China: prime investigation, early warning, and emergency response
Two successive landslides within a month started in October 11, 2018, and dammed twice the Jinsha River at the border between Sichuan Province and Tibet in China. Both events had potential to cause catastrophic flooding that would have disrupted lives of millions and induced significant economic losses. Fortunately, prompt action by local authorities supported by the deployment of a real-time landslide early warning system allowed for quick and safe construction of a spillway to drain the dammed lake. It averted the worst scenario without loss of life and property at least one order of magnitude less to what would have been observed without quick intervention. Particularly, the early warning system was able to predict the second large-scale slope failure 24 h in advance, along with minor rock falls during the spillway construction, avoiding false alerts. This paper presents the main characteristics of both slope collapses and damming processes, and introduces the successful landslide early warning system. Furthermore, we found that the slope endured cumulative creeping displacements of > 40 m in the past decade before the first event. Twenty-five meter displacement occurred in the year immediately before. The deformation was measured by the visual interpretation of multitemporal satellite images, which agrees with the interferometry synthetic aperture radar (InSAR) measurement. If these had been done before the emergency, economic losses could have been reduced further. Therefore, our findings strengthen the case for the deployment of systematic monitoring of potential landslide sites by integrating earth observation methods (i.e., multitemporal satellite or UAV images) and in situ monitoring system as a way to reduce risk. It is expected that this success story can be replicated worldwide, contributing to make our society more resilient to landslide events.
Journal Article
Birnam Wood
The Booker-winning author of The Luminaries delivers a gripping thriller of high drama and kaleidoscopic insight into what drives us to survive -- Provided by publisher.
Book
Erosion-based analysis of breaching of Baige landslide dams on the Jinsha River, China, in 2018
The Yangtze River is one of the most important rivers in China due to its large basin size, the large population along the river, and the numerous large dams and reservoirs on the river. The Jinsha River, the upper reach of the Yangtze River, was dammed twice recently at Baige, Tibet, one on 10 October 2018 and the other on 3 November 2018 (UTC + 8). Accordingly, two large landslide dams, 61 m and 96 m in height to the lowest dam crest, were formed in a 3-week interval. Due to the large inflow rates at the time of damming, the barrier lake level rose rapidly, posing huge risks to the downstream residents and properties. In managing the landslide dam risk, one of the important tasks is to predict the dam breaching flood beforehand. This paper focuses on rapid prediction of the dam breaching hydrograph and breach geometric parameters of the two landslide dams. The predictions were made timely before the breaching of the two landslide dams using both erosion-based empirical equations and numerical simulation and were refined based on detailed field investigation at the site after breaching. Comprehensive field investigations were conducted to determine the geological structures of the landslide dams, characterize the erodibility of dam materials, and measure the final beach dimensions. The simulated dam breaching processes, outflow hydrographs, lake water level changes, and final breach dimensions were validated by field observations. Compared with the hypothetical scenario without a diversion channel on the second landslide dam, a diversion channel 15 m in depth successfully lowered the peak flood discharge by about one third and helped to mitigate the flood risk significantly. The analysis outcome serves as basis for warning and evacuation of the downstream residents and making appropriate engineering risk mitigation plans.
Journal Article
An open dataset for landslides triggered by the 2016 Mw 7.8 Kaikōura earthquake, New Zealand
On November 14, 2016, the northeastern South Island of New Zealand was hit by the magnitude Mw 7.8 Kaikōura earthquake, which is characterized by the most complex rupturing mechanism ever recorded. The widespread landslides triggered by the earthquake make this event a great case study to revisit our current knowledge of earthquake-triggered landslides in terms of factors controlling the spatial distribution of landslides and the rapid assessment of geographic areas affected by widespread landsliding. Although the spatial and size distributions of landslides have already been investigated in the literature, a polygon-based co-seismic landslide inventory with landslide size information is still not available as of June 2021. To address this issue and leverage this large landslide event, we mapped 14,233 landslides over a total area of approximately 14,000 km2. We also identified 101 landslide dams and shared them all via an open-access repository. We examined the spatial distribution of co-seismic landslides in relation to lithologic units and seismic and morphometric characteristics. We analyzed the size statistics of these landslides in a comparative manner, by using the five largest co-seismic landslide inventories ever mapped (i.e., Chi-Chi, Denali, Wenchuan, Haiti, and Gorkha). We compared our inventory with respect to these five ones to answer the question of whether the landslides triggered by the 2016 Kaikōura earthquake are less numerous and/or share size characteristics similar to those of other strong co-seismic landslide events. Our findings show that the spatial distribution of the Kaikōura landslide event is not significantly different from those belonging to other extreme landslide events, but the average landslide size generated by the Kaikōura earthquake is relatively larger compared to some other large earthquakes (i.e., Wenchuan and Gorkha).
Journal Article
Governing affect : neoliberalism and disaster reconstruction
\"Roberto E. Barriospresents an ethnographic study of the aftermaths of four natural disasters: southern Honduras after Hurricane Mitch; New Orleans following Hurricane Katrina; Chiapas, Mexico, after the Grijalva River landslide; and southern Illinois following the Mississippi River flood. Focusing on the role of affect, Barrios examines the ways in which people who live through disasters use emotions as a means of assessing the relevance of governmentally sanctioned recovery plans, judging the effectiveness of such programs, and reflecting on the risk of living in areas that have been deemed prone to disaster. Emotions such as terror, disgust, or sentimental attachment to place all shape the meanings we assign to disasters as well as our political responses to them. The ethnographic cases in Governing Affect highlight how reconstruction programs, government agencies, and recovery experts often view postdisaster contexts as opportune moments to transform disaster-affected communities through principles and practices of modernist and neoliberal development. Governing Affect brings policy and politics into dialogue with human emotion to provide researchers and practitioners with an analytical toolkit for apprehending and addressing issues of difference, voice, and inequity in the aftermath of catastrophes.\"-- Provided by publisher.
Book
Landslide Fatality Occurrence: A Systematic Review of Research Published between January 2010 and March 2022
Landslides triggered by rainfall kill people worldwide, and frequent extreme events that are expected to be an effect of climate change could exacerbate this problem. This review aims to identify recent research, highlighting both the dynamics of landslide accidents and the characteristics of victims. From SCOPUS and WOS databases, using the PRISMA (preferred reporting items for systematic reviews and meta-analysis) approach, 25 articles written in English, published in the January 2010–March 2022 period and focused on landslide fatalities, were mined. The selected articles recognized a worldwide underestimation of landslide fatalities and analyzed landslide mortality from three perspectives, indicating the importance of this topic for a multidisciplinary research community. The papers focused on (a) fatal landslides and their geographic distribution, seasonality, trends, and relationships with socioeconomic indicators; (b) landslide fatalities and their behaviors and the dynamics of accidents; and (c) clinical causes of death or injury types, aiming to improve emergency rescue procedures. The gaps that emerged include (a) the insufficient reuse of valuable fatality databases; (b) the absence of simple take-home messages for citizens, practitioners, schoolteachers, and policymakers, aiming to set educational campaigns and adaptation measures; and (c) the lack of joint research projects between researchers working on landslides and doctors treating victims to provide complete research results that would be able to actually reduce landslide mortality.
Journal Article