Landslide Inventory, Susceptibility, Hazard and Risk Mapping

Landslide is that the downslope movement of debris, rocks, or earth material under the influence of the force of gravity. Although the causes and mechanisms of landslides are complicated, human action, earthquakes, and severe rainfall can trigger them. It can happen when the driving force surpasses the resisting force due to natural soil or rock slope destabilization. Landslide is one of the foremost destructive and dangerous natural hazards that cause numerous fatalities and economic losses worldwide. Therefore, landslide investigation, susceptibility, hazard, and risk mapping are vital tasks to disaster loss reduction and performance as a suggestion for sustainable land use planning. The determination of the cause variables, identification of existing landslides, and production of a landslide susceptibility, hazard, and risk map are all necessary steps in the mitigation of landslide incidence on the globe. Landslide susceptibility, hazard, and risk maps are the outcome of a statistical relationship between environmental conditions and previously occurring landslides. It provides critical scientific support for the government’s reaction to land use practices and the management of landslide threats. The type, concept of landslides, factor, inventories, susceptibility, hazard, and risk, as well as mapping and validation methodologies, have all been examined in this chapter. The distinction between landslide susceptibility and hazard has surely been debated.

Using the Local Drought Data and GRACE/GRACE-FO Data to Characterize the Drought Events in Mainland China from 2002 to 2020

Accurate quantification of drought characteristics helps to achieve an objective and comprehensive analysis of drought events and to achieve early warning of drought and disaster loss assessment. In our study, a drought characterization approach based on drought severity index derived from Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) data was used to quantify drought characteristics. In order to improve drought detection capability, we used the local drought data as calibration criteria to improve the accuracy of the drought characterization approach to determine the onset of drought. Additionally, the local precipitation data was used to test drought severity determined by the calibrated drought characterization approach. Results show that the drought event probability of detection (POD) of this approach in the four study regions increased by 61.29%, 25%, 94.29%, and 66.86%, respectively, after calibration. We used the calibrated approach to detect the drought events in Mainland China (MC) during 2016 and 2019. The results show that CAR of the four study regions is 100.00%, 92.31%, 100.00%, and 100.00%. Additionally, the precipitation anomaly index (PAI) data was used to evaluate the severity of drought from 2002 to 2020 determined by the calibrated approach. The results indicate that both have a strong similar spatial distribution. Our analysis demonstrates that the proposed approach can serve a useful tool for drought monitoring and characterization.

A County Town in Ruins: Memories, Emotions, and Sense of Place in Post-Earthquake Beichuan, China

Ruins serve as symbolic sites at which to re-examine people’s relationships with the past and bonds with places. In the context of the ruination caused by earthquakes and the displacement and resettlement of local residents post-disaster, this paper explores vernacular (residents’ and survivors’) memories, emotions, and senses of place triggered by the ruins of Beichuan county town, China. Results show vernacular memories of specific ruins were highly fragmented and multi-temporal. Interwoven before- and after-quake memories gave rise to complex emotions, mainly including traumatic feeling of sadness, fear, and painful nostalgia. The study further identifies people’s sense of place towards the ruined county town and finds that locals’ sense of place was not accompanied by the loss of physical dependence to the negative side; locals still expressed high levels of place identity (physical uniqueness, self-esteem, and meanings), place attachment (rootedness and emotional attachment), and positive consequences of place behaviours (protection intention and revisiting) post-earthquake. Moreover, it found that sociodemographic variables of age and length of residence in Beichuan and the variables of disaster loss had significant effect on people’s sense of place. This study balances the overriding focus on visual and representational concerns common in ruin scholarship and further reveals the complex psychological processes impacting on sense of place after large-scale disasters. The findings reflect on the relief practices of post-disaster planning and can serve to guide ruin preservation.

Dynamic Assessment of the Impact of Flood Disaster on Economy and Population under Extreme Rainstorm Events

In the context of climate change and rapid urbanization, flood disaster loss caused by extreme rainstorm events is becoming more and more serious. An accurate assessment of flood disaster loss has become a key issue. In this study, extreme rainstorm scenarios with 50- and 100-year return periods based on the Chicago rain pattern were designed. The dynamic change process of flood disaster loss was obtained by using a 1D–2D coupled model, Hazard Rating (HR) method, machine learning, and ArcPy script. The results show that under extreme rainstorm events, the direct economic loss and affected population account for about 3% of the total GDP and 16% of the total population, respectively, and built-up land is the main disaster area. In addition, the initial time and the peak time of flood disaster loss increases with an increasing flood hazard degree and decreases with the increase in the return period. The total loss increases with the increase in the return period, and the unit loss decreases with the increase in the return period. Compared with a static assessment, a dynamic assessment can better reveal the development law of flood disaster loss, which has great significance for flood risk management and the mitigation of flood disaster loss.

A Method for Prediction of Waterlogging Economic Losses in a Subway Station Project

In order to effectively solve the problems of low prediction accuracy and calculation efficiency of existing methods for estimating economic loss in a subway station engineering project due to rainstorm flooding, a new intelligent prediction model is developed using the sparrow search algorithm (SSA), the least-squares support vector machine (LSSVM) and the mean impact value (MIV) method. First, in this study, 11 input variables are determined from the disaster loss rate and asset value, and a complete method is provided for acquiring and processing data of all variables. Then, the SSA method, with strong optimization ability, fast convergence and few parameters, is used to optimize the kernel function and the penalty factor parameters of the LSSVM. Finally, the MIV is used to identify the important input variables, so as to reduce the predicted input variables and achieve higher calculation accuracy. In addition, 45 station projects in China were selected for empirical analysis. The empirical results revealed that the linear correlation between the 11 input variables and output variables was weak, which demonstrated the necessity of adopting nonlinear analysis methods such as the LSSVM. Compared with other forecasting methods, such as the multiple regression analysis, the backpropagation neural network (BPNN), the BPNN optimized by the particle swarm optimization, the BPNN optimized by the SSA, the LSSVM, the LSSVM optimized by the genetic algorithm, the PSO-LSSVM and the LSSVM optimized by the Grey Wolf Optimizer, the model proposed in this paper had higher accuracy and stability and was effectively used for forecasting economic loss in subway station engineering projects due to rainstorms.

Assessment of the Direct Economic Losses of Flood Disasters Based on the Spatial Valuation of Land Use and Quantification of Vulnerabilities: A Case Study of the 2014 Flood in Lishui city, China

The refined assessment of the direct economic losses of flood disasters is important for emergency dispatch and risk management in small- and medium-sized cities. There are still great challenges in the accuracy and timeliness of the previous research methods. In this study, a single flood disaster in Lishui city in 2014 was taken as an example to study and verify a method for the rapid and refined assessment of direct economic loss. First, based on a field investigation, the inundation range and submerged depth simulated by the flooding model were verified. Next, the urban land use status map and high-precision remote sensing classification data were fused and combined with expert questionnaire surveys, thereby providing the types and values of disaster-bearing bodies. Then, the existing vulnerability curve database was summarized, and the curves were calibrated by disaster loss reporting. Finally, the spatial distributions of the flood disaster loss ratio and loss value were estimated by spatial analysis. It is found that the constructed land use map has detailed types and value attributes as well as high-precision spatial information. Secondly, the vulnerability curves after function fitting and calibration effectively reflect the change characteristics of land use loss ratio in this area. Finally, the estimated loss ratio and loss value distributions can accurately reflect the spatial pattern of flood disaster loss, which is useful for the government to formulate effective disaster reduction and relief measures.

Developing Efficient Web Crawler for Effective Disaster Management

<p>The exponential escalation of disaster loss in our country has led to the awareness that disaster risks are presumably increasing. In the past few years, numerous hazards have been reported in India which has caused severe casualties, infrastructural, agricultural and economic damages. Over the years, researchers have scrutinized social media data for disaster management as it has the advantage of being available in real time and stays relevant in hazard response. But, the authenticity of social media data has been questioned particularly in a disaster management scenario where false information cannot be afforded. Collection of credible disaster statistics during or after a hazard occurrence is a demanding task. Web documents such as a news report are credible when compared to social media data and hence, the proposed work aims in developing a web crawler which is a software that's capable of indexing legitimate news websites from the world wide web which contains news articles related to hazards. The articles are extracted by incorporating the technique of data scraping which includes the use of a developed hazard ontology. The ontology contains hazard relevant keywords at multiple granularities. The developed crawler is able to prioritise websites based on its contents which makes the data collection more accurate. The collected data is  analyzed and structured as it may assist in administering hazard emergencies during a hazard, preparedness before a hazard occurrence and other post disaster activities efficiently. The proposed work also focuses on local media as it may provide news reports from regional locations which might not be reported in the mainstream media.  News articles are written in natural languages and hence structuring them into a statistical form involves natural language processing methodologies. The proposed work mainly focuses on semantic information extraction from news articles to extract statistical data related to the hazard, its impacts and loss.  News illustrations often include less newsworthy content such as advertisements and past studies of the hazard location. Hence, a supervised learning based text classification is performed to classify newsworthy content from the articles and approximately 70% accuracy has been achieved.</p>