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Community-Based Disaster Risk Reduction
Communities are at the core of disaster risk reduction (DRR), and community based approaches are getting increasing focus in national DRR plans. In the case of past disasters, communities were always the first responders, and took leading roles in the post disaster recovery. The roles of communities in pre-disaster preparedness are also very important. This is the first comprehensive book available on CBDRR (community based disaster risk reduction) which outlines both research and practice, citing field examples and research results. It provides an overview of the subject and looks at the role of governments, NGOs, academics and corporate sectors in community based disaster risk reduction. It proceeds to examine experiences from Asian and African countries, and concludes by looking ahead to the future perspective of CBDRR.
eBook
Environment Disaster Linkages
This is one of the first books to focus on explicit linkages between the changing environment and disasters and suggests proactive approaches towards disaster management. A ready-reference for field practitioners it covers areas such as elements of environmental entry, impacts of environment and disaster, strategies, planning and the way forward.
eBook
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
Facing flood disaster: A cluster randomized trial assessing communities’ knowledge, skills and preparedness utilizing a health model intervention
Floods occur when a body of water overflows and submerges normally dry terrain. Tropical cyclones or tsunamis cause flooding. Health and safety are jeopardized during a flood. As a result, proactive flood mitigation measures are required. This study aimed to increase flood disaster preparedness among Selangor communities in Malaysia by implementing a Health Belief Model-Based Intervention (HEBI). Selangor’s six districts were involved in a single-blinded cluster randomized controlled trial Community-wide implementation of a Health Belief Model-Based Intervention (HEBI). A self-administered questionnaire was used. The intervention group received a HEBI module, while the control group received a health talk on non-communicable disease. The baseline variables were compared. Immediate and six-month post-intervention impacts on outcome indicators were assessed. 284 responses with a 100% response rate. At the baseline, there were no significant differences in ethnicity, monthly household income, or past disaster experience between groups (p>0.05). There were significant differences between-group for intervention on knowledge, skills, preparedness (p<0.001), Perceived Benefit Score (p = 0.02), Perceived Barrier Score (p = 0.03), and Cues to Action (p = 0.04). GEE analysis showed receiving the HEBI module had effectively improved knowledge, skills, preparedness, Perceived Benefit Score, Perceived Barrier Score, and Cues to Action in the intervention group after controlling the covariate. Finally, community flood preparedness ensured that every crisis decision had the least impact on humans. The HEBI module improved community flood preparedness by increasing knowledge, skill, preparedness, perceived benefit, perceived barrier, and action cues. As a result, the community should be aware of this module. Clinical trial registration: The trial registry name is Thai Clinical Trials Registry, trial number TCTR20200202002 .
Journal Article
Catastrophe in the making : the engineering of Katrina and the disasters of tomorrow
Based on the false promise of widespread prosperity, communities across the U.S. have embraced all brands of \"economic development\" at all costs. In Louisiana, that meant development interests turning wetlands into shipping lanes. By replacing a natural buffer against storm surges with a 75-mile long, obsolete canal that cost hundreds of millions of dollars, they guided the hurricane into the heart of New Orleans and adjacent communities. The authors reveal why, despite their geographic differences, California and Missouri are building--quite literally--toward similar destruction. Too often, the U.S. \"growth machine\" generates wealth for a few and misery for many. Drawing lessons from the most expensive \"natural\" disaster in American history, Catastrophe in the Making shows why thoughtless development comes at a price we can ill afford.
eBook