Search

The vulnerability and flood risk assessment of Bhitarkanika National Park in Odisha, India, was conducted using a data-driven approach and a machine-based embedded decision support system. The park, located in the estuaries of the Brahmani, Baitarani, Dharma, and Mahanadi river systems, is home to India’s second-largest mangrove environment and the world’s most active and diverse saline wetland. To evaluate its vulnerability and risk, various threats were considered, with a focus on floods. Satellite imageries, such as Landsat 8 OLI, SRTM digital elevation model, open street map, Google pro image, reference map, field survey, and other ancillary data, were utilized to develop vulnerability and risk indicators. These indicators were then reclassified into ‘Cost’ and ‘Benefit’ categories for better understanding. The factors were standardized using the max-min standardization method before being fed into the vulnerability and risk model. Initially, an analytical hierarchy approach was used to develop the model, which was later compared with machine learning algorithms (e.g., SVM) and uncertainty analysis indices (e.g., overall accuracy, kappa, map quality, etc.). The results showed that the SVM-RBF machine learning algorithm outperformed the traditional geostatistical model (AHP), with an overall accuracy of 99.54% for flood risk mapping compared to AHP’s 91.12%. The final output reveals that a large area of Bhitarkanika National park falls under high flood risk zone. The Eastern coastal regions of Govindapur, Kanhupur, Chinchri, Gobardhanpur and Barunei fall under high risk zone of tidal floods, The Northern and western regions of Ramachandrapur, Jaganathpur, Kamalpur, Subarnapur, Paramanandapur, etc., Fall under high risk region of riverine floods. The study also revealed that the areas covered with mangroves have a higher elevation and hence are repellent to any kind of flood. In the event of a flood high priority conservation measures should be taken along all high flood risk areas. This study is helpful for decision-making and carrying out programs for the conservation of natural resources and flood management in the national park and reserve forest for ecological sustainability to support sustainable development goals (e.g., SDGs-14, 15).

Spatial information technology has been widely used for vehicles in general and for fleet management. Many studies have focused on improving vehicle positioning accuracy, although few studies have focused on efficiency improvements for managing large truck fleets in the context of the current complex network of roads. Therefore, this paper proposes a multilayer-based map matching algorithm with different spatial data structures to deal rapidly with large amounts of coordinate data. Using the dimension reduction technique, the geodesic coordinates can be transformed into plane coordinates. This study provides multiple layer grouping combinations to deal with complex road networks. We integrated these techniques and employed a puncture method to process the geometric computation with spatial data-mining approaches. We constructed a spatial division index and combined this with the puncture method, which improves the efficiency of the system and can enhance data retrieval efficiency for large truck fleet dispatching. This paper also used a multilayer-based map matching algorithm with raster data structures. Comparing the results revealed that the look-up table method offers the best outcome. The proposed multilayer-based map matching algorithm using the look-up table method is suited to obtaining competitive performance in identifying efficiency improvements for large truck fleet dispatching.

The Linbeiken area is located in the village of Pingding, Taiwan. Since the Mindulle and Aere Typhoons in 2004, and as a result of the landslide triggered by the continuous heavy rainfall on 9 June 2006, there has been a persistent collapse of side slopes in the area. This paper describes the equipment that was installed to collect on-site topographic and hydrological information in the Linbeiken area upstream of the Pingding River and to monitor changes in the landslide area, as well as the measurements that were collected during the 2008 Typhoon Sinlaku. A case study of a landslide in Pingding, Taiwan was used to monitor the accurate coordinate changes in the potential landslide areas during typhoons. The goal of this study was to establish warning indexes, and to strengthen the software and hardware at the local disaster response center in the hope of gaining a full idea of the surface movement in landslide areas in future flood seasons. This is important for boosting the preparedness to adapt to landslide hazards, for improving disaster warnings, and for reporting efficiently to better protect the lives and property of local residents. The results show that the landslide disaster monitoring and warning system in Taiwan, as applied during Typhoon Sinlaku in 2008, is both effective and comprehensive.

Forest fires constitute a major environmental problem in tropical countries, especially in the context of climate change and increasing human populations. This paper aims to identify the causes of frequent forest fires in Son La Province, a fire-prone and forested mountainous region in northwest Vietnam, with a view to constructing a forest fire-related database with multiple layers of natural, social and economic information, extracted largely on the basis of Landsat 7 images. The assessment followed an expert systems approach, applying multi-criteria analysis (MCA) with an analytical hierarchy process (AHP) to determine the weights of the individual parameters related to forest fires. A multi-indicator function with nine parameters was constructed to establish a forest fire risk map at a scale of 1:100,000 for use at the provincial level. The results were verified through regression analysis, yielding R2 = 0.86. A real-time early warning system for forest fire areas has been developed for practical use by the relevant government authorities to provide more effective forest fire prevention planning for Son La Province.

The early-warning model for flash floods is based on a hydrological and geomorphological concept connected to the river basin, with the principle that flash floods will only occur where there is a high potential risk and when rainfall exceeds the threshold. In the model used to build flash-floods risk maps, the parameters of the basin are analyzed and evaluated and the weight is determined using Thomas Saaty’s analytic hierarchy process (AHP). The flash-floods early-warning software is built using open source programming tools. With the spatial module and online processing, a predicted precipitation of one to six days in advance for iMETOS (AgriMedia—Vietnam) automatic meteorological stations is interpolated and then processed with the potential risk maps (iMETOS is a weather-environment monitoring system comprising a wide range of equipment and an online platform and can be used in various fields such as agriculture, tourism and services). The results determine the locations of flash floods at several risk levels corresponding to the predicted rainfall values at the meteorological stations. The system was constructed and applied to flash floods disaster early warning for Thuan Chau in Son La province when the rainfall exceeded the 150 mm/d threshold. The system initially supported positive decision-making to prevent and minimize damage caused by flash floods.

When Typhoon Meiji made landfall in Taiwan in 2010, sudden and heavy rainfall caused disasters in Yilan, Hualien, and other places quickly. The Suhua Highway on the 9th line of Taiwan was the most serious. It caused a large landslide of 50,000 cubic meters at 115K and lost 500 meters of subgrade and 30 meters of subgrade at 116K, causing severe disasters in the local area. Therefore, it was built on Suhua Highway 115.9K. There is an urgent need for monitoring instruments for the large-scale collapse of slopes. To provide real-time automatic rainfall monitoring information and GPS satellite positioning monitoring displacement information, a real-time information system was established to receive the coordinates of points measured by GPS instruments and automatically calculate displacement and movement velocity. At the same time, the radar rainfall forecast data of the "Severe Weather Monitoring System (QPESUMS)" of the Central Meteorological Administration was introduced, and the ground rainfall was used to observe the rainfall correction influence factors to improve the estimation accuracy of the radar rainfall forecast. Through the local automatic rain gauge information, it displays information such as hourly rainfall and accumulated rainfall, develops warning reference values, integrates with the "Disaster Warning Data Exchange" for an automatic notification system, and builds functional modules in the "Highway Disaster Prevention and Relief Decision Support System". (TRENDS)". Integrate the front-end and back-end software and hardware interfaces to establish a complete information system to provide real-time slope information for road management units in the face of disasters as an auxiliary tool for decision-making. [ABSTRACT FROM AUTHOR]

Flooding is the most damaging natural hazard in the world. A better understanding of floods is a fundamental element in guiding the development of policies for managing and reducing flood risk. The main objective of this research is to contribute to the improvement of knowledge on flood susceptibility by combining Fuzzy AHP and Multivariate Analysis. Ha Tinh province in Vietnam was selected as the case study in which many floods have occurred in recent years. To accomplish this goal, 14 factors and 1427 flood locations were used to establish the flood susceptibility model. The area under the curve (AUC) was applied to assess the model accuracy. The results showed that rainfall, NDVI, NDBI, density of river, elevation, slope and aspect are the main factors influencing the occurrence of flooding. The models are efficient in analyzing the flood susceptibility in study area with AUC=0.87. Approximately 36% of the study area was recognized as Very High and High flood susceptibility, which focused on the littoral zone and along the river. The results of this research could be used as a scientific platform for better land use planning and sustainable development in areas highly vulnerable to floods. [ABSTRACT FROM AUTHOR]

Flooding is the most damaging natural hazard in the world. A better understanding of floods is a fundamental element in guiding the development of policies for managing and reducing flood risk. The main objective of this research is to contribute to the improvement of knowledge on flood susceptibility by combining Fuzzy AHP and Multivariate Analysis. Ha Tinh province in Vietnam was selected as the case study in which many floods have occurred in recent years. To accomplish this goal, 14 factors and 1427 flood locations were used to establish the flood susceptibility model. The area under the curve (AUC) was applied to assess the model accuracy. The results showed that rainfall, NDVI, NDBI, density of river, elevation, slope and aspect are the main factors influencing the occurrence of flooding. The models are efficient in analyzing the flood susceptibility in study area with AUC=0.87. Approximately 36% of the study area was recognized as Very High and High flood susceptibility, which focused on the littoral zone and along the river. The results of this research could be used as a scientific platform for better land use planning and sustainable development in areas highly vulnerable to floods.