ALBERT

Description: Groundwater-withdrawal-induced land subsidence has been a big concern in Montgomery County, Texas, U.S. since the 2000s. As of 2020, approximately half of the entire county is experiencing subsidence over 5 mm/year. This study aims to investigate ongoing land subsidence in Montgomery County using groundwater-level, extensometer, and GPS datasets. According to this study, land subsidence in Montgomery County since the mid-2000s is primarily contributed by sediment compaction in the Evangeline and Jasper aquifers; the compaction of Jasper aquifer contributes approximately one-third of the land subsidence since the mid-2000s; the pre-consolidation heads of the Chicot, Evangeline, and Jasper aquifers in Montgomery County are close to each other, approximately 15–25 m below mean sea level; the virgin-compaction/head-decline ratio is approximately 1:250 in the Evangeline aquifer and 1:800 in the Jasper aquifer in central and southern Montgomery County. As of 2020, the Jasper groundwater-level altitude is approximately 20–40 m below the pre-consolidation head in the central and southern Montgomery County; the Evangeline groundwater-level altitude is about 40–60 m below the pre-consolidation head. Land subsidence will continue to occur as long as the groundwater-level altitude in either the Evangeline or the Jasper aquifer remains below the pre-consolidation head.

Description: During and just after flash flood, data regarding water extent and inundation will not be available as the traditional data collection methods fail during disasters. Rapid water extent map is vital for disaster responders to identify the areas of immediate need. Real time data available in social networking sites like Twitter and Facebook is a valuable source of information for response and recovery, if handled in an efficient way. This study proposes a method for mining social media content for generating water inundation mapping at the time of flood. The case of 2015 Chennai flood was considered as the disaster event and 95 water height points with geographical coordinates were derived from social media content posted during the flood. 72 points were within Chennai and based on these points water extent map was generated for the Chennai city by interpolation. The water depth map generated from social media information was validated using the field data. The root mean square error between the actual water height data and extracted social media data was ± 0.3 m. The challenge in using social media data is to filter the messages that have water depth related information from the ample amount of messages posted in social media during disasters. Keyword based query was developed and framed in MySQL to filter messages that have location and water height mentions. The query was validated with tweets collected during the floods that hit Mumbai city in July 2019. The validation results confirm that the query reduces the volume of tweets for manual evaluation and in future will aid in mapping the water extent in near real time at the time of floods.

Description: Detailed study of the response of pipelines during seismic excitation can help reduce physical and financial losses during and after an earthquake. The current research investigated the seismic behavior of pipelines passing through variations in topography using two-dimensional and three-dimensional modeling. Their behavior has been modeled at the crest and toe of a slope and during longitudinal passage through the topography. The effects of the soil stiffness, diameter-to-thickness ratio of the pipeline, height-to-half-width ratio (shape factor), and input wave characteristics on the performance of the pipeline have been investigated. The results indicate that topographic effects can increase the strain on pipelines and the factors studied are crucial to accommodating this potential hazard.

Description: Although the Andean region is one of the most landslide-susceptible areas in the world, limited attention has been devoted to the topic in this context in terms of research, risk reduction practice, and urban policy. Based on the collection of landslides data of the Andean city of Quito, Ecuador, this article aims to explore the predictive power of a binary logistic regression model (LOGIT) to test secondary data and an official multicriteria evaluation model for landslide susceptibility in this urban area. Cell size resampling scenarios were explored as a parameter, as the inclusion of new “urban” factors. Furthermore, two types of sensitivity analysis (SA), univariate and Monte Carlo methods, were applied to improve the calibration of the LOGIT model. A Kolmogorov–Smirnov (K-S) test was included to measure the classification power of the models. Charts of the three SA methods helped to visualize the sensitivity of factors in the models. The Area Under the Curve (AUC) was a common metric for validation in this research. Among the ten factors included in the model to help explain landslide susceptibility in the context of Quito, results showed that population and street/road density, as novel “urban factors”, have relevant predicting power for landslide susceptibility in urban areas when adopting data standardization based on weights assigned by experts. The LOGIT was validated with an AUC of 0.79. Sensitivity analyses suggested that calibrations of the best-performance reference model would improve its AUC by up to 0.53%. Further experimentation regarding other methods of data pre-processing and a finer level of disaggregation of input data are suggested. In terms of policy design, the LOGIT model coefficient values suggest the need for a deep analysis of the impacts of urban features, such as population, road density, building footprint, and floor area, at a household scale, on the generation of landslide susceptibility in Andean cities such as Quito. This would help improve the zoning for landslide risk reduction, considering the safety, social and economic impacts that this practice may produce.

Description: Extensive impervious area and the man-made streams are the characteristics of urban areas. In recent years, rapid urbanization has led to change of rural areas into urban areas, and urban runoff will increase as the result of spread and growth of impervious areas. Land use changes, increasing urbanization, unauthorized construction, inefficiency of sewage system and increased impervious surface in urban areas have significant impacts on inundation hazard. Therefore, to manage urban areas and prioritize regions to inundation elimination problems, the area most affected by inundation should be determined. In this study, the Storm Water Management Model (SWMM) is used to simulate the rainfall-runoff in the study area. The simulated runoff in the SWMM model is used as input to the HEC-RAS model and determines inundation hazard zones in 5, 25 and 50 return periods. Then, six factors such as distance from the main channel, slope, land use, drainage density, the main channel slope and elevation were selected to determine inundation hazard map using Analytic Hierarchy Process (AHP). The results showed that the combined model (SWMM and HEC-RAS) was suitable to analyze urban inundation and determine inundation hazard zones on urban areas. Simulated results can be used to develop urban inundation hazard forecasts. In addition, the result of inundation hazard map indicates that 8.2% of the case study is determined as a high hazard zone.

Description: This study presents a method to identify safe places to build temporary accommodation and accessible relief routes using the results of damage analysis for an earthquake within the bounds of probability for the city of Shiraz in Iran. The commonly used HAZUS damage estimation method was used. The most influential factors on the location of safe temporary accommodation in Shiraz were determined by use of the damage results, the Analytic Hierarchy Process (AHP) model, and Expert Choice software. A map for the resulting optimal locations of temporary accommodation was prepared. Subsequently, all of the parameters influencing the safety of emergency evacuation efforts and the relief network were identified and the impact rate of each one was determined based on expert opinions through AHP. Based on the resultant importance of each index, roads were weighted and coded. The optimally safe routes for relief and emergency evacuation were determined. The results suggested that different indices suggest different relief routes and the optimal route was obtained through overlapping the data layers according to the importance of each parameter. This optimal route could provide maximum services in the minimum time and subsequently create capacity building in urban crisis management.

Description: As climate change continues, wildfire outbreaks are becoming more frequent and more difficult to control. In mid-July 2017, a forest fire spread from the forests to the city of Split in Croatia. This unpredictable spread nearly caused emergency systems to collapse. Fortunately, a major tragedy was avoided due to the composure of the responsible services and the help of citizens. Citizens helped to extinguish the fire and provided a large amount of disaster-related information on various social media platforms in a timely manner. In this paper, we addressed the problem of identifying useful Volunteered Geographic Information (VGI) and georeferenced social media crowdsourcing data to improve situational awareness during the forest fire in the city of Split. In addition, social media data were combined with other external data sources (e.g., Sentinel-2 satellite imagery) and authoritative data to establish geographic relationships between wildfire phenomena and social media messages. This article highlights the importance of using georeferenced social media data and provides a different perspective for disaster management by filling gaps in authoritative data. Analyses from the presented reconstruction of events from multiple sources impact a better understanding of these types of events, knowledge sharing, and insights into crowdsourcing processes that can be incorporated into disaster management.

Description: Earthquake is a sudden release of energy due to faults. Natural calamities like earthquakes can neither be predicted nor prevented. However, the severity of the damages can be minimized by development of proper infrastructure which includes microzonation studies, appropriate construction procedures and earthquake resistant designs. The earthquake damaging effect depends on the source, path and site conditions. The earthquake ground motion is affected by topography (slope, hill, valley, canyon, ridge and basin effects), groundwater and surface hydrology. The seismic hazard damages are ground shaking, structural damage, retaining structure failures and lifeline hazards. The medium to large earthquake magnitude (

Description: Natural disasters have been a significant hurdle in the economic growth of middle-income developing countries. Thailand has also been suffering from recurring flood disasters and was most which are severely affected during the 2011 floods. This paper aims to identify the various factors that impact the speed of disaster recovery among the Small and Medium Enterprises (SMEs) severely affected by the floods in Pathumthani province in central Thailand, and how it is related to its speed decision of neighbours SMEs. The methodology adopts a spatial econometric model, to analysis and understand each of the chosen factors’ impact. The findings include the impact of disaster resilience, mitigation and planning at the SME level as well as the government level. The absence of accurate perception of actual risk, flood insurance and disaster management planning before the 2011 floods had contributed to the severity of the impacts during the 2011 floods.