Flood Endangered Area Classification Using the K-Nearest Neighbour Algorithm

Preparing for the uncertainty of life is one aspect of the human existence that cannot be over emphasized. With the growth of technology especially the sophisticated nature of data mining and machine learning algorithms, these uncertainties can be predicted, planned and prepared for using existing variables and computer methodologies. The achievements and accomplishments of big data analytics over the past decade in diverse areas called for its implementation in meteorological and space data. Notably, enhancement of the proper management of life’s uncertainties when they eventually occur. This research work focuses on the classification of areas within the Nigerian Geographical territory that are prone to flood using the K-nearest neighbour Algorithm as a classifier. Data from Nigeria Meteorological Agency (NiMET) on seasonal rainfall prediction and temperature of different stations and cities for over three (3) years (2014-2017) was used as a dataset which was trained and classified with the k-Nearest Neighbour algorithm of machine learning. Results showed that some areas are prone to flood considering the historic data of both rainfall and temperature

Integrated Socio-environmental Vulnerability Assessment of Coastal Hazards Using Data-driven and Multi-criteria Analysis Approaches

Coastal hazard vulnerability assessment has been centered around the multi-variate analysis of geo-physical and hydroclimate data. The representation of coupled socio-environmental factors has often been ignored in vulnerability assessment. This study develops an integrated socio-environmental Coastal Vulnerability Index (CVI), which simultaneously combines information from five vulnerability groups: biophysical, hydroclimate, socio-economic, ecological, and shoreline. Using the Multi-Criteria Decision Making (MCDM) approach, two CVI (CVI-50 and CVI-90) have been developed based on average and extreme conditions of the factors. Each CVI is then compared to a data-driven CVI, which is formed based on Probabilistic Principal Component Analysis (PPCA). Both MCDM and PPCA have been tied into geospatial analysis to assess the natural hazard vulnerability of six coastal counties in South Carolina. Despite traditional MCDM-based vulnerability assessments, where the final index is estimated based on subjective weighting methods or equal weights, this study employs an entropy weighting technique to reduce the individuals’ biases in weight assignment. Considering the multivariate nature of the coastal vulnerability, the validation results show both CVI-90 and PPCA preserve the vulnerability results from biophysical and socio-economic factors reasonably, while the CVI-50 methods underestimate the biophysical vulnerability of coastal hazards. Sensitivity analysis of CVIs shows that Charleston County is more sensitive to socio-economic factors, whereas in Horry County the physical factors contribute to a higher degree of vulnerability. Findings from this study suggest that the PPCA technique facilitates the high-dimensional vulnerability assessment, while the MCDM approach accounts more for decision-makers\u27 opinions

Modelling Coastal Vulnerability: An integrated approach to coastal management using Earth Observation techniques in Belize

This thesis presents an adapted method to derive coastal vulnerability through the application of Earth Observation (EO) data in the quantification of forcing variables. A modelled assessment for vulnerability has been produced using the Coastal Vulnerability Index (CVI) approach developed by Gornitz (1991) and enhanced using Machine learning (ML) clustering. ML has been employed to divide the coastline based on the geotechnical conditions observed to establish relative vulnerability. This has been demonstrated to alleviate bias and enhanced the scalability of the approach – especially in areas with poor data coverage – a known hinderance to the CVI approach (Koroglu et al., 2019).Belize provides a demonstrator for this novel methodology due to limited existing data coverage and the recent removal of the Mesoamerican Reef from the International Union for Conservation of Nature (IUCN) List of World Heritage In Danger. A strong characterization of the coastal zone and associated pressures is paramount to support effective management and enhance resilience to ensure this status is retained.Areas of consistent vulnerability have been identified using the KMeans classifier; predominantly Caye Caulker and San Pedro. The ability to automatically scale to conditions in Belize has demonstrated disparities to vulnerability along the coastline and has provided more realistic estimates than the traditional CVI groups. Resulting vulnerability assessments have indicated that 19% of the coastline at the highest risk with a seaward distribution to high risk observed. Using data derived using Sentinel-2, this study has also increased the accuracy of existing habitat maps and enhanced survey coverage of uncharted areas.Results from this investigation have been situated within the ability to enhance community resilience through supporting regional policies. Further research should be completed to test the robust nature of this model through an application in regions with different geographic conditions and with higher resolution input datasets

Supporting metropolitan Venice coastline climate adaptation. A multi-vulnerability and exposure assessment approach

Urban planning for adaptation to climate change privileges the construction of cognitive frameworks developed through the use of new spatial technologies and open-source databases. The significant and most highly innovative aspect concerns how resilience to CC under conditions of vulnerability and risk is defined, monitored and assessed. Based on these premises, this paper aims to explore a new methodology of climate vulnerability, exposure and risk analysis through multicriteria assessment techniques by activating a case study in the coastal municipality of Jesolo (Italy). Taking into consideration three main weather-climate impacts (Urban Flooding, Coastal Flooding and Urban Heat Island) the methodology searches for the best geo-referenced data that can best describe the recognizing impact of the cumulative impact condition through testing a GIS-based multi-attribute exploratory procedure. Intersectoral and multilevel vulnerability conditions at different spatial scales are configured. The analysis methodology continues using open source data (from Open Street Map) to construct local exposure information layers. Exposure combined with spatial vulnerability conditions allows the generation of multi-hazard mapping. Experimentation with multi-hazard climate-oriented spatial assessment can guide planning and public decision-making in new policy domains and target mitigation and adaptation actions in land planning, management and regulation practices. Finally, the proposed methodology can activate stakeholder engagement processes within municipalities to discuss the actual perceived risk and begin a collaborative journey with citizens to identify best practices and solutions to adopt in the areas indicated by the risk mapping

Evaluating multi-hazard risk associated with tropical cyclones using the fuzzy analytic hierarchy process model

Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S2666592123001178#appsec1 .Multi-hazard events have received attention globally due to their increasing frequency and severity in recent years. The coastal region of Bangladesh is particularly vulnerable to multi-hazard events induced by tropical cyclones (TC), including coastal flooding, extreme precipitation, extreme winds, and salinity intrusion. These events inflict substantial damage on human lives and property, yet there has been limited effort to quantitatively assess the associated risks. This study aims to investigate the spatial distribution of multi-hazard risks stemming from TC events, employing a Fuzzy Analytic Hierarchy Process (FAHP) approach. Risk is assessed in relation to hazard, exposure, vulnerability, and mitigation capacities in the study area. Various indicators are selected to define each of these four risk components, with weights determined through expert input for FAHP modeling. The results indicate that more than 50% of the area faces multi-hazard risks, with the hazard component exhibiting the highest degree of risk association, followed by exposure, vulnerability, and adaptive capacity. Storm surge-induced flooding is identified as the most prominent hazard during TC events, followed by intense precipitation, extreme winds, and salinity intrusion. Areas characterized by high population density, a large number of vulnerable populations (e.g., those under 15 years or over 65 years), low elevation, and underdevelopment are found to be the most risk prone. Notably, the presence of hospitals, cyclone centers, and effective warning systems in proximity to an area enhances its potential to withstand multi-hazard impacts. Among the 19 coastal districts, Cox's Bazar and Feni are identified as the most risk prone. The framework and findings presented in this study offer valuable insights for the development and prioritization of multi-hazard risk mitigation policies by identifying the most vulnerable zones and the associated risk factors

Social vulnerability to tropical cyclones: A case study in Muscat Governorate, Oman

Social vulnerability (SV) assessment reveals the hidden weaknesses in the human system that make populations susceptible to loss following exposure to external stress. In this study, SV to natural hazards, such as tropical cyclones, are studied and assessed at the local level for coastal cities in Oman. Vulnerability is determined using the underlying social characteristics specific to people in Oman that put them at risk from cyclones. Oman is a developing country exposed to frequent tropical cyclones that create devastating impacts on its coastal cities, yet disaster risk reduction is undeveloped, with limited understanding of the spatial and temporal distribution of risk and vulnerability, and limited investment in resources and skills in this field. In particular, Oman lacks a natural hazard risk assessment system, hence the response to cyclone events is still reactive and not scientifically based. Some unpublished biophysical vulnerability studies exist that focus mainly on the coastal vulnerability to tsunami in Oman, but there have been no prior studies of SV to natural hazards. In this research, an SV model is adopted and applied at the local level (smallest administration boundary) for four coastal cities in the Muscat capital region. Drawing on a conceptual framework of social vulnerability, based on the work of Susan Cutter, the study identified appropriate SV variables reported by the 2010 census. From a preliminary list of 38 potential variables, 24 variables in 9 social dimensions were selected following exclusion of variables due to multicollinearity and singularity. These variables were then used in a principal component analysis (PCA) to further reduce the number of factors to a few meaningful components/factors/indicators. This process produced three indicators, each consisting of a cluster of variables that make up a construct representative of a vulnerable social group. The subsequent aggregation of these variables created a social vulnerability index (SVI) used in GIS to map the spatial distribution of SV to cyclones across Muscat region. This analysis was then repeated for the 1993 and 2003 censuses, which along with the 2010 analysis, allowed an exploration of the temporal variation of SV over two decades. The results show that for Muscat’s coastal cities, in addition to their exposure to physical hazards, there are clusters of municipal blocks with high SV to cyclones, and others with very low social vulnerability. The level of SV also increases over time. In 1993 there were only three municipal blocks with high SV to cyclones, but by 2010 there were 20 high SV municipal blocks, and a decline in low vulnerability areas. This increase in SV is attributed mainly to an increase in population (particularly rural to urban migration for employment), and an increase in the number of non-Omanis arriving for work, especially those in low wage categories. The study thus demonstrates the need to consider the dynamic nature of SV in natural hazard risk assessment and management. The results can be useful in practice, with the spatial SV maps supporting decision makers in planning and resource allocation before and during an emergency event. The Muscat case study can also be replicated elsewhere in Oman, based on the common nationally available small area data

Hydro-meteorological risk assessment methods and management by nature-based solutions

Hydro-meteorological risk (HMR) management involves a range of methods, such as monitoring of uncertain climate, planning and prevention by technical countermeasures, risk assessment, preparedness for risk by early-warnings, spreading knowledge and awareness, response and recovery. To execute HMR management by risk assessment, many models and tools, ranging from conceptual to sophisticated/numerical methods are currently in use. However, there is still a gap in systematically classifying and documenting them in the field of disaster risk management. This paper discusses various methods used for HMR assessment and its management via potential nature-based solutions (NBS), which are actually lessons learnt from nature. We focused on three hydro-meteorological hazards (HMHs), floods, droughts and heatwaves, and their management by relevant NBS. Different methodologies related to the chosen HMHs are considered with respect to exposure, vulnerability and adaptation interaction of the elements at risk. Two widely used methods for flood risk assessment are fuzzy logic (e.g. fuzzy analytic hierarchy process) and probabilistic methodology (e.g. univariate and multivariate probability distributions). Different kinds of indices have been described in the literature to define drought risk, depending upon the type of drought and the purpose of evaluation. For heatwave risk estimation, mapping of the vulnerable property and population-based on geographical information system is a widely used methodology in addition to a number of computational, mathematical and statistical methods, such as principal component analysis, extreme value theorem, functional data analysis, the Ornstein–Uhlenbeck process and meta-analysis. NBS (blue, green and hybrid infrastructures) are promoted for HMR management. For example, marshes and wetlands in place of dams for flood and drought risk reduction, and green infrastructure for urban cooling and combating heatwaves, are potential NBS. More research is needed into risk assessment and management through NBS, to enhance its wider significance for sustainable living, building adaptations and resilience

Predicting complex system behavior using hybrid modeling and computational intelligence

“Modeling and prediction of complex systems is a challenging problem due to the sub-system interactions and dependencies. This research examines combining various computational intelligence algorithms and modeling techniques to provide insights into these complex processes and allow for better decision making. This hybrid methodology provided additional capabilities to analyze and predict the overall system behavior where a single model cannot be used to understand the complex problem. The systems analyzed here are flooding events and fetal health care. The impact of floods on road infrastructure is investigated using graph theory, agent-based traffic simulation, and Long Short-Term Memory deep learning to predict water level rise from river gauge height. Combined with existing infrastructure models, these techniques provide a 15-minute interval for making closure decisions rather than the current 6-hour interval. The second system explored is fetal monitoring, which is essential to diagnose severe fetal conditions such as acidosis. Support Vector Machine and Random Forest were compared to identify the best model for classification of fetal state. This model provided a more accurate classification than existing research on the CTG. A deep learning forecasting model was developed to predict the future values for fetal heart rate and uterine contractions. The forecasting and classification algorithms are then integrated to evaluate the future condition of the fetus. The final model can predict the fetal state 4 minutes ahead to help the obstetricians to plan necessary interventions for preventing acidosis and asphyxiation. In both cases, time series predictions using hybrid modeling provided superior results to existing methods to predict complex behaviors”--Abstract, page iv

Susceptibility to Changes in Coastal Land Dynamics in Bangladesh

Coastal areas of the world are physically dynamic in nature. The present study contributes new knowledge to studies on coastal land dynamics and land susceptibility to erosion. This study developed a raster GIS-based model namely, Land Susceptibility to Coastal Erosion (LSCE) to assess erosion susceptibility of coastal lands under hydro-climatic changes. The devised model was applied to the entire coastal area of Bangladesh. The model required the characterisation of the nature of land dynamics (i.e. erosion and accretion). The analysis showed a net gain of 237 km² of land over the past thirty years but, constant changes in land dynamics were observed in the area. The study then applied the LSCE model to measure the existing levels of land susceptibility of the coastal area to erosion. The validated model outputs were then used as a baseline for generating four possible scenarios of future land susceptibility to erosion in the coastal area. This allowed the model to ascertain the probable impacts of future hydro-climatic changes on land susceptibility to erosion in the area. Additionally, the study assessed seasonal variations of land susceptibility to erosion by using the same model. The model outputs showed that 276.33 km² of existing coastal lands classified as highly and very highly susceptible to erosion, would substantially increase in the future. Using a Fuzzy Cognitive Mapping (FCM) approach, the study elicited expert views to evaluate the model scenarios and to address uncertainties relevant to erosion susceptibility. This study could allow coastal managers and policymakers to develop effective measures in managing highly erosion susceptible coastal lands in the area