Flood Hazard and Natural Risk Assessment: A Case Study of Bangladesh (original) (raw)
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Flood Damage Assessment Geospatial Application Using Geoinformatics and Deep Learning Classification
International Journal of Interactive Mobile Technologies (iJIM)
The data of impacts and damage caused by floods is necessary for manipulation to assist and relieve those impacts in each area. The main issue for data acquisition was acquisition methods that affect the durations, accuracy, and completeness of data obtained. Most data are currently obtained by field survey for data on impacts in each area. However, this method contains limitations, i.e., taking a long time, high cost, and no real-time data visualization. Thus, this research presented the study to develop an application for inspecting areas under impact and damage caused by floods using deep learning classification for flood classification and land use type classification in the affected areas using digital images, remote sensing data, and crowdsource data notified by users through the accuracy assessment application of classification. It was found that deep learning classification for flood classification had 97.50% accuracy, with Kappa = 0.95. Land use type classification had 93.7...
International journal of water resources and environmental engineering, 2013
Flood is a natural hazard resulting from extreme meteorological events which cause an unexpected threat to human lives and properties. Flood stems from the probability that a major hazard event will occur in a well-defined area and it will impacts negatively, in particular on the people and their welfare. Flood protection planning is a very important step which helps not only to rescue the people affected by flood but also to mitigate the effects of these calamitous events and to take necessary preventive measures. Geographic Information System (GIS) is a useful tool to carry out methods of environmental management. With Remote Sensing (RS) techniques, GIS helps in (i) bringing forth hidden patterns in a dataset (ii) performing queries (iii) storing, editing and retrieving (converting) data in maps, (iv) preparing exceedingly ‘expressive’ maps to facilitate survey, spatial modeling, analysis and decision-making. This paper aims to take stock of the GIS capabilities which are particu...
Water Resources …, 2007
Floods are a common feature in rapidly urbanizing Dhaka and its adjoining areas. Though Greater Dhaka experiences flood almost in every year, flood management policies are mostly based on structural options including flood walls, dykes, embankments etc. Many shortcomings of the existing flood management systems are reported in numerous literatures. The objective of this paper is to assess flood hazard in Greater Dhaka for the historical flood event of 1998 using Synthetic Aperture Radar (SAR) data with GIS data. Flood-affected frequency and flood depth calculated from the multi-date SAR imageries were used as hydrologic parameters. Elevation heights, land cover classification, geomorphic division and drainage network data generated from optical remote sensing and analogue maps were used through GIS approach. Using a ranking matrix in three dimensional multiplication mode, flood hazard was assessed. All possible combination of flood hazard maps was prepared using land-cover, geomorphology and elevation heights for flood-affected frequency and floodwater depth. Using two hazard maps which produced the highest congruence for flood frequency and flood depth, a new flood hazard map was developed by considering the interactive effect of flood-affected frequency and floodwater depth, simultaneously. This new hazard map can provide more safety for flood countermeasures because pixels belonging to higher hazard degrees were increased due to the
A Review of Flood Risk Assessment
International Journal of Environment, Agriculture and Biotechnology, 2016
Flooding is considered as one of the most destructive events in many parts of the world in terms of occurrence and distribution, river flooding remains as a common disaster that has to be faced by the civilization in the flood plains. Due to the periodical happening and horrible impact that may be generated, Flood risk management is necessary to conduct. Risk assessment is a necessity for flood risk management. Practically, the majority of the decision-making requires that the risks and costs of all risk mitigation options are evaluated in quantified terms. As a result, a quantitative assessment of potential flood loss is very important, mainly for emergency planning and pre-disaster preparedness. The present review work primarily focuses on the assessment methodologies and an operational approach for assessing the risk of flood loss to the population, crops, housing, and the economy at different Scale's around the world. These techniques are based on hydro-meteorological, socioeconomic and with the combination of those parameters in Geographic Information System (GIS) platform. Concluding that GIS platform appears to be most competent, as it is capable of integrating all the other techniques of flood risk assessment, providing plenty of evidence that the right combination of scientific understanding, experience, forecast and common sense can significantly reduce the risks posed by flood disasters.
Journal of Flood Risk Management, 2021
Flood havoc during 2019 in the Sangu River basin caused widespread damage to residents, crops, roads, and communications in parts of hills in Bangladesh. Developing flood hazard maps can play an essential step in risks management. For this purpose, this study assessed 12 hydro-geomorphological factors, namely, topographic wetness index, elevation, slope, extreme rainfall, land-use and land-cover, soil type, lithology, curvature, drainage density, aspect, height above the nearest drainage, and distance from streams. Maps prepared by individual application of the Analytical Hierarchy Process (AHP) and Analytical Network Process (ANP) exhibit validation scores ranging from 0.77 to 0.79. It is found that the ANP-based model under 1-day maximum rainfall denotes a reliable hazard map presenting comparable accuracy to the field results. The hazard map under 100-year return periods shows that a total of 0.71 million population living downstream is prone to "very high" flood because of its lowland morphology, mild slope, and high drainage density. Alarmingly, 39% of roads, 43% of farming lands, and 25% of education buildings are observed to lie in the highest flood-prone area. Details on subdistrict level exposures have the potential to serve the decision-makers and planners in site selection for flood management strategies and setting priorities for remedial measures. K E Y W O R D S analytical hierarchy process, analytical network process, Bangladesh, flood hazard map, hydro-geomorphological factors, sentinel data 1 | INTRODUCTION Natural hazards have become one of the significant global issues facing humankind (Sui et al., 2018). Among all categories of natural hazards, flooding is one of the widespread, familiar, and regular events (Heidari, 2014; Foudi et al., 2015). Flood events have become more devastating and frequent in the rainy season, especially for subtropical and tropical regions of Asia (Islam and Dharanirajan, 2017), where almost 80% of annual rain falls from June to September. Substantial rainfall along with massive river discharge overflow surrounding land area and initiate flood situation (Mohamed and El-Raey, 2019). A flood turns into a disaster when it results
International Conference on Technologies for Computer, Electrical, Electronics & Communication (ICT-CEEL 2023): Bhaktapur, Nepal, 2023
A flood susceptibility map (FSM) can aid in flood preparedness and planning by identifying potential flooding areas using historical data and conditioning factors. However, traditional flood modeling approaches have limitations due to data availability, computational complexities, and non-linear behavior. In contrast, machine learning algorithms, combined with increased computation capacity and GIS and remote sensing data, have revolutionized flood studies. In the case of Nepali river basins, flood susceptibility mapping using machine learning approaches is very limited or almost not started. This study trained and validated three machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Networks (ANN) using flood incident data across the Karnali River Basin (KRB) of Nepal to fill the gap in understanding the use of machine learning approach for flood studies of Nepal's River basins. The training data set was based on flood inventory data prepared using different flood databases and remote sensing data such as Sentinel-1 SAR imagery. A 15fold cross-validation approach was used to increase the performance of the model. The geospatial database of ten flood conditioning factors used for modeling included aspect, curvature, distance to the river (DTR), normalized difference vegetation index (NDVI), elevation, slope, rainfall, soil, stream power index (SPI), and topographical wetness index (TWI) after multicollinearity and Pearson correlation tests. The variation Inflation Factor (VIF)s are less than 10, and tolerances are higher than 0.1 for all the factors. We used the Area Under the Curve for Receiver Operating Cost (AUROC) for success and prediction rates to test the ML algorithms' performance and validate the models. The prepared susceptibility map was overlayed with different layers, such as buildings and population, to look at the possible impacts of flood in KRB. These findings can aid decision-making processes for flood management and help policymakers prepare and plan effectively to mitigate the impacts of floods.
Geospatial analysis of river flood hazard assessment
E3S Web of Conferences
Floods are one of the most damaging natural disasters which occur frequently in the world. They occur every year in Malaysia due to higher precipitation rates, river meandering, and heavily populated suburban areas. Monsoon rains are the major cause of floods and occur two times per year. The heavy floods in the Kelantan River Basin have shown an increasing trend in recent years. Terrain characteristics of the land and meteorological properties of the region are the main natural factors for this disaster. In this study, the Kuala Krai district of the Kelantan River is selected to be reviewed as the case study for flood risk analysis. Geographical Information System (GIS) integrated with Multicriteria Decision Analysis (MCDA) can be used to evaluate the potential flood risk areas. Historically flooded areas can be extracted from the satellite images to determine flood causing factors for the analysis. At the end of the study, a map of flood risk areas can be generated and validated t...
Flood Risk Assessment Using GIS (Case Study: Golestan Province, Iran)
Polish Journal of Environmental Studies, 2012
In recent years humans have endured increasing numbers of natural disasters, of which flooding is the greatest and most common throughout the world. Iran is also exposed to floods, considering the severe damage recently incurred in Golestan province, particularly Gorganroud watershed. Due to the importance of the subject and lack of comprehensive studies on flood risk in the country's watersheds, it is crucial to perform flood risk assessment using appropriate tools, such as Landsat ETM+ imaging and digital elevation model data collections in geographic information system throughout the region. For this purpose, database maps of 6 subwatersheds in Gorganroud watershed were prepared in 5 layers affecting flooding in the region. By overlaying and weighing three layers in GIS software, a layer of flood hazard intensity was obtained. Next, by means of obtained numbers and scoring, the overuse layer priorities were determined. Then, these two flooding layers were overlaid with the he...
2001
Physiographic divisions, geological divisions, land cover categories and drainage network data were used as GIS components. Flood frequency and floodwater depth were estimated using NOAA AVHRR data for the development of a flood hazard map. The flood hazard map provides information for the development of counter measures and preparation of high risk areas, on a priority basis, against flood damage. It is concluded that the flood hazard map, which was developed by considering the interaction of floodwater depth and flood frequency, gives good results for other events.