Integrating GIS and Analytical Hierarchy Process to Determine Flood Vulnerability Level in Surabaya, Indonesia

Flood is one of the hydrometeorological disasters that occur in Surabaya, especially during the rainy season. The occurrence of floods brings a huge impact on the economy, the environment, and humans’ losses. Based on the National Disaster Management Agency in Indonesia (BNPB) records, the flood inundation height in Surabaya reach about 10 -70 cm for 6 hours. Some anticipation efforts are required to minimize the impact. Therefore, this study aims to provide a flood vulnerability level assessment using the GIS and Analytical Hierarchy Process (AHP) method as a priority recommendation in increasing capacity. This research uses 4 criteria in the assessment including social, economic, physical, and environmental. Each criterion is divided into several classes with adjusted scoring values. The results of the AHP rank the social criteria as the highest weighted value of 0.42322. Furthermore, the results of the flood vulnerability assessment yield some areas that have the highest vulnerability value including Trengilismejoyo District, Wonocolo District, Dukuhpakis District, Sukolilo District, Krembangan District, Semampir District, and Benowo District.

Watershed Flood Vulnerability Assessment Based Land Subsidence Analysed from a Long Time Period of Sentinel-1 Radar Data

Identifying flood vulnerability in an area is part of disaster risk management, especially for mitigating flood events. The level of vulnerability can be recognized from many factors. One of the primary sources of flood vulnerability in urban lowland areas is land subsidence. Sentinel Radar data has been proven to be able to produce land subsidence data accurately. This study was conducted using two Sentinel 1A Radar data acquired on 7 December 2014 and 20 August 2021. Determining the level of flood vulnerability, the analysis of land subsidence is operated at the watershed scale. The highest land subsidence recorded in the Kali Sunter watershed area was 81mm in Kayuputih Village, East Jakarta City. The villages classified as “Extremely Vulnerable” are Sumurbatu in Central Jakarta City, Kayuputih in East Jakarta City in Kelapagading Barat in North Jakarta City and Mekarsari in Depok City.

Artificial Intelligence in Geospatial Analysis for Flood Vulnerability Assessment: A Case of Dire Dawa Watershed, Awash Basin, Ethiopia

This study presents the novelty artificial intelligence in geospatial analysis for flood vulnerability assessment in Dire Dawa, Ethiopia. Flood-causing factors such as rainfall, slope, LULC, elevation NDVI, TWI, SAVI, K-factor, R-factor, river distance, geomorphology, road distance, SPI, and population density were used to train the ANN model. The weights were generated in the ANN model and prioritized. Initial values were randomly assigned to the NN and trained with the feedforward processes. Ground-truthing points collected from the historical flood events of 2006 were used as targeting data during the training. A rough flood hazard map generated in feedforward was compared with the actual data, and the errors were propagated back into the NN with the backpropagation technique, and this step was repeated until a good agreement was made between the result of the GIS-ANN and the historical flood events. The results were overlapped with ground-truthing points at 88.46% and 89.15% agreement during training and validation periods. Therefore, the application of the GIS-ANN for the assessment of flood vulnerable zones for this city and its catchment was successful. The result of this study can also be further considered along with the city and its catchment for practical flood management.

Flood Vulnerability Assessment on Selected Communities in Ikwerre Local Government Area of Rivers State, Nigeria, Using Remote Sensing and GIS Techniques

A flood is an overflow of water that submerges land that is usually dry. Flooding may occur as an overflow of water from water bodies, such as river, lake or ocean, in which the water overtops or breaks levees, resulting in some of that water escaping its usual boundaries, or it may occur due to an accumulation of rain water on saturated ground in an area. To find out the most vulnerable communities, the Digital Elevation Model (DEM) and location data of selected communities were used. The Image Re-class and Map Overlay were performed on QGIS software to identify communities that are most affected. The result shows that the region has no river channel that discharge excess water easily. It also shows that four, out of twelve settlements were located at the high risk zone. The settlements are Uniport, Omuoda, Omuahunwo and Okparagwa. It was recommended that the high risk zone in the area should be provided with artificial water channel that will contain and convey surface sun-off to a nearby stream. The local authority should relocate the affected settlements to a safer zone.

Flood vulnerability assessment using data envelopment analysis – The case of Narmada river basin districts in central India

This paper presents a Data Envelopment Analysis (DEA) based framework for estimating the flood vulnerabilities in River basins. The methodology has been exemplified for the 21 districts of the Narmada River basin in central India. Sensitivity and adaptive capacity indicators have been identified and used for the development of the Flood Vulnerability Index (FVI). DEA based study was employed to assess the Scale Efficiencies and the Returns to Scale and insights drawn from the analysis have been discussed in the context of policy and planning related to reduction of flood vulnerabilities. Cluster analysis has also been deployed to classify districts in terms of flood vulnerabilities. Results from the flood vulnerability assessment model case study indicate that 76% of the districts in the Narmada River Basin remain highly vulnerable to flood-risk, while the socio-economic parameters and physical sizes of districts and their resources play a crucial role.