Flood Risk Mapping by Remote Sensing Data and Random Forest Technique

Detecting effective parameters in flood occurrence is one of the most important issues that has drawn more attention in recent years. Remote Sensing (RS) and Geographical Information System (GIS) are two efficient ways to spatially predict Flood Risk Mapping (FRM). In this study, a web-based platform called the Google Earth Engine (GEE) (Google Company, Mountain View, CA, USA) was used to obtain flood risk indices for the Galikesh River basin, Northern Iran. With the aid of Landsat 8 satellite imagery and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), 11 risk indices (Elevation (El), Slope (Sl), Slope Aspect (SA), Land Use (LU), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Topographic Wetness Index (TWI), River Distance (RD), Waterway and River Density (WRD), Soil Texture (ST]), and Maximum One-Day Precipitation (M1DP)) were provided. In the next step, all of these indices were imported into ArcMap 10.8 (Esri, West Redlands, CA, USA) software for index normalization and to better visualize the graphical output. Afterward, an intelligent learning machine (Random Forest (RF)), which is a robust data mining technique, was used to compute the importance degree of each index and to obtain the flood hazard map. According to the results, the indices of WRD, RD, M1DP, and El accounted for about 68.27 percent of the total flood risk. Among these indices, the WRD index containing about 23.8 percent of the total risk has the greatest impact on floods. According to FRM mapping, about 21 and 18 percent of the total areas stood at the higher and highest risk areas, respectively.

A GIS-based flood risk mapping of Assam, India, using the MCDA-AHP approach at the regional and administrative level

Floods are hydrological disasters that can alter the physical, socioeconomic, and environmental settings of a region. The objective of the present study is to develop an efficient and reliable methodology to prepare a flood risk map for Assam, the North-eastern region (NER) of India, by the integration of hazard and vulnerability components. Three indices, namely flood hazard index (FHI), flood vulnerability index (FVI), and flood risk index (FRI), are developed using multi-criteria decision analysis (MCDA) – Analytical hierarchy process (AHP) approach in GIS environment for the regional and administrative level of Assam. The selected hazard and vulnerability indicators define the topographical, geological, meteorological, drainage characteristics, land use land cover, and demographical features of Assam. The results show that more than 70% of the total area lies in the moderate to very high FHI class, 57.37% have moderate to high FVI, and more than 50% have moderate to very high FRI class.

Vulnerability Assessment of Urban Floods in Lahore, Pakistan using GIS based integrated Analytical Hierarchy Approach

Urban flooding is getting attention due to its adverse impact on urban lives in mega cities of the developing world particularly Pakistan. This study aims at finding a suitable methodology for mapping urban flooded areas to estimate urban flooding vulnerability risks in the cities of developing countries particularly Lahore, Pakistan. To detect the urban flooded vulnerability and risk areas due to natural disaster, GIS-based integrated Analytical Hierarchy Process (AHP) is applied for the case of Lahore, which is the second most populous city and capital of the Punjab, Pakistan. For the present research, the flood risk mapping is prepared by considering these significant physical factors like elevation, slope, and distribution of rainfall, land use, density of the drainage network, and soil type. Results show that the land use factor is the most significant to detect vulnerable areas near roads and commercial areas. For instance, this method of detection is 88%, 80% and 70% accurate for roads, commercial and residential areas. The methodology implemented in the present research can provide a practical tool and techniques to relevant policy and decision-makers authorities to prioritize and actions to mitigate flood risk and vulnerabilities and identify certain vulnerable urban areas, while formulating a methodology for future urban flood risk and vulnerability mitigation through an objectively simple and organizationally secure approach.

AN EFFICIENT ND-POINT DATA STRUCTURE FOR QUERYING FLOOD RISK

Governments use flood maps for city planning and disaster management to protect people and assets. Flood risk mapping projects carried out for these purposes generate a huge amount of modelling results. Previously, data submitted are highly condensed products such as typical flood inundation maps and tables for loss analysis. Original modelling results recording critical flood evolution processes are overlooked due to cumbersome management and analysis. This certainly has drawbacks: the ‘static’ maps impart few details about the flood; also, the data fails to address new requirements. This significantly confines the use of flood maps. Recent development of point cloud databases provides an opportunity to manage the whole set of modelling results. The databases can efficiently support all kinds of flood risk queries at finer scales. Using a case study from China, this paper demonstrates how a novel nD-PointCloud structure, HistSFC, improves flood risk querying. The result indicates that compared with conventional database solutions, HistSFC holds superior performance and better scalability. Besides, the specific optimizations made on HistSFC can facilitate the process further. All these indicate a promising solution for the next generation of flood maps.

A GIS Multi-Criteria Analysis and Remote Sensing for Flood Risk Management of Bou Salem City, Tunisia

In the context of global warming, it is very critical to delineate areas of high flood vulnerability and risk. Climate and hydrologic surveying using traditional methods is not always available and depends on external factors. So, the use of geographical information system and remote sensing is of high importance as a decision support system. This approach is of low cost and can cover a long period for surveying. This study aims to provide decision makers a framework of GIS based on multicriteria analysis for flood risk mapping. Classified remote sensing image layers are used to complete GIS-multicriteria results. Results show that the high to very high-risk levels affect the majority of the study area, particularly the south-west and north-east zones. The comparison between GIS and remote sensing approaches shows the same areas of risk and reveals that it is a reliable methodology that greatly enhances decision making.