Abstract
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.
Highlights
Flood is one of the natural hazards that happens when either the capacity of the river bank or the infiltration capacity of soil is less than the intensity of the rainfall [1, 2]. is natural hazard occurs when there is torrential rainfall that lasts for a few minutes/hours, resulting in the overflow of the natural river banks. e natural factors and influences of human activities can derive this natural hazard to happen [3]
Flood vulnerability analysis is a function of different factors such as hydrologic factors (rainfall, stream power index (SPI), and stream networks), morphometric factors, permeability factors (soil type, topographic wetness index (TWI), soil erodibility factor (K), and rainfall erosivity factor (R)), surface dynamics (LULC, soil-adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI)), and anthropogenic influence, and the significance of these factors is prioritized and weights are given in multicriteria analysis (MCA) by the method called the analytical hierarchy process (AHP)
It was observed that the scholars of hydrological modelling intensively used the artificial neural network (ANN) model for flood forecasting worldwide [17,18,19]; the application of this newly emerged approach is very rare in areas of geospatial analysis. e current study area (Dire Dawa) is located in the east-central part of Ethiopia, and it is one of the most flood-prone cities when compared to other cities of the country
Summary
Flood is one of the natural hazards that happens when either the capacity of the river bank or the infiltration capacity of soil is less than the intensity of the rainfall [1, 2]. is natural hazard occurs when there is torrential rainfall that lasts for a few minutes/hours, resulting in the overflow of the natural river banks. e natural factors and influences of human activities can derive this natural hazard to happen [3]. Wahab and Muhamad Ludin [16] assessed flood vulnerability using the ANN model, and a good result was obtained It is the novelty of the ANN model that it can capture the complicated characteristics of both factual and value-based information that cannot be handled by traditional geospatial techniques. It was observed that the scholars of hydrological modelling intensively used the ANN model for flood forecasting worldwide [17,18,19]; the application of this newly emerged approach is very rare in areas of geospatial analysis. Erefore, this study aimed to present the novelty of the ANN model and geospatial analysis to assess the flood vulnerability in Dire Dawa city, Ethiopia As witnessed by [20, 21], the city was flooded due to the torrential rainfall from upstream highlands. e assessment of flood vulnerability using different criteria such as hydrologic, morphometric, permeability, anthropogenic, and surface dynamic change of the city and its watershed is vital as it can provide information about the spatial severity of the flood. erefore, this study aimed to present the novelty of the ANN model and geospatial analysis to assess the flood vulnerability in Dire Dawa city, Ethiopia
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