Wollega University, Department of Water Resources and Irrigation Engineering, P.O. Box 395, Nekemte, Ethiopia.
University of Johannesburg, Department of Civil Engineering Sciences, Johannesburg, South Africa.
ScientificWorldJournal. 2021 Nov 22;2021:6128609. doi: 10.1155/2021/6128609. eCollection 2021.
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.
本研究提出了一种新颖的人工智能在地理空间分析中的应用，用于评估埃塞俄比亚迪雷达瓦的洪水脆弱性。洪水成因因素，如降雨量、坡度、土地利用/土地覆被、海拔 NDVI、TWI、SAVI、K 因子、R 因子、河流距离、地貌、道路距离、SPI 和人口密度，被用于训练人工神经网络 (ANN) 模型。ANN 模型生成权重并进行优先级排序。神经网络的初始值被随机分配，并通过前馈过程进行训练。从 2006 年历史洪水事件中收集的实地验证点被用作训练期间的目标数据。在feedforward 中生成的粗略洪水灾害图与实际数据进行比较，误差通过反向传播技术反向传播到神经网络中，并且该步骤重复进行，直到 GIS-ANN 的结果与历史洪水事件之间达成良好的一致。在训练和验证期间，结果与实地验证点的重叠率分别为 88.46%和 89.15%。因此，GIS-ANN 对该城市及其集水区洪水脆弱区的评估应用是成功的。本研究的结果还可以与城市及其集水区一起，用于实际的洪水管理。
