Flood Hazard Zonation Using an Artificial Neural Network Model: A Case Study of Kabul River Basin, Pakistan

Muhammad Saeed, Huan Li, Sami Ullah, Atta-ur Rahman, Amjad Ali, Rehan Khan, Waqas Hassan, Iqra Munir and Shuaib Alam
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Muhammad Saeed: Department of Geography, University of Peshawar, Peshawar 25000, Pakistan
Huan Li: Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, School of Geosciences and Info-Physics, Central South University, 932 South Lushan Road, Yuelu District, Changsha 410083, China
Sami Ullah: Department of Geography, University of Peshawar, Peshawar 25000, Pakistan
Atta-ur Rahman: Department of Geography, University of Peshawar, Peshawar 25000, Pakistan
Amjad Ali: Center for Disaster Preparedness and Management, University of Peshawar, Peshawar 25000, Pakistan
Rehan Khan: Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, School of Geosciences and Info-Physics, Central South University, 932 South Lushan Road, Yuelu District, Changsha 410083, China
Waqas Hassan: National Engineering Research Center for Geographic Information System (NERCGIS), School of Geography, China University of Geosciences, Wuhan 430074, China
Iqra Munir: State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China
Shuaib Alam: Department of Computer Science, National University of Science and Technology, Islamabad 44000, Pakistan

Sustainability, 2021, vol. 13, issue 24, 1-21

Abstract: Floods are the most frequent and destructive natural disasters causing damages to human lives and their properties every year around the world. Pakistan in general and the Peshawar Vale, in particular, is vulnerable to recurrent floods due to its unique physiography. Peshawar Vale is drained by River Kabul and its major tributaries namely, River Swat, River Jindi, River Kalpani, River Budhni and River Bara. Kabul River has a length of approximately 700 km, out of which 560 km is in Afghanistan and the rest falls in Pakistan. Looking at the physiography and prevailing flood characteristics, the development of a flood hazard model is required to provide feedback to decision-makers for the sustainability of the livelihoods of the inhabitants. Peshawar Vale is a flood-prone area, where recurrent flood events have caused damages to standing crops, agricultural land, sources of livelihood earnings and infrastructure. The objective of this study was to determine the effectiveness of the ANN algorithm in the determination of flood inundated areas. The ANN algorithm was implemented in C# for the prediction of inundated areas using nine flood causative factors, that is, drainage network, river discharge, rainfall, slope, flow accumulation, soil, surface geology, flood depth and land use. For the preparation of spatial geodatabases, thematic layers of the drainage network, river discharge, rainfall, slope, flow accumulation, soil, surface geology, flood depth and land use were generated in the GIS environment. A Neural Network of nine, six and one neurons for the first, second and output layers, respectively, were designed and subsequently developed. The output and the resultant product of the Neural Network approach include flood hazard mapping and zonation of the study area. Parallel to this, the performance of the model was evaluated using Root Mean Square Error (RMSE) and Correlation coefficient (R2). This study has further highlighted the applicability and capability of the ANN in flood hazard mapping and zonation. The analysis revealed that the proposed model is an effective and viable approach for flood hazard analysis and zonation.

Keywords: flood; neural network; GIS; zonation; Kabul river (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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