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Machine learning algorithm for flash flood prediction mapping in Wadi El-Laqeita and surroundings, Central Eastern Desert, Egypt

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Abstract

In the work described here, flash flood prediction mapping for the Wadi El-Laqeita in the Central Eastern Desert of Egypt was established, using machine learning approaches involving two algorithms—extreme gradient boosting (XGBoost) and k-nearest neighbor (KNN). Flash flood driving factors, including elevation, slope, curvature, slope-aspect, lithological rock units, distance from streams, stream density, and topographic wetness index (TWI) were selected. Based on the machine learning models, the XGBoost and KNN algorithms were quite similar, in terms of variables importance, with distance from the stream network, slope angle, elevation, and stream density identified as the key driving factors, in order of importance. It is often difficult to allocate model parameter settings; therefore, hyper-parameter setting optimization was applied to improve model prediction performance. The models were trained using 70% flooding location and 70% non-flooding data, with the remaining 30% flooding and 30% non-flooding location data used for model and simulation result validation. The applied models exhibited accuracies of 90.2% and 80.7% for XGBoost and KNN, respectively, showing that the XGBoost algorithm performed better than KNN in this situation. Therefore, XGBoost was used in a powerful approach to flash flood prediction mapping, with the obtained predictions providing important guidance for decision-makers with respect to future study site development.

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Funding

This research is supported by Researchers Supporting Project number RSP-2020/14, King Saud University, Riyadh, Saudi Arabia.

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Authors and Affiliations

  1. Geology Department, Faculty of Science, Suez University, Suez, Egypt

    Sherif Ahmed Abu El-Magd

  2. Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering & IT, University of Technology Sydney, Sydney, NSW, 2007, Australia

    Biswajeet Pradhan

  3. Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia

    Biswajeet Pradhan

  4. Department of Geology & Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia

    Abdullah Alamri

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  1. Sherif Ahmed Abu El-Magd
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  2. Biswajeet Pradhan
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  3. Abdullah Alamri
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Correspondence to Biswajeet Pradhan.

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El-Magd, S.A.A., Pradhan, B. & Alamri, A. Machine learning algorithm for flash flood prediction mapping in Wadi El-Laqeita and surroundings, Central Eastern Desert, Egypt. Arab J Geosci 14, 323 (2021). https://doi.org/10.1007/s12517-021-06466-z

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