Simulation of flood-prone areas using machine learning and GIS techniques in Samangan Province, Afghanistan
Abstract
Flood events are the most sophisticated and damaging natural hazard compared to other natural catastrophes. Every year, this hazard causes human-financial losses and damage to croplands in different locations worldwide. This research employs a combination of artificial neural networks and geographic information systems (GIS) to simulate flood-vulnerable locations in the Samangan Province of Afghanistan. First, flood-influencing factors, such as soil, slope layer, elevation, flow direction, and land use/cover, were evaluated as influential factors in simulating flood-prone areas. These factors were imported into GIS software. The Fishnet command was used to partition the information layers. Furthermore, each layer was converted into points, and this data was fed into the perceptron neural network along with the educational data obtained from Google Earth. In the perceptron neural network, the input layers have five neurons and 16 nodes, and the outputs showed that elevation had the lowest possible weight (R2 = 0.713) and flow direction had the highest weight (R2 = 0.913). This study demonstrated that combining GIS and artificial neural networks results in acceptable performance for simulating and modeling flood susceptible areas in different geographical locations and significantly helps prevent or reduce flood hazards.
Keyword : Flood, Perceptron artificial neural network, Digital elevation model, Samangan, Afghanistan
How to Cite
Isazade, V., Qasimi, A. B., Al Kafy, A., Dong, P., & Mohammadi, M. (2024). Simulation of flood-prone areas using machine learning and GIS techniques in Samangan Province, Afghanistan. Geodesy and Cartography, 50(1), 20–29. https://doi.org/10.3846/gac.2024.18555
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Costache, R., Pham, Q. B., Sharifi, E., Linh, N. T. T., Abba, S., Vojtek, M., Vojteková, J., Nhi, P. T. T., & Khoi, D. N. (2019). Flash-flood susceptibility assessment using multi-criteria decision making and machine learning supported by remote sensing and GIS techniques. Remote Sensing, 12(1), Article 106. https://doi.org/10.3390/rs12010106
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Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D., Watanabe, S., & Kanae, S. (2013). Global flood risk under climate change. Nature Climate Change, 3(9), 816–821. https://doi.org/10.1038/nclimate1911
Hong, H., Panahi, M., Shirzadi, A., Ma, T., Liu, J., Zhu, A. X., Chen, W., Kougias, I., & Kazakis, N. (2018). Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Science of the Total Environment, 621, 1124–1141. https://doi.org/10.1016/j.scitotenv.2017.10.114
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Khosravi, K., Shahabi, H., Pham, B. T., Adamowski, J., Shirzadi, A., Pradhan, B., Dou, J., Ly, H.-B., Gróf, G., Ho, H. L., Hong, H., Chapi, K., & Prakash, I. (2019). A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. Journal of Hydrology, 573, 311–323. https://doi.org/10.1016/j.jhydrol.2019.03.073
Kourgialas, N. N., & Karatzas, G. P. (2011). Flood management and a GIS modelling method to assess flood-hazard areas – a case study. Hydrological Sciences Journal–Journal des Sciences Hydrologiques, 56(2), 212–225. https://doi.org/10.1080/02626667.2011.555836
Metin, A. D., Dung, N. V., Schröter, K., Vorogushyn, S., Guse, B., Kreibich, H., & Merz, B. (2020). The role of spatial dependence for large-scale flood risk estimation. Natural Hazards and Earth System Sciences, 20(4), 967–979. https://doi.org/10.5194/nhess-20-967-2020
Mishra, K., & Sinha, R. (2020). Flood risk assessment in the Kosi alluvial plains using Analytical Hierarchy Process (AHP) framework, North Bihar, India. Geomorphology, 350, Article 106861. https://doi.org/10.1016/j.geomorph.2019.106861
Mukerji, A., Chatterjee, C., & Singh Raghuwanshi, N. (2009). Flood forecasting using ANN, neuro-fuzzy, and neuro-GA models. Journal of Hydrologic Engineering, 14(6), 647–652. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000040
National Statistics and Information Authority of Afghanistan. (2019). Central Yearbook of the Central Statistics Country. http://nsia.gov.af/library
Nayak, P. C., Sudheer, K. P., Rangan, D. M., & Ramasastri, K. S. (2005). Short‐term flood forecasting with a neurofuzzy model. Water Resources Research, 41(4). https://doi.org/10.1029/2004WR003562
Neumann, B., Vafeidis, A. T., Zimmermann, J., & Nicholls, R. J. (2015). Future coastal population growth and exposure to sea-level rise and coastal flooding-a global assessment. PloS ONE, 10(3), Article e0118571. https://doi.org/10.1371/journal.pone.0118571
Noymanee, J., Nikitin, N. O., & Kalyuzhnaya, A. V. (2017). Urban pluvial flood forecasting using open data with machine learning techniques in pattani basin. Procedia Computer Science, 119, 288–297. https://doi.org/10.1016/j.procs.2017.11.187
Panahi, M., Jaafari, A., Shirzadi, A., Shahabi, H., Rahmati, O., Omidvar, E., Lee, S., & Bui, D. T. (2021). Deep learning neural networks for spatially explicit prediction of flash flood probability. Geoscience Frontiers, 12(3), Article 101076. https://doi.org/10.1016/j.gsf.2020.09.007
Phillips, T. H., Baker, M. E., Lautar, K., Yesilonis, I., & Pavao-Zuckerman, M. A. (2019). The capacity of urban forest patches to infiltrate stormwater is influenced by soil physical properties and soil moisture. Journal of Environmental Management, 246, 11–18. https://doi.org/10.1016/j.jenvman.2019.05.127
Pradhan, B. (2009). Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques. Central European Journal of Geosciences, 1(1), 120–129. https://doi.org/10.2478/v10085-009-0008-5
Rahmati, O., Pourghasemi, H. R., & Zeinivand, H. (2016). Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto International, 31(1), 42–70. https://doi.org/10.1080/10106049.2015.1041559
Rezaeianzadeh, M., Tabari, H., Arabi Yazdi, A., Isik, S., & Kalin, L. (2014). Flood flow forecasting using ANN, ANFIS and regression models. Neural Computing and Applications, 25(1), 25–37. https://doi.org/10.1007/s00521-013-1443-6
Sachdeva, S., Bhatia, T., & Verma, A. K. (2017). Flood susceptibility mapping using GIS-based support vector machine and particle swarm optimization: A case study in Uttarakhand (India). In 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1–7). IEEE. https://doi.org/10.1109/ICCCNT.2017.8204182
Santos, P. P., & Reis, E. (2018). Assessment of stream flood susceptibility: a cross‐analysis between model results and flood losses. Journal of Flood Risk Management, 11, S1038–S1050. https://doi.org/10.1111/jfr3.12290
Schubert, J. E., & Sanders, B. F. (2012). Building treatments for urban flood inundation models and implications for predictive skill and modeling efficiency. Advances in Water Resources, 41, 49–64. https://doi.org/10.1016/j.advwatres.2012.02.012
Seejata, K., Yodying, A., Wongthadam, T., Mahavik, N., & Tantanee, S. (2018). Assessment of flood hazard areas using analytical hierarchy process over the Lower Yom Basin, Sukhothai Province. Procedia Engineering, 212, 340–347. https://doi.org/10.1016/j.proeng.2018.01.044
Shahabad, S. I., Zhang, Z., Keshavarzkermani, A., Ali, U., Mahmoodkhani, Y., Esmaeilizadeh, R., Bonakdar, A., & Toyserkani, E. (2020). Heat source model calibration for thermal analysis of laser powder-bed fusion. The International Journal of Advanced Manufacturing Technology, 106(7–8), 3367–3379. https://doi.org/10.1007/s00170-019-04908-3
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