Flood susceptibility zoning using machine learning improved by genetic algorithm | ||
| نشریه محیط زیست طبیعی | ||
| Volume 76, Issue 1, May 2023, Pages 43-60 PDF (2.01 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.22059/jne.2022.350170.2485 | ||
| Authors | ||
| Peyman Karami1; Seyed Ahmad Eslamnezhad1; Mobin Eftekhari* 2; Mohammad Akbari3; Melika Rastgoo4 | ||
| 1Department of Surveying Engineering, Faculty of Surveying Engineering and Spatial Information, University of Tehran, Tehran, Iran. | ||
| 2Department of Water Engineering and Hydraulic Structures, Faculty of Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran. | ||
| 3Department of of Civil Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran. | ||
| 4Department of Engineering and Water Resources Management, Faculty of Civil and Environmental Engineering, Tarbiat Madras University, Tehran, Iran. | ||
| Abstract | ||
| Due to the increase in the risk of floods, especially in the cities, and the emergence of human, financial, and environmental risks due to its increase, the flood zoning areas are of great importance. Therefore, in this study, flood susceptible areas in Birjand plain were tried to be zoned with the help of effective criteria. In this regard, the data-driven methods of support vector machine (SVM) and random forest (RF) were used in combination with genetic algorithm to zoning flood susceptible areas. Therefore, in order to implement and validate the mentioned models, 42 flood prone locations in the study area were extracted. In addition, 19 hydrogeological, topographical, geological and environmental criteria affecting flood susceptibility in the study area were extracted to be used to predict flood susceptibility map. Area under the curve (AUC) and a variety of other statistical indicators including coefficient of determination (R2) and Root mean square error (RMSE) were used to evaluate the performances of the models. The values of R2, RMSE and AUC obtained from the SVM-GA method were 0.9032, 0.2751 and 0.931, respectively, and the RF-GA method were 0.9823, 0.2321 and 0.914, respectively, which indicate the compatibility and The RF model is more accurate than the SVM model. The results also showed that the susceptibility of flooding in the central areas of the study area, due to lower altitude and slope angle, is higher than other areas. | ||
| Keywords | ||
| Optimization; Flood; Random forest; Support vector machine | ||
| References | ||
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Ahmadlou, M., Karimi, M., Alizadeh, S., Shirzadi, A., Parvinnejhad, D., Shahabi, H., Panahi, M., 2019. Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA). Geocarto International 34(11), 1252-1272. Arabgol, R., Sartaj, M., Asghari, K., 2016. Predicting nitrate concentration and its spatial distribution in groundwater resources using support vector machines (SVMs) model. Environmental Modeling & Assessment 21(1), 71-82. Chapi, K., Singh, V.P., Shirzadi, A., Shahabi, H., Tien Bui, D., Pham, BT., Khosravi, K., 2017. A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environmental Modelling & Software 95, 229-245. Chen, W., Hong, H., Li, S., Shahabi, H., Wang, Y., Wang, X., Ahmad, B.B., 2019. Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles. Journal of Hydrology 575, 864-873. Choubin, B., Moradi, E., Golshan, M., Adamowski, J., Sajedi-Hosseini, F., Mosavi, A., 2019. An ensemble prediction of flood susceptibility using multivariate discriminant analysis classification and regression trees and support vector machines. Science of the Total Environment 651(Pt2), 2087-2096. de Santana, F.B., de Souza, A.M., Poppi, R.J., 2018. Visible and near infrared spectroscopy coupled to random forest to quantify some soil quality parameters. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 191, 454-462. Eftekhari, M., Eslaminezhad, S., Haji Elyasi, A., Akbari, M., 2021. Predicting Groundwater Potential Areas Using Hybrid Artificial Intelligence Methods (Case Study: Birjand Plain). Iranian Journal of Soil and Water Research 52(9), 2383-2397. (In Persian) Eini, M., Kaboli, H.S., Rashidian, M., Hedayat, H., 2020. Hazard and vulnerability in urban flood risk mapping: Machine learning techniques and considering the role of urban districts. International Journal of Disaster Risk Reduction 50, 101687. Eslaminezhad, S.A., Eftekhari, M., Akbari, M., 2020. GIS-Based Flood Risk Zoning Based On Data-Driven Models. Journal of Hydraulic Structures 6(4), 75-98. Eslaminezhad, S., Eftekhari, M., Mahmoodizadeh, S., Akbari, M., Haji Elyasi, A., 2021a. Evaluation of Tree-Based Artificial Intelligence Models to Predict Flood Risk using GIS. Iran-Water Resources Research 17(2), 174-189. (In Persian) Eslaminezhad, S.A., Omarzadeh, D., Eftekhari, M., Akbari, M., 2021b. Development of a data-driven model to predict landslide sensitive areas. Geographia Technica 16(1). Eslaminezhad, S.A., Eftekhari, M., Azma, A., Kiyanfar, R., Akbari, M., 2022a. Assessment of flood susceptibility prediction based on optimized tree-based machine learning models. Journal of Water and Climate Change 13(6), 2353-2385. Eslaminezhad, S.A., Eftekhari, M., Akbari, M., Bayat, H., Barghi, W., 2022b. Using Boosted Regression Tree, Logistic Model Tree, and Random Forest Algorithms to Evaluate the Groundwater Potential. Watershed Management Research Journal 35(3), 44-59. Fotheringham, AS., Oshan, T.M., 2016. Geographically weighted regression and multicollinearity: dispelling the myth. Journal of Geographical Systems 18(4), 303-329. Ghavami, Z., Mohamadinia, A., 2017. Spatial forecasting of flood-prone areas using Geographic Information System (GIS). 4th national conference on Application of GIS in Water and Electric Industries, Arak. 13 & 14 December 2017. (In Persian) Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S.R., Tiede, D., Aryal, J., 2019. Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sensing 11(2), 196. Guevara, J., Zadrozny, B., Buoro, A., Lu, L., Tolle, J., Limbeck, J., Wu, M., Hohl, D., 2018. A hybrid data-driven and knowledge-driven methodology for estimating the effect of completion parameters on the cumulative production of horizontal wells. In SPE Annual Technical Conference and Exhibition. OnePetro. Hong, H., Tsangaratos, P., Ilia, I., Liu, J., Zhu, AX., Chen, W., 2018. Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China. Science of the Total Environment 625, 575-588. Hudson, P., Botzen, W.W., 2019. Cost–benefit analysis of flood‐zoning policies: A review of current practice". Wiley Interdisciplinary Reviews: Water 6(6), e1387. Jancewicz, K., Migoń, P., Kasprzak, M., 2019. Connectivity patterns in contrasting types of tableland sandstone relief revealed by Topographic Wetness Index. Science of the Total Environment 656, 1046-1062. Johann, G., Leismann, M., 2017. How to realise flood risk management plans efficiently in an urban area–the S eseke project. Journal of Flood Risk Management 10(2), 173-181. Kalantari, Z., Ferreira, C.S.S., Walsh, RPD., Ferreira, AJD., Destouni, G., 2017. Urbanization development under climate change: hydrological responses in a peri-urban Mediterranean catchment. Land Degradation & Development 28 (7), 2207–2221. Kanani-Sadat, Y., Arabsheibani, R., Karimipour, F., Nasseri, M., 2019. A new approach to flood susceptibility assessment in data-scarce and ungauged regions based on GIS-based hybrid multi criteria decision-making method. Journal of Hydrology 572, 17-31. Khosravi, K., Marufinia, E., Nohani, E., Chapy, K., 2017. Evaluation of Logistic Regression Efficiency in Mapping Flood Susceptibility. Journal of Range and Watershed Management 69(4), 863-876. (In Persian) Khosravi, K., Nohani, E., Maroufinia, E., Pourghasemi, HR., 2016. A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision- making technique. Nat. Hazards 83(2), 947-987. Kocaman, S., Tavus, B., Nefeslioglu, H.A., Karakas, G., Gokceoglu, C., 2020. Evaluation of floods and landslides triggered by a meteorological catastrophe (Ordu, Turkey, August 2018) using optical and radar data. Geofluids 2020, 1-18. Mirjalili, S., 2019. Genetic algorithm. In Evolutionary algorithms and neural networks. pp. 43-55. Springer, Cham. Mojaddadi, H., Pradhan, B., Nampak, H., Ahmad, N., and Ghazali, A. H. B., 2017. Ensemble machine-learning based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geomatics, Natural Hazards and Risk 8(2), 1080-1102. Nachappa, T.G., Piralilou, S.T., Gholamnia, K., Ghorbanzadeh, O., Rahmati, O., Blaschke, T., 2021. Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory. Journal of hydrology 590, 125275. Papaioannou, G., Vasiliades, L., Loukas, A., 2015. Multi-criteria analysis framework for potential flood prone areas mapping. Water resources management 29(2), 399-418. Pourghasemi, H.R., Razavi-Termeh, S.V., Kariminejad, N., Hong, H., Chen, W., 2020. An assessment of metaheuristic approaches for flood assessment. Journal of Hydrology 582, 124536. Quiroz, J.C., Mariun, N., Mehrjou, M.R., Izadi, M., Misron, N, and Mohd Radzi, M.A., 2018. Fault detection of broken rotor bar in LS-PMSM using random forests. Measurement 116, 273-280. 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. Razavi Termeh, S.V., Kornejady, A., Pourghasemi, H.R., Keesstra, S., 2018. Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Science of the Total Environment 615, 438-451. Sun, Y., Xue, B., Zhang, M., Yen, G.G., Lv, J., 2020. Automatically designing CNN architectures using the genetic algorithm for image classification. IEEE Transactions on Cybernetics 50(9), 3840-3854. Tien Bui, D., Pradhan, B., Nampak, H., Bui, Q.T., Tran, Q.A., Nguyen, Q.P., 2016. Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS. Journal of Hydrology 540, 317-330. Wang, X., Liu, H., 2019. A Knowledge-and Data-Driven Soft Sensor Based on Deep Learning for Predicting the Deformation of an Air Preheater Rotor. IEEE Access, 7, 159651-159660. Xiao, Y., Yi, S., Tang, Z., 2017. Integrated flood hazard assessment based on spatial ordered weighted averaging method considering spatial heterogeneity of risk preference. Science of the Total Environment 599, 1034-1046. Zeraatkar, Z., Hassanpour, F., 2016. Simulation of BirjandUrban FloodUsing HEC-RAS and ARC-GIS. Watershed Management Research Journal 29(3), 41-56. (In Persian) Ziaiian Firouz Abadi, P., Badragh Nejad, A., Sarli, R., Babaie, M., 2020. Measurement and identification of areas susceptible to flood spreading from the viewpoint of geological formations in Birjand watershed using RS/GIS. Scientific Journals Management System 20 (57), 1-24. (In Persian) | ||
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