Abstract
Flood susceptibility mapping is an important method for flood research. In this paper, we combine a backpropagation neural network (BPNN) with a genetic quantum algorithm (GQA) for the first time to develop flood susceptibility mapping. The area on the Chinese side of the Tumen River Basin was selected as the research object. A set of flood conditioning factors was selected based on relevant literature and an actual situation and then validated using the chi-square test and correlation analysis. Different weights were assigned using stepwise weight assessment ratio analysis. Finally, modeling and flood susceptibility mapping using GQA-BPNN. As a reference, the same work was performed with both the pure BPNN and optimized BPNN using a genetic algorithm (GA). The results show that the area under the curve, root mean squared error, Nash-Sutcliffe coefficient and percentage of bias are significantly better for the GQA-BPNN than for the BPNN and GA-BPNN and that the flood sensitivity maps constructed by the GQA-BPNN have more flood points in high flood sensitivity areas. Therefore, the GQA-BPNN method can be considered an effective method for flood susceptibility mapping.
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References
Awais M, Rehman H, Raja MAZ, Awan SE, Ali A, Shoaib M, Malik MY (2022) Hall effect on MHD Jeffrey fluid flow with Cattaneo-Christov heat flux model: An application of stochastic neural computing. Complex & Intelligent Systems, DIO: https://doi.org/10.1007/s40747-022-00754-1
Bhasin A, Tripathi M (2021) Quantum computing at an inflection point: Are we ready for a new paradigm. IEEE Transactions on Engineering Management, DOI: https://doi.org/10.1109/TEM.2021.3103904
Bui DT, Khosravi K, Li SJ, Shahabi H, Panahi M, Singh VP, Chapi K, Shirzadi A, Panahi S, Chen W, Bin Ahmad B (2018) New hybrids of anfis with several optimization algorithms for flood susceptibility modeling. Water 10(9): 1210, DOI: https://doi.org/10.3390/w10091210
Bui DT, Ngo PTT, Pham TD, Jaafari A, Minh NQ, Hoa PV, Samui P (2019) A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping. Catena 179: 184–196, DOI: https://doi.org/10.1016/j.catena.2019.04.009
Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)? — Arguments against avoiding RMSE in the literature. Geoscientific Model Development 7(3): 1247–1250, DOI: https://doi.org/10.5194/gmd-7-1247-2014
Chapi K, Singh VP, Shirzadi A, Shahabi H, Bui DT, Pham BT, Khosravi K (2017) A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environmental Modelling & Software 95: 229–245, DOI: https://doi.org/10.1016/j.envsoft.2017.06.012
Chen W, Hong HY, Li SJ, Shahabi H, Wang Y, Wang XJ, Bin Ahmad 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, DOI: https://doi.org/10.1016/j.jhydrol.2019.05.089
Chen JL, Huang GR, Chen WJ (2021) Towards better flood risk management: Assessing flood risk and investigating the potential mechanism based on machine learning models. Journal of Environmental Management 293: 112810, DOI: https://doi.org/10.1016/j.jenvman.2021.112810
Choubin B, Moradi E, Golshan M, Adamowski J, Sajedi-Hosseini F, Mosavi A (2018) An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Science of the Total Environment 651: 2087–2096(2), DOI: https://doi.org/10.1016/j.scitotenv.2018.10.064
Dang ATN, Kumar L (2017) Application of remote sensing and GIS-based hydrological modelling for flood risk analysis: A case study of District 8, Ho Chi Minh city, Vietnam. Geomatics Natural Hazards & Risk 8(2): 1792–1811, DOI: https://doi.org/10.1080/19475705.2017.1388853
Dreyfus S (1973) The computational solution of optimal control problems with time lag. IEEE Transactions on Automatic Control 18(4): 383–385, DOI: https://doi.org/10.1109/TAC.1973.1100330
Ganjefar S, Tofighi M (2018) Optimization of quantum-inspired neural network using memetic algorithm for function approximation and chaotic time series prediction. Neurocomputing 291: 175–186, DOI: https://doi.org/10.1016/j.neucom.2018.02.074
Hagan MT, Menhaj MB (1994) Training feedforward networks with the marquardt algorithm. IEEE Transactions on Neural Networks 5(6): 989–93, DOI: https://doi.org/10.1109/72.329697
Han KH, Kim JH (2000) Genetic quantum algorithm and its application to combinatorial optimization problem. Proceedings of the 2000 congress on evolutionary computation, July 16–19, La Jolla, CA, USA
Hong HY, Panahi M, Shirzadi A, Ma TW, Liu JZ, Zhu AX, 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, DOI: https://doi.org/10.1016/j.scitotenv.2017.10.114
Jahangir MH, Reineh SMM, Abolghasemi M (2019) Spatial predication of flood zonation mapping in Kan River Basin Iran using artificial neural network algorithm. Weather and Climate Extremes 25: 100215, DOI: https://doi.org/10.1016/j.wace.2019.100215
Khalifeh S, Esmaili K, Khodashenas S, Akbarifard S (2020) Data on optimization of the non-linear Muskingum flood routing in Kardeh River using Goa algorithm. Data in Brief 30: 105398, DOI: https://doi.org/10.1016/j.dib.2020.105398
Khosravi K, Shahabi H, Pham BT, Adamowski J, Shirzadi A, Pradhan B, Dou J, Ly HB, Grof G, Ho HL, 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, DOI: https://doi.org/10.1016/j.jhydrol.2019.03.073
Kia MB, Pirasteh S, Pradhan B, Mahmud AR, Sulaiman WNA, Moradi A (2012) An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environmental Earth Sciences 67(1): 251–264, DOI: https://doi.org/10.1007/s12665-011-1504-z
Laboudi Z, Chikhi S (2012) Comparison of genetic algorithm and quantum genetic algorithm. International Arab Journal of Information Technology 9(3): 243–249, DOI: https://doi.org/10.1007/BF00448594
LeDell E, van der Laan MJ, Peterson M (2016) AUC-maximizing ensembles through metalearning. International Journal of Biostatistics 12(1): 203–218, DOI: https://doi.org/10.1515/ijb-2015-0035
Li HQ, Yuan DY, Ma XD, Cui DY, Cao L (2017) Genetic algorithm for the optimization of features and neural networks in ECG signals classification. Scientific Reports 7: 41011, DOI: https://doi.org/10.1038/srep41011
Li LP, Lan HX, Guo CB, Zhang YS, Li QW, Wu YM (2017) A modified frequency ratio method for landslide susceptibility assessment. Landslides 14(2): 727–741, DOI: https://doi.org/10.1007/s10346-016-0771-x
Liu RY, Liu N (2002) Flood area and damage estimation in Zhejiang, China. Journal of Environmental Management, 2002 66(1):1–8, DOI: https://doi.org/10.1006/jema.2002.0544
Liu YY, Zhao Y (2016) Flood of tumen river once in a century. Huanqiu, Retrieved September 1, 2016, https://www.china.huanqiu.com/article/9CaKrnJXoKf
Lou WP, Chen HY, Shen XL, Sun K, Deng SR (2012) Fine assessment of tropical cyclone disasters based on GIS and SVM in Zhejiang Province, China. Natural Hazards 64(1): 511–529, DOI: https://doi.org/10.1007/s11069-012-0254-x
Mahmoud SH, Gan TY (2018) Multi-criteria approach to develop flood susceptibility maps in arid regions of Middle East. Journal of Cleaner Production 196: 216–229, DOI: https://doi.org/10.1016/j.jclepro.2018.06.047
Mukerji A, Chatterjee C, Raghuwanshi NS (2009) Flood forecasting using ANN, neuro-fuzzy, and neuro-GA models. Journal of Hydrologic Engineering 14(6): 647652, DOI: https://doi.org/10.1061/(ASCE)HE.1943-5584.0000040
Ngo PTT, Hoang ND, Pradhan B, Nguyen QK, Tran XT, Nguyen QM, Nguyen VN, Samui P, Bui DT (2018) A novel hybrid swarm optimized multilayer neural network for spatial prediction of flash floods in tropical areas using Sentinel-1 SAR imagery and geospatial data. Sensors 18(11): 3704, DOI: https://doi.org/10.3390/s18113704
Pu LR, Li YF, Gao P, Zhang HH, Hu L (2022) A photosynthetic rate prediction model using improved RBF neural network. Scientific Reports 12(1): 9563, DOI: https://doi.org/10.1038/s41598-022-12932-9
Qing F, Zhao Y, Meng XM, Su XJ, Qi TJ, Yue DX (2020) Application of machine learning to debris flow susceptibility mapping along the China-Pakistan Karakoram Highway. Remote Sensing 12(18): 2933, DOI: https://doi.org/10.3390/rs12182933
Salis HHCD, Costa AMD, Vianna JHM, Schuler MA, Pacheco FAL (2019) Hydrologic modeling for sustainable water resources management in urbanized karst areas. International Journal of Environmental Researchand Public Health, 2019 16(14):2542, DOI: https://doi.org/10.3390/ijerphl6142542
Tehrany MS, Pradhan B, Jebur MN (2013) Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of Hydrology 504: 69–79, DOI: https://doi.org/10.1016/j.jhydrol.2013.09.034
Tehrany MS, Pradhan B, Mansor S, Ahmad N (2015) Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena 125: 91–101, DOI: https://doi.org/10.1016/j.catena.2014.10.017
Termeh SVR, Kornejady A, Pourghasemi HR, Keesstra S (2018) Flood susceptibility mapping using novel ensembles of adaptive neurofuzzy inference system and metaheuristic algorithms. Science of the Total Environment 615: 438–451, DOI: https://doi.org/10.1016/j.scitotenv.2017.09.262
Trigila A, Iadanza C, Esposito C, Scarascia-Mugnozza G (2015) Comparison of logistic regression and random forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology 249: 119–136, DOI: https://doi.org/10.1016/j.geomorph.2015.06.001
Wang Y, Hong HY, Chen W, Li SJ, Panahi M, Khosravi K, Shirzadi A, Shahabi H, Panahi S, Costache R (2019) Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm. Journal of Environmental Management 247: 712–729, DOI: https://doi.org/10.1016/j.jenvman.2019.06.102
Wang JJ, Shi P, Jiang P, Hu JW, Qu SM, Chen XY, Chen YB, Dai YQ, Xiao ZW (2018) Application of BP neural network algorithm in traditional hydrological model for flood forecasting. Water 9(1): 48, DOI: https://doi.org/10.3390/w9010048
Xing Y, Shao D, Ma XY, Zhang SS, Jiang G (2021) Investigation of the importance of different factors of flood inundation modeling applied in urbanized area with variance-based global sensitivity analysis. Science of the Total Environment 772: 145327, DOI: https://doi.org/10.1016/j.scitotenv.2021.145327
Xinhua NA (2022) The investigation report on the “July 20” Torrential Rain Disaster in Zhengzhou, Henan Province was released. Xinhua Daily Telegraph, Retrieved January 22, 2022, http://www.news.cn/mrdx/2022-01/22/c_1310435801.html
Yariyan P, Janizadeh S, Van Tran P, Nguyen HD, Costache R, Van Le H, Pham BT, Pradhan B (2020) Improvement of best first decision trees using bagging and dagging ensembles for flood probability mapping. Water Resources Management 34(9): 3037–3053, DOI: https://doi.org/10.1007/s11269-020-02603-7
Zhao G Pang B, Xu ZX, Yue JJ, Tu TB (2018) Mapping flood susceptibility in mountainous areas on a national scale in China. Science of The Total Environment 615: 1133–1142, DOI: https://doi.org/10.1016/j.scitotenv.2017.10.037
Zhou QQ, Su JH, Arnbjerg-Nielsen K, Ren Y, Luo JH, Ye ZJ, Feng JM (2021) A GIS-based hydrological modeling approach for rapid urban flood hazard assessment. Water 13(11): 1483, DOI: https://doi.org/10.3390/w13111483
Acknowledgments
We express our gratitude to editor of KSCE Journal of Civil Engineering, and the anonymous reviewers for their valuable comments and suggestions that improved the quality of our paper. This work was supported by the National Natural Science Foundation of China (42067065).
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Cui, H., Quan, H., Jin, R. et al. Flood Susceptibility Mapping Using Novel Hybrid Approach of Neural Network with Genetic Quantum Ensembles. KSCE J Civ Eng 27, 431–441 (2023). https://doi.org/10.1007/s12205-022-0559-6
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DOI: https://doi.org/10.1007/s12205-022-0559-6