請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/17099
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 林國峰(Gwo-Fong Lin) | |
dc.contributor.author | Jhih-Huang Wang | en |
dc.contributor.author | 王志煌 | zh_TW |
dc.date.accessioned | 2021-06-07T23:56:27Z | - |
dc.date.copyright | 2020-08-21 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-08-10 | |
dc.identifier.citation | Al-Abadi, A.M., 2018. Mapping flood susceptibility in an arid region of southern Iraq using ensemble machine learning classifiers: a comparative study. Arabian Journal Geosciences 11, 218. https://doi.org/10.1007/s12517-018-3584-5. Al-Abadi, A.M., Shahid, S., Al-Ali, A.K., 2016. A GIS-based integration of catastrophe theory and analytical hierarchy process for mapping flood susceptibility: a case study of Teeb area, Southern Iraq. Environmental Earth Sciences 75, 687. https://doi.org/10.1007/s12665-016-5523-7. Alipanahi, B., Delong, A., Weirauch, M.T., Frey, B.J., 2015. Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nature Biotechnology 33(8), 831–838. https://doi.org/10.1038/nbt.3300. Alfieri, L., Salamon, P., Bianchi, A., Neal, J., Bates, P., Feyen, L., 2014. Advances in pan‐European flood hazard mapping. Hydrological Processes 28(13), 4067–4077. https://doi.org/10.1002/hyp.9947. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. 2000a. Artificial Neural Networks in hydrology, I: Preliminary Concepts. Journal of Hydrologic Engineering, ASCE 5(2), 115–123. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(115). ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. 2000b. Artificial Neural Networks in hydrology, II: Hydrological Applications. Journal of Hydrologic Engineering, ASCE 5(2), 124–137. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(124). Beven, K., Kirkby, M.J., 1979. A physically based, variable contributing area model of basin hydrology. Hydrological Sciences Bulletin 24(1), 43–69. Breiman, L., 2001. Random Forests. Machine Learning 45, 5–32. Borovykh, A., Bohte, S., Oosterlee, C.W., 2017 Conditional time series forecasting with convolutional neural networks. In: Proceedings of 26th International Conference on Artificial Neural Networks, pp. 729–730. Bui, D.T., Pradhan, B., Nampak, N., 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. https://doi.org/10.1016/j.jhydrol.2016.06.027. Bui, D.T., Hoang, N.D., Pham, T.D., Ngo, P.T.T., Hoa, P.V., Minh, N.Q., Tran, X.T., Samui, P., 2019. A new intelligence approach based on GIS-based Multivariate Adaptive Regression Splines and metaheuristic optimization for predicting flash flood susceptible areas at high-frequency tropical typhoon area. Journal of Hydrology 575, 314–326. https://doi.org/10.1016/j.jhydrol.2019.05.046. Chang, L.C., Shen, H.Y., Chang, F.J., 2014. Regional flood inundation nowcast using hybrid SOM and dynamic neural networks. Journal of Hydrology 519, 476–489. https://doi.org/10.1016/j.jhydrol.2014.07.036. Chang, L.C., Shen, H.Y., Wang, Y.F., Huang, J.Y., Lin, Y.T., 2010. Clustering-based hybrid inundation model for forecasting flood inundation depths. Journal of Hydrology 385 (1), 257–268. https://doi.org/10.1016/j.jhydrol.2010.02.028. Chang, M.J., Chang, H.K., Chen, Y.C., Lin, G.F., Chen, P.A., Lai, J.S., Tan, Y.C., 2018. A support vector machine forecasting model for typhoon flood inundation mapping and early flood warning systems. Water 10(12), 1734. https://doi.org/10.3390/w10121734. Chapi, K., Singh, V.P., Shirzadi, A., Shahabi, H., Bui, D.T., Pham, T.P., Khosravi, K., 2017. A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environmental Modelling Software 95, 229–245. https://doi.org/10.1016/j.envsoft.2017.06.012. Chen, H., Ito, y., Sawamukai, M., Tokunaga, T., 2015. Flood hazard assessment in the Kujukuri Plain of Chiba Prefecture, Japan, based on GIS and multicriteria decision analysis. Natural Hazards 78, 105–120. https://doi.org/10.1007/s11069-015-1699-5. 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. https://doi.org/10.1016/j.jhydrol.2019.05.089. Chen, W., Li, Y., Xue, W., Shahabi, H., Li, S., Hong, H., Wang, X., Bian, H., Pradhan, B., Ahmad, B.B., 2020. Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods. Science of The Total Environment 701, 134979. https://doi.org/10.1016/j.scitotenv.2019.134979. 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, 2087–2096. https://doi.org/10.1016/j.scitotenv.2018.10.064. Chung, C.J.F., Fabbri, A.G., 2003. Validation of spatial prediction models for landslide hazard mapping. Natural Hazards 30, 451–472. https://doi.org/10.1023/B:NHAZ.0000007172.62651.2b. Dimitriadis, P., Tegos, A., Oikonomou, A., Pagana, V., Koukouvinos, A., Mamassis, N., Koutsoyiannis, D., Efstratiadis, A., 2016. Comparative evaluation of 1D and quasi-2D hydraulic models based on benchmark and real-world applications for uncertainty assessment in flood mapping. Journal of Hydrology 534, 478–492. https://doi.org/10.1016/j.jhydrol.2016.01.020. Farzad, F., El-Shafie, A.H., 2017. Performance enhancement of rainfall pattern–water level prediction model utilizing self-organizing-map clustering method. Water Resources Management 31(3), 945–959. https://doi.org/10.1007/s11269-016-1556-7. Feng, Q., Wen, X., Li, J., 2015. Wavelet analysis-support vector machine coupled models for monthly rainfall forecasting in arid regions. Water Resources Management 29(4), 1049–1065. https://doi.org/10.1007/s11269-014-0860-3. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., 2009. Multivariate Data Analysis. 7th ed. Upper Saddle River, NJ: Prentice Hall. Hallegatte, S., Green, C., Nicholls, R. J., Corfee-Morlot, J., 2013. Future flood losses in major coastal cities. Nature climate Change 3, 802–806. https://doi.org/10.1038/nclimate1979. He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778. Hinton, G.E., Osindero, S., Teh, Y.W., 2006. A fast learning algorithm for deep belief nets. Neural Computation 18(7), 1527–1554. Hipni, A., El-shafie, A., Najah, A., Karim, O.A., Hussain, A., Mukhlisin, M., 2013. Daily forecasting of dam water levels: comparing a support vector machine (SVM) model with adaptive neuro fuzzy inference system (ANFIS). Water Resources Management 27(10), 3803–3823. https://doi.org/10.1007/s11269-013-0382-4. Hochreiter, S., Schmidhuber, J., 1997. LSTM can solve hard long time lag problems. In: Mozer, M.C., Jordan, M.I., Petsche, T. (Eds.), Advances in Neural Information Processing Systems 9. MIT Press, Cambridge MA, pp. 473–479. Huang, C.C., Fang, H.T., Ho, H.C., Jhong, B.C., 2019. Interdisciplinary application of numerical and machine-learning-based models to predict half-hourly suspended sediment concentrations during typhoons. Journal of Hydrology 573, 661–675. https://doi.org/10.1016/j.jhydrol.2019.04.001. Jing, Y., Bian, Y., Hu, Z., Wang, L., Xie, X.Q., 2018. Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data era. AAPS Journal 20, 58. https://doi.org/10.1208/s12248-018-0210-0. Jhong, B.C., Wang, J.H., Lin, G.F., 2016. Improving the long lead-time inundation forecasts using effective typhoon characteristics. Water Resources Management 30(12), 4247–4271. https://doi.org/10.1007/s11269-016-1418-3. Jhong, B.C., Wang, J.H., Lin, G.F., 2017. An integrated two-stage support vector machine approach to forecast inundation maps during typhoons. Journal of Hydrology 547, 236–252. https://doi.org/10.1016/j.jhydrol.2017.01.057. 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. https://doi.org/10.1016/j.jhydrol.2019.02.034. Khosravi, K., Nohani, E., Maroufinia, E., Pourghasemi, H. R., 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. Natural Hazards 83, 947–987. https://doi.org/10.1007/s11069-016-2357-2. Khosravi, K., Pham, B.T., Chapi, K., Shirzadi, A., Shahabi, H., Revhaug, I., Prakash, I., Bui, D.T., 2018. A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Science of The Total Environment 627, 744–755. https://doi.org/10.1016/j.scitotenv.2018.01.266. 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. Kia, M.B., Pirasteh, S., Pradhan, B., Mahmud, A.R., Sulaiman, W.N.A., Moradi, A., 2012. An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environmental Earth Sciences 67, 251–264. https://doi.org/10.1007/s12665-011-1504-z. Kiang, M.Y., 2001. Extending the Kohonen self-organizing map networks for clustering analysis. Computational Statistics Data Analysis 38(2), 161–180. https://doi.org/10.1016/S0167-9473(01)00040-8. Krizhevsky, A., Sutskever, I., Hinton, G.E., 2012. Imagenet classification with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems 25, pp. 1090–1098. Kumar, S., Tiwari, M.K., Chatterjee, C., Mishra, A., 2015. Reservoir inflow forecasting using ensemble models based on neural networks, wavelet analysis and bootstrap method. Water Resources Management 29(13), 4863–4883. https://doi.org/10.1007/s11269-015-1095-7. Lai, C., Shao, Q., Chen, X., Wang, Z., Zhou, X., Yang, B. and Zhang, L., 2016. Flood risk zoning using a rule mining based on ant colony algorithm. Journal of Hydrology 542, 268–280. https://doi.org/10.1016/j.jhydrol.2016.09.003. LeCun, Y., Bengio, Y., 1995. Convolutional networks for images, speech, and time series. In: Arbib, M.A. (Ed.), The Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge MA, pp. 255–257. LeCun, Y., Bengio, Y., Hinton, G., 2015. Deep learning. Nature 521(7553), 436–444. Lee, S., Kim, J.C., Jung, H.S., Lee, M.J., Lee, S., 2017. Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea. Geomatics, Natural Hazards Risk 8(2), 1185–1203. https://doi.org/10.1080/19475705.2017.1308971. Lian, J., Xu, H., Xu, K., Ma, C., 2017. Optimal management of the flooding risk caused by the joint occurrence of extreme rainfall and high tide level in a coastal city. Natural Hazards 89(1), 183-200. https://doi.org/10.1007/s11069-017-2958-4. Lin, G.F., Chen, G.R., Huang, P.Y., Chou, Y.C., 2009a. Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods. Journal of Hydrology 372(1–4), 17–29. https://doi.org/doi:10.1016/j.jhydrol.2009.03.032. Lin, G.F., Chen, G.R., Wu, M.C., Chou, Y.C., 2009b. Effective forecasting of hourly typhoon rainfall using support vector machines. Water Resources Research 45(8), W08440. https://doi.org/doi:10.1029/2009WR007911. Lin, G.F., Chen, L.H., 2005. Time series forecasting by combining the radial basis function network and the self‐organizing map. Hydrological Processes 19(10), 1925–1937. https://doi.org/10.1002/hyp.5637. Lin, G.F., Chen, L.H., 2006. Identification of homogeneous regions for regional frequency analysis using the self-organizing map. Journal of Hydrology 324(1–4), 1–9. https://doi.org/10.1016/j.jhydrol.2005.09.009. Lin, G.F., Huang, P.Y., Chen, G.R., 2010. Using typhoon characteristics to improve the long lead-time flood forecasting of a small watershed. Journal of Hydrology 380(3–4), 450–459. https://doi.org/doi:10.1016/j.jhydrol.2009.11.019. Lin, G.F., Jhong, B.C., Chang, C.C., 2013a. Development of an effective data-driven model for hourly typhoon rainfall forecasting. Journal of Hydrology 495, 52–63. https://doi.org/doi:10.1016/j.jhydrol.2013.04.050. Lin, G.F., Jhong, B.C., 2015, A real-time forecasting model for the spatial distribution of typhoon rainfall. Journal of Hydrology 521, 302–313. https://doi.org/10.1016/j.jhydrol.2014.12.009. Lin, G.F., Lin, H.Y., Chou, Y.C., 2013b. Development of a real-time regional inundation forecasting model for the inundation warning system. Journal of Hydroinformatics 15(4), 1391–1407. https://doi.org/doi:10.2166/hydro.2013.202. Liong, S.Y., Lim, W.H., Paudyal, G.N., 2000. River stage forecasting in Bangladesh: neural network approach. Journal of computing in civil engineering 14(1), 1–8. Liuzzo, L., Sammartano, V., Freni, G., 2019. Comparison between different distributed methods for flood susceptibility mapping. Water Resources Management 33(9), 3155–3173. https://doi.org/10.1007/s11269-019-02293-w. Marconi, M., Gatto, B., Magni, M. and Marincioni, F., 2016. A rapid method for flood susceptibility mapping in two districts of Phatthalung Province (Thailand): present and projected conditions for 2050. Natural Hazards 81(1): 329–346. https://doi.org/10.1007/s11069-015-2082-2. Mirzaei, G., Soltani, A., Soltani, M., Darabi, M., 2018. An integrated data-mining and multi-criteria decision-making approach for hazard-based object ranking with a focus on landslides and floods. Environmental Earth Sciences 77, 581. https://doi.org/10.1007/s12665-018-7762-2. Mohanty, S., Jha, M.K., Raul, S.K., Panda, R.K., Sudheer, K.P., 2015 Using artificial neural network approach for simultaneous forecasting of weekly groundwater levels at multiple sites. Water Resources Management 29(15), 5521–5532. https://doi.org/10.1007/s11269-015-1132-6. Moore, R.D., Thompson, J.C., 1996. Are water table variations in a shallow forest soil consistent with the TOPMODEL concept? Water Resources Research 32(3), 663–669. Nandi, A., Mandal, A., Wilson, M., Smith, D., 2016. Flood hazard mapping in Jamaica using principal component analysis and logistic regression. Environmental Earth Sciences 75, 465. https://doi.org/10.1007/s12665-016-5323-0. Nguyen, P.K.T., Chua, L.H.C., 2012. The data‐driven approach as an operational real‐time flood forecasting model. Hydrological Processes 26(19), 2878–2893. https://doi.org/10.1002/hyp.8347. Norbiato, D., Borga, M., Dinale, R., 2009. Flash flood warning in ungauged basins by use of the flash flood guidance and model‐based runoff thresholds. Meteorological Applications 16(1), 65–75. https://doi.org/10.1002/met.126. Oliveira, S.C., Zêzere, J.L., Lajas, S., Melo, R., 2017. Combination of statistical and physically based methods to assess shallow slide susceptibility at the basin scale. Natural Hazards and Earth System Sciences 17(7), 1091–1109. https://doi.org/10.5194/nhess-17-1091-2017. Oyebode, O., Stretch, D., 2019. Neural network modeling of hydrological systems: a review of implementation techniques. Natural Resource Modeling 32(1), e12189. Partal, T., Cigizoglu, H.K., 2008. Estimation and forecasting of daily suspendedsediment data using wavelet–neural networks. Journal of Hydrology 358(3–4), 317–331. https://doi.org/10.1016/j.jhydrol.2008.06.013. Polishchuk, P.G., Muratov, E.N., Artemenko, A.G., Kolumbin, O.G., Muratov, N.N., Kuz’min, V.E., 2009. Application of random forest approach to QSAR prediction of aquatic toxicity. Journal of Chemical Information and Modeling 49(11), 2481–2488. https://doi.org/10.1021/ci900203n. Rahmati, O., Pourghasemi, H.R., 2017. Identification of critical flood prone areas in data-scarce and ungauged regions: a comparison of three data mining models. Water Resources Management 31(5), 1473–1487. https://doi.org/10.1007/s11269-017-1589-6. Roy, K., Kar, S., Das, R.N., 2015. Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment. Academic Press: New York. Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1986. Learning representations by back-propagating errors. Nature 323, 318–362. https://doi.org/10.1038/323533a0. Sari, V., dos Reis Castro, N.M., Pedrollo, O.C., 2017. Estimate of suspended sediment concentration from monitored data of turbidity and water level using artificial neural networks. Water Resources Management 31(15), 4909–4923. https://doi.org/10.1007/s11269-017-1785-4. Seibert, J., 2000. Multi-criteria calibration of a conceptual runoff model using a genetic algorithm. Hydrology Earth System Sciences 4(2), 215–224. https://doi.org/10.5194/hess-4-215-2000. Seyoum, S.D., Vojinovic, Z., Price, R.K., Weesakul, S., 2012. Coupled 1D and noninertia 2D flood inundation model for simulation of urban flooding. Journal of Hydraulic Engineering 138(1), 23–34. Sun, W., Trevor, B., 2018. Multiple model combination methods for annual maximum water level prediction during river ice breakup. Hydrological Processes 32(3), 421–435. https://doi.org/10.1002/hyp.11429. Tehrany, M.S., Lee, M.J., Pradhan, B., Jebur, M.N. and Lee, S., 2014a. Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environmental Earth Sciences 72, 4001–4015. https://doi.org/10.1007/s12665-014-3289-3. Tehrany, M.S., Pradhan, B. and Jebur, M.N., 2014b. Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of Hydrology 512, 332–343. https://doi.org/10.1016/j.jhydrol.2014.03.008. Tehrany, M.S., Pradhan, B. and Jebur, M.N., 2015a. Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stochastic Environmental Research and Risk Assessment 29(4), 1149–1165. https://doi.org/10.1007/s00477-015-1021-9. Tehrany, M.S., Pradhan, B., Mansor, S., Ahmad, N., 2015b. Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena 125, 91–101. https://doi.org/10.1016/j.catena.2014.10.017. Termeh, S.V.R., 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. https://doi.org/10.1016/j.scitotenv.2017.09.262. van den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., Kavukcuoglu, K., 2016a. WaveNet: A generative model for raw audio. In: Proceedings of 9th ISCA Speech Synthesis Workshop, pp. 125. van den Oord, A., Kalchbrenner, N., Kavukcuoglu, K., 2016b. Pixel recurrent neural networks. In: Proceedings of 33rd International Conference on Machine Learning, pp. 1747–1756. Vapnik, V., 1995. The Nature of Statistical Learning Theory. Springer: New York. Vapnik, V., 1998. Statistical Learning Theory. Wiley: New York. Vojtek, M., Vojteková, J., 2019. Flood susceptibility mapping on a national scale in slovakia using the analytical hierarchy process. Water 11(2), 364. https://doi.org/10.3390/w11020364. Wang, J.H., Lin, G.F., Chang, M.J., Huang, I.H., Chen, Y.R., 2019. Real-time water-level forecasting using dilated causal convolutional neural networks. Water Resources Management 33(11), 3759–3780. https://doi.org/10.1007/s11269-019-02342-4. Wang, Y., Fang, Z., Hong, H., Peng, L., 2020. Flood susceptibility mapping using convolutional neural network frameworks. Journal of Hydrology 582, 124482. https://doi.org/10.1016/j.jhydrol.2019.124482. Wang, Z., Lai, C., Chen, X., Yang, B., Zhao, S., Bai, X., 2015. Flood hazard risk assessment model based on random forest. Journal of Hydrology 527, 1130–1141. https://doi.org/10.1016/j.jhydrol.2015.06.008. Wu, M.C., Lin, G.F., 2015. An hourly streamflow forecasting model coupled with an enforced learning strategy. Water 7(11), 5876–5895. https://doi.org/10.3390/w7115876. Wu, M.C., Lin, G.F., Lin, H.Y., 2013. The effect of data quality on model performance with application to daily evaporation estimation. Stochastic Environmental Research and Risk Assessment 27(7), 1661–1671. https://doi.org/doi:10.1007/s00477-013-0703-4. Wu, M.C., Lin, G.F., Lin, H.Y., 2014. Improving the forecasts of extreme streamflow by support vector regression with the data extracted by self-organizing map. Hydrological Processes 28(2), 386–397. https://doi.org/10.1002/hyp.9584. Xu, H., Ma, C., Lian, J., Xu K., Chaima, E., 2018. Urban flooding risk assessment based on an integrated k-means cluster algorithm and improved entropy weight method in the region of Haikou, China. Journal of Hydrology 563, 975–986. https://doi.org/10.1016/j.jhydrol.2018.06.060. Yang, T.H., Hwang, G.D., Tsai, C.C., Ho, J.Y., 2016. Using rainfall thresholds and ensemble precipitation forecasts to issue and improve urban inundation alerts. Hydrology and Earth System Sciences 20(12), 4731–4745. https://doi.org/10.5194/hess-20-4731-2016. Yao, C., Zhang, K., Yu, Z., Li, Z., Li, Q., 2014. Improving the flood prediction capability of the Xinanjiang model in ungauged nested catchments by coupling it with the geomorphologic instantaneous unit hydrograph. Journal of Hydrology 517, 1035–1048. https://doi.org/10.1016/j.jhydrol.2014.06.037. Yaseen, Z.M., Naganna, S.R., Sa’adi, Z., Samui, P., Ghorbani, M.A., Salih, S.Q., Shahid, S., 2020. Hourly river flow forecasting: application of emotional neural network versus multiple machine learning paradigms. Water Resources Management 34(3), 1075–1091. https://doi.org/10.1007/s11269-020-02484-w. Youssef, A.M., Hegab, M.A., 2019. Flood-hazard assessment modeling using multi-criteria analysis and GIS: A case study: ras gharib area, egypt. In: Pourghasemi, H.R., Gokceoglu, C. (Eds.), Spatial Modeling in GIS and R for Earth and Environmental Sciences (1st ed.). Elsevier: Amsterdam, pp. 229–257. https://doi.org/10.1016/B978-0-12-815226-3.00010-7. Yu, F., Koltun, V., 2016. Multi-scale context aggregation by dilated convolutions. In Proc International Conference on Learning Representations, arXiv:1511.07122. Yu, P.S., Chen, S.T., Chang, I.F., 2006. Support vector regression for real-time flood stage forecasting. Journal of Hydrology 328(3–4), 704–716. https://doi.org/10.1016/j.jhydrol.2006.01.021. Zeiler, M.D., Fergus, R., 2014. Visualizing and understanding convolutional networks. In: Proceedings of the European Conference on Computer Vision, Springer, pp. 818–833. Zhao, G., Pang, B., Xu, Z., Yue, J., Tu, T., 2018. Mapping flood susceptibility in mountainous areas on a national scale in China. Science of The Total Environment 615, 1133–1142. https://doi.org/10.1016/j.scitotenv.2017.10.037. Zwenzner, H., Voigt, S., 2009. Improved estimation of flood parameters by combining space based SAR data with very high resolution digital elevation data. Hydrology Earth System Sciences (13)5, 567–576. https://doi.org/10.5194/hess-13-567-2009. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/17099 | - |
dc.description.abstract | 洪水為世界上最具破壞力的自然災害之一,為了降低洪水所帶來的危害,發展淹水災害分區和水位預報模式在災害預警系統中是相當重要一環。人工智慧方法於模擬水文過程的潛力已經被許多研究所肯定。然而,過去文獻大部多使用人工智慧建立淹水潛勢模式,鮮少應用人工智慧方法並考慮周圍環境淹水潛勢以進行淹水災害分區評估。此外,過去傳統的人工智慧方法屬於淺層機器學習,例如,人工神經網絡、支援向量機和適應性網路模糊推論系統等,這些方法有著無法從原始數據資料當中提取有效特徵的問題。因此,本論文之目的為發展新型之淹水災害分區模式和水位預報模式來改良傳統模式之缺點。本論文內容將分成兩部份來展現所提出之模式優點。 於論文第一部份,本研究提出以結合隨機森林與自組織映射圖,建立一個新型淹水災害分區模式,以產生淹水災害風險分區地圖。所建立之模式包含兩個模組:淹水潛勢分析及淹水災害分區。首先,以隨機森林為基礎建構淹水潛勢分析模組,產生淹水潛勢地圖,接著依據淹水潛勢地圖中每個網格的淹水潛勢值,以自組織映射圖建構淹水災害分區模組,將地圖中每個淹水潛勢值進行分類,最後產生淹水災害風險分區地圖。另外,本研究也考慮了兩種不同自組織映射圖的輸入項,一種為只採用自身網格的潛勢值,另一種為採用自身及周圍網格的潛勢值。為了證明所建立之模式的改善效率,本研究與傳統用於淹水災害分區的自然斷點法進行比較。本研究以台灣宜蘭的蘭陽平原作為實際應用,以呈現所建立之模式的優越性。應用結果顯示,採用自身及周圍網格的潛勢值建立之淹水災害分區模式可改善災害風險劃分之準確度,且本研究建立之模式也比傳統模式更具有優勢及合理性。 於論文第二部份,本研究以擴展序列卷積神經網路為基礎,發展一個新型之水位預報模式,以改善時水位預報準確度。擴展序列卷積神經網路可有效地廣泛學習時間序列的歷史資料,且採用殘留層與捷徑連結方式使網路架構能夠更深層及強健,加速訓練之收斂速度。本研究以台灣東北部宜蘭河流域作為實際應用,以呈現所發展模式之優點,並將所發展之模式與分別以多層感知機及支援向量機為基礎之兩種傳統模式作比較。結果顯示本研究發展之模式優於傳統模式,且在較長的預報時間點能夠有效改善預報之表現。綜合以上所述,本研究建立之新型之淹水災害分區模式和水位預報模式對於災害預警系統有相當之助益。 | zh_TW |
dc.description.abstract | Floods are among the most harmful natural catastrophes in the world, often resulting in loss of human lives and properties. To mitigate flood damage, the development of flood hazard zoning and water-level forecasting models has been played an essential role in disaster warning systems. Previous studies have shown the potential of artificial intelligence (AI) for modeling hydrological processes. However, in previous flood hazard zoning studies, there is no literature on the use of AI for performing flood hazard zoning assessments and consideration of potential flooding in surrounding environment. Moreover, in the past, traditional AI methods belong to shallow machine learning such as artificial neural network (ANN), support vector machine (SVM), and adaptive network-based fuzzy inference systems. The application of these methods is not sufficient to extract stable recognition features because they can only process natural data in the original format. In this thesis, novel approaches are established to construct flood hazard zoning and water-level forecasting models. Two parts are conducted herein to demonstrate the superiority of the proposed models. In the first part of the thesis, a new type of flood hazard zoning model that uses integrated random forest (RF) and self-organizing map (SOM) methods is proposed. The model has two steps. The first is the creation of a module for flood susceptibility analysis to yield flood susceptibility values using the RF method. The second is the classification of flood susceptibility values according to the results of flood susceptibility analysis to obtain flood hazard zones with the use of a flood hazard zoning module based on the SOM network. Moreover, two different inputs for SOM are considered: (i) only the flood susceptibility value of a self-pixel is used as input, and (ii) the flood susceptibility values of the self-pixel and surrounding pixels are used as input. To examine the efficiency of the proposed model for flood hazard zoning, this study compares it with the existing model that is based on the natural break (NB) method. The proposed model is applied to the Lanyang Plain in Yilan County, Taiwan to demonstrate its advantages. The results indicate that the proposed model with flood susceptibility values from the self-pixel and surrounding pixels do improve assessment performance. The proposed model also performs better than the existing model, and it can provide optimal flood hazard zoning maps. In the second part of the thesis, a novel water-level forecasting model based on dilated causal convolution neural network (DCCNN) is proposed to obtain water-level forecasts with a lead time of 1- to 6-h, because a DCCNN model can efficiently exploit a broad range of history. Residual and skip connections are also applied throughout the network to enable the training of deeper networks and to accelerate convergence. To demonstrate the superiority of the proposed forecasting technique, it is applied to a dataset of 16 typhoon events that occurred during 2012–2017 in the Yilan River basin in Taiwan. To examine the efficiency of the improved methodology, this study also compares the proposed model with two existing models that are based on multilayer perceptron (MLP) and SVM. The results indicate that the DCCNN-based model is superior to both SVM and MLP models, especially in terms of modeling peak water levels. Much of the performance improvement in the proposed model is due to its ability to provide water-level forecasts with a long lead time. In conclusion, the proposed modeling technique is expected to be particularly useful in supporting disaster warning systems. | en |
dc.description.provenance | Made available in DSpace on 2021-06-07T23:56:27Z (GMT). No. of bitstreams: 1 U0001-0708202011063300.pdf: 5419090 bytes, checksum: e9871735821ec17faa19396968509ce0 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 口試委員審定書 i 誌謝 iii 中文摘要 iv Abstract vi Contents viii List of figures xi List of tables xiii Chapter 1 Introduction 1 1.1 Motivations 1 1.2 Objectives 4 1.3 Backgrounds and inspiration 5 1.3.1 A novel hybrid machine learning model for flood hazard zoning assessments 5 1.3.2 Real-time water-level forecasting using dilated causal convolutional neural networks 9 1.4 Organization of the thesis 13 Chapter 2 Methodology 14 2.1 Random forest 14 2.2 Self-organizing map 16 2.3 Multilayer perceptron 18 2.4 Support vector machine 19 2.5 Dilated causal convolutional neural network 21 2.5.1 CNN 21 2.5.2 Causal CNN 22 2.5.3 Dilated CNN 23 2.5.4 Gated Activation Units 24 2.5.5 Residual and Skip Connections 25 Chapter 3 A novel hybrid machine learning model for flood hazard zoning assessments 26 3.1 Model development 26 3.1.1 Flood susceptibility analysis step 26 3.1.2 Flood hazard zoning step 27 3.2 Application 30 3.2.1 The study area and data 30 3.2.2 Cross-validation and performance measures 36 3.3 Results and discussions 37 3.3.1 Flood conditioning factor analysis 37 3.3.2 Parameters and training data patterns sensitivity analysis for RF 41 3.3.3 Influence of the different inputs of the SOM on the classification 44 3.3.4 Performance of the proposed model for flood hazard zoning 45 3.3.5 Importance of flood conditioning factor analysis 49 3.4 Summary 52 Chapter 4 Real-time water-level forecasting using dilated causal convolutional neural networks 54 4.1 Model Construction 54 4.1.1 Input and Parameter Optimization 54 4.2 Application 60 4.2.1 The study area and data 60 4.2.2 Cross-validation and performance evaluation 64 4.3 Results and discussions 65 4.3.1 Comparison between proposed model and two existing models 65 4.3.2 Water-level forecasting performance results 69 4.3.3 Performance of proposed model during extreme events 72 4.4 Summary 77 Chapter 5 Conclusions 79 References 82 Publications 97 | |
dc.language.iso | en | |
dc.title | 人工智慧技術於淹水災害分區及水位預報之研究 | zh_TW |
dc.title | Flood hazard zoning and water-level forecasting using artificial intelligence techniques | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 賴進松(Jihn-Sung Lai),李方中(Fong-Chung Lee),林文欽(Wen-Chin Lin),陳主惠(Chu-Hui Chen) | |
dc.subject.keyword | 淹水災害分區,淹水潛勢,水位預報,人工智慧,隨機森林,自組織映射圖,擴展序列卷積神經網路,多層感知機,支援向量機,淹水預警系統, | zh_TW |
dc.subject.keyword | Flood hazard zoning,Flood susceptibility,Water-level forecasting,Artificial intelligence,Random forest,Self-organizing map,Dilated causal convolutional neural network,Artificial neural network,Support vector machine,Flood-warning systems, | en |
dc.relation.page | 99 | |
dc.identifier.doi | 10.6342/NTU202002605 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2020-08-11 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
顯示於系所單位: | 土木工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
U0001-0708202011063300.pdf 目前未授權公開取用 | 5.29 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。