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Earth Science Informatics

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18.09.2021 | Research Article

Prediction of urban water accumulation points and water accumulation process based on machine learning

verfasst von: Hongfa Wang, Yajuan Zhao, Yihong Zhou, Huiliang Wang

Erschienen in: Earth Science Informatics | Ausgabe 4/2021

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Abstract

With the development of urbanization, global warming, rain island effect and other factors, cities around the world are facing more frequent and intense flood events. In order to deal with the damage caused by urban flood effectively, it is increasingly important to accurately predict and characterize the information of the flood in cities. In recent years, the rise of machine learning methods provides a new technical means for flood prediction. In this study, Naive Bayes (NB) and Random Forest (RF) algorithm were used to forecast the waterlogging point and the waterlogging process at the waterlogging point respectively to achieve the goal of predicting the whole process of urban waterlogging. Compared with the actual result, the four evaluation indexes (P, R, A and F₁) of the NB classification models are 91%, 90.5%, 98.9% and 90.7% respectively, and the three regression indexes (MAE, MRER and RMSE) of the RF regression model were respectively 0.95%, 9.53% and 1.21%. The results demonstrated that the prediction result of NB model for waterlogging point is reliable, and the process of waterlogging predicted by RF model is also consistent with the actual situation, which verify the validity and applicability of the NB model and RF model. This research is expected to provide scientific guidance and theoretical support for urban flood disaster mitigation and relief work.

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Nächster Artikel Correction to: Prediction of urban water accumulation points and water accumulation process based on machine learning

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Bhan SK, Team F (2001) Study of floods in West Bengal during september, 2000 using indian remote sensing satellite data. J Indian Soc Remote 29:1–2. https://doi.org/10.1007/bf02989907CrossRef

Bisht DS, Chatterjee C, Kalakoti S, Upadhyay P, Sahoo M, Panda A (2016) Modeling urban floods and drainage using SWMM and MIKE URBAN: a case study. Nat Hazards 84(2):749–776. https://doi.org/10.1007/s11069-016-2455-1CrossRef

Braud I, Breil P, Thollet F, Laagouy M, Branger F, Jacqueminet C, Kermadi S, Michel K (2013) Evidence of the impact of urbanization on the hydrological regime of a medium-sized periurban catchment in France. J Hydrol 485:5–23. https://doi.org/10.1016/j.jhydrol.2012.04.049CrossRef

Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324CrossRef

Bubeck P, Aerts JCJH, de Moel H, Kreibich H (2016) Preface: flood-risk analysis and integrated management. Nat Hazard Earth Syst Sci 16(4):1005–1010. https://doi.org/10.5194/nhess-16-1005-2016CrossRef

Chang H, Lafrenz M, Jung IW, Figliozzi M, Platman D, Pederson C (2010) Potential impacts of climate change on flood-induced travel disruptions: a case study of Portland, Oregon, USA. Annal Assoc Am Geogr 100(4):938–952. https://doi.org/10.1080/00045608.2010.497110CrossRef

Chen XW, Liu M (2006) Prediction of protein-protein interactions using random decision forest framework. Bioinformatics 21(24):4394–4400. https://doi.org/10.1093/bioinformatics/bti721CrossRef

Cheng T, Xu ZX, Hong SY, Song SL (2017) Flood risk zoning by using 2D hydrodynamic modeling: a case study in Jinan City. Math Probl Eng 2017:5659197. https://doi.org/10.1155/2017/5659197CrossRef

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. Sci Total Environ 651:2087–2096. https://doi.org/10.1016/j.scitotenv.2018.10.064CrossRef

Dalziell E, Nicholson A (2001) Risk and impact of natural hazards on a road network. J Transp Eng 127(2):159–166. https://doi.org/10.1061/(ASCE)0733-947X(2001)127:2(159)CrossRef

Diaz-Nieto J, Lerner DN, Saul AJ, Blanksby J (2012) GIS water-balance approach to support surface water flood-risk management. J Hydrol Eng 17(1):55–67. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000416CrossRef

Ding YK, Zhu YL, Feng J, Zhang PC, Cheng ZR (2020) Interpretable spatio-temporal attention LSTM model for flood forecasting. Neurocomputing 403:348–359. https://doi.org/10.1016/j.neucom.2020.04.110CrossRef

EM-DAT 2020. Disaster profiles. https://www.emdat.be/emdat_db/. Accessed 9 March 2020

Ferreira CSS, Walsh RPD, Shakesby RA, Keizer JJ, Soares D, Gonz á lez-Pelayo O, Coelho COA, Ferreira AJD (2016) Differences in overland flow, hydrophobicity and soil moisture dynamics between Mediterranean woodland types in a peri-urban catchment in Portugal. J Hydrol 533:473–485. https://doi.org/10.1016/j.jhydrol.2015.12.040CrossRef

Gain AK, Hoque MM (2013) Flood risk assessment and its application in the eastern part of Dhaka City, Bangladesh. J Flood Risk Manag 6(3):219–228. https://doi.org/10.1111/jfr3.12003CrossRef

GebreEgziabher M, Demissie Y (2020) Modeling urban flood inundation and recession impacted by manholes. Water 12(4):1160. https://doi.org/10.3390/w12041160CrossRef

Gislason PO, Benediktsson JA, Sveinsson JR (2006) Random forests for land cover classification. Pattern Recogn Lett 27(4):294–300. https://doi.org/10.1016/j.patrec.2005.08.011CrossRef

Granata F, Gargano R, Marinis G (2016) Support vector regression for rainfall-runoff modeling in urban drainage: a comparison with the EPA’s storm water management model. Water 8(3):69. https://doi.org/10.3390/w8030069CrossRef

Guo XJ, Zhang CC, Luo WR, Yang J, Yang M (2020) Urban impervious surface extraction based on multi-features and random forest. IEEE Access 8:226609–226623. https://doi.org/10.1109/ACCESS.2020.3046261CrossRef

Hajat S, Ebi KL, Kovats S, Menne B, Edwards S, Haines A (2003) The human health consequences of flooding in Europe and the implications for public health. Appl Environ Sci Public Health 1(1):13–21

Hirabayashi Y, Mahendran R, Koirala S, Konoshima L, Yamazaki D, Watanabe S, Kim H, Kanae S (2013) Global flood risk under climate change. Nat Clim Chang 3(9):816–821. https://doi.org/10.1038/nclimate1911CrossRef

Huong HTL, Pathirana A (2013) Urbanization and climate change impacts on future urban flooding in Can Tho city, Vietnam. Hydrol Earth Syst Sci 17(1):379–394. https://doi.org/10.5194/hess-17-379-2013CrossRef

IPCC (2014). In: Core Writing Team, Pachauri RK, Meyer LA (eds) Climate Change 2014: synthesis report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, Switzerland, p 151

Jamshed A, Birkmann J, McMillan JM, Rana IA, Feldmeyer D, Sauter H (2021) How do rural-urban linkages change after an extreme flood event? Empirical evidence from rural communities in Pakistan. Sci Total Environ 705:141462. https://doi.org/10.1016/j.scitotenv.2020.141462CrossRef

Jhong BC, Wang JH, Lin GF (2016) Improving the long lead-time inundation forecasts using effective typhoon characteristics. Water Resour Manag 30(12):4247–4271. https://doi.org/10.1007/s11269-016-1418-3CrossRef

Jing WL, Zhang PY, Zhao XD, Yang YP, Jiang H, Xu JH, Yang J, Li Y (2020) Extending GRACE terrestrial water storage anomalies by combining the random forest regression and a spatially moving window structure. J Hydrol 590:125239. https://doi.org/10.1016/j.jhydrol.2020.125239CrossRef

Jongman B (2018) Effective adaptation to rising flood risk COMMENT. Nat Commun 9:1986. https://doi.org/10.1038/s41467-018-04396-1CrossRef

Kabir E, Guikema S, Kane B (2018) Statistical modeling of tree failures during storms. Reliab Eng Syst Safe 177:68–79. https://doi.org/10.1016/j.ress.2018.04.026CrossRef

Kim HI, Han KY (2020) Data-driven approach for the rapid simulation of urban flood prediction. KSCE J Civ Eng 24(6):1932–1943. https://doi.org/10.1007/s12205-020-1304-7CrossRef

Kong FH, Ban YL, Yin HW, James P (2017) Modeling stormwater management at the city district level in response to changes in land use and low impact development. Environ Modell Softw 95:132–142. https://doi.org/10.1016/j.envsoft.2017.06.021CrossRef

LA Lee S, Kouzania AZ, Hu EJ (2010) Random forest based lung nodule classification aided by clustering. Comput Med Imag Grap 34(7):535–542. https://doi.org/10.1016/j.compmedimag.2010.03.006CrossRef

Li W, Niu L, Chen H, Wu H (2020) Robust downscaling method of land surface temperature by using random forest algorithm. J Geo-Inf Sci 22(8):1666–1678. https://doi.org/10.12082/dqxxkx.2020.190142CrossRef

Liu J, Sun SQ, Tan ZL, Liu Y (2020) Nondestructive detection of sunset yellow in cream based on near-infrared spectroscopy and interval random forest. Spectrochim Acta A 242:118718. https://doi.org/10.1016/j.saa.2020.118718CrossRef

Loperfideo JV, Noe GB, Jarnagic ST, Hogan DM (2014) Effects of distributed and centralised stormwater best management practices and land cover on urban stream hydrology at the catchment scale. J Hydrol 519:2584–2595. https://doi.org/10.1016/j.jhydrol.2014.07.007CrossRef

Lou WC, Wang XQ, Chen F, Chen YX, Jiang B, Zhang H (2014) Sequence based prediction of DNA-binding proteins based on hybrid feature selection using random forest and gaussian Naive bayes. PLoS ONE 9(1):e86703. https://doi.org/10.1371/journal.pone.0086703CrossRef

Malekipirbazari M, Aksakalli V (2015) Risk assessment in social lending via random forests. Expert Syst Appl 42(10):4621–4631. https://doi.org/10.1016/j.eswa.2015.02.001CrossRef

Miller JD, Kim H, Kjeldsen TR, Packman J, Grebby S, Dearden R (2014) Assessing the impact of urbanization on storm runoff in a peri-urban catchment using historical change in impervious cover. J Hydrol 515:59–70. https://doi.org/10.1016/j.jhydrol.2014.04.011CrossRef

Mu DR, Luo PP, Lyu J, Zhou MM, Huo AD, Duan WL, Nover D, He B, Zhao XL (2020) Impact of temporal rainfall patterns on flash floods in Hue City, Vietnam. J Flood Risk Manag 14(1):e12668. https://doi.org/10.1111/jfr3.12668CrossRef

Nourani V, Alami MT, Aminfar MH (2009) A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Eng Appl Artif Intell 22(3):466–472. https://doi.org/10.1016/j.engappai.2008.09.003CrossRef

Omran S, El Houby EMF (2020) Prediction of electrical power disturbances using machine learning techniques. J Ambient Intel Hum Comp 11(7):2987–3003. https://doi.org/10.1007/s12652-019-01440-wCrossRef

Parkhurst DF, Brenner KP, Dufour AP, Wymer LJ (2005) Indicator bacteria at five swimming beaches—analysis using random forests. Water Res 39(7):1354–1360. https://doi.org/10.1016/j.watres.2005.01.001CrossRef

Patil HP, Atique M (2020) CDNB: CAVIAR-dragonfly optimization with Naive bayes for the sentiment and affect analysis in social media. Big Data 8(2):107–124. https://doi.org/10.1089/big.2019.0130CrossRef

Patro S, Chatterjee C, Mohanty S, Singh R (2009) Flood inundation modeling using MIKE FLOOD and remote sensing data. J Indian Soc Remote 37(1):107–118. https://doi.org/10.1007/s12524-009-0002-1CrossRef

Perez A, Larranaga P, Inza I (2009) Bayesian classifiers based on kernel density estimation: flexible classifiers. Int J Approx Reason 50(2):341–362. https://doi.org/10.1016/j.ijar.2008.08.008CrossRef

Prajwala TR (2015) A comparative study on decision tree and random forest using R tool. IJARCCE 4(1):4

Pregnolato M, Ford A, Glenis V, Wilkinson S, Dawson R (2017) Impact of climate change on disruption to urban transport networks from pluvial flooding. J Infrastruct Syst 23(4):04017015. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000372CrossRef

Rahmati O, Darabi H, Panahi M, Kalantari Z, Naghibi SA, Ferreira CSS, Kornegady A, Karimidastenaei Z, Mohamadi F, Stefanidis S, Bui DT, Haghighi AT (2020) Development of novel hybridized models for urban flood susceptibility mapping. Sci Rep 10(1):12937. https://doi.org/10.1038/s41598-020-69703-7CrossRef

Schmitt TG, Thomas M, Ettrich N (2004) Analysis and modeling of flooding in urban drainage systems. J Hydrol Eng 299(3–4):300–311. https://doi.org/10.1016/j.jhydrol.2004.08.012CrossRef

She L, You XY (2019) A dynamic flow forecast model for urban drainage using the coupled artificial neural network. Water Resour Manag 33(9):3143–3153. https://doi.org/10.1007/s11269-019-02294-9CrossRef

Smith A, Sterba-Boatwright B, Mott J (2010) Novel application of a statistical technique, random forests, in a bacterial source tracking study. Water Res 44(14):4067–4076. https://doi.org/10.1016/j.watres.2010.05.019CrossRef

Vafakhah M, Loor SMH, Pourghasemi H, Katebikord A (2020) Comparing performance of random forest and adaptive neuro-fuzzy inference system data mining models for flood susceptibility mapping. Arab J Geosci 13(11):417. https://doi.org/10.1007/s12517-020-05363-1CrossRef

Vojinovic Z, Tutulic D (2009) On the use of 1D and coupled 1D–2D modelling approaches for assessment of flood damage in urban areas. Urban Water J 6(3):183–199. https://doi.org/10.1080/15730620802566877CrossRef

Wang X, Kruger U, Irwin GW (2005) Process monitoring approach using fast moving window PCA. Ind Eng Chem Res 44(15):5691–5702. https://doi.org/10.1021/ie048873fCrossRef

Ward R (1978) Floods: a geographical perspective. MacMillan, LondonCrossRef

Wu XS, Wang ZL, Guo SL, Liao WL, Zeng ZY, Chen XH (2017) Scenario-based projections of future urban inundation within a coupled hydrodynamic model framework: a case study in Dongguan City, China. J Hydrol 547:428–442. https://doi.org/10.1016/j.jhydrol.2017.02.020CrossRef

Wu ZN, Zhou YH, Wang HL (2020a) Real-time prediction of the water accumulation process of urban stormy accumulation points based on deep learning. IEEE Access 8:151938–151951. https://doi.org/10.1109/ACCESS.2020.3017277CrossRef

Wu ZN, Zhou YH, Wang HL, Jiang ZH (2020b) Depth prediction of urban flood under different rainfall return periods based on deep learning and data warehouse. Sci Total Environ 716:137077. https://doi.org/10.1016/j.scitotenv.2020.137077CrossRef

White MD, Greer KA (2006) The effects of watershed urbanization on the stream hydrology and riparian vegetation of Los Penasquitos Creek, California. Landsc Urban Plan 74(2):125–138. https://doi.org/10.1016/j.landurbplan.2004.11.015CrossRef

Yazdi J, Neyshabouri SAAS (2012) A simulation-based optimization model for flood management on a watershed scale. Water Resour Manag 26(15):4569–4586. https://doi.org/10.1007/s11269-012-0167-1CrossRef

Yazdi MN, Ketabchy M, Sample DJ, Scott D, Liao HH (2019) An evaluation of HSPF and SWMM for simulating streamflow regimes in an urban watershed. Environ Modell Softw 118:211–225. https://doi.org/10.1016/j.envsoft.2019.05.008CrossRef

Zhang S, Li Z (2019) Simulation of urban rainstorm waterlogging and pipeline network drainage process based on swmm. J Phys 1213:052061. https://doi.org/10.1088/1742-6596/1213/5/052061CrossRef

Zhao DQ, Chen JN, Wang HZ, Tong QY, Cao SB, Sheng Z (2009) GIS-based urban rainfall-runoff modeling using an automatic catchment-discretization approach: a case study in Macau. Environ Earth Sci 59(2):465–472. https://doi.org/10.1007/s12665-009-0045-1CrossRef

Zolch T, Henze L, Keilholz P, Pauleit S (2017) Regulating urban surface runoff through nature-based solutions—an assessment at the micro-scale. Environ Res 157:135–144. https://doi.org/10.1016/j.envres.2017.05.023CrossRef

Zoppou C (2001) Review of urban storm water models. Environ Modell Softw 16(3):195–231. https://doi.org/10.1016/S1364-8152(00)00084-0CrossRef

Titel: Prediction of urban water accumulation points and water accumulation process based on machine learning
verfasst von: Hongfa Wang
Yajuan Zhao
Yihong Zhou
Huiliang Wang
Publikationsdatum: 18.09.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Earth Science Informatics / Ausgabe 4/2021
Print ISSN: 1865-0473
Elektronische ISSN: 1865-0481
DOI: https://doi.org/10.1007/s12145-021-00700-8

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