Flood Prediction Using Machine Learning Models: Literature Review

Amir Mosavi; Pinar Ozturk; Kwok-Wing Chau

doi:10.3390/w10111536

journal article Open Access Oct 27, 2018

Flood Prediction Using Machine Learning Models: Literature Review

Amir Mosavi

Pinar Ozturk Kwok-Wing Chau

Water Vol. 10 No. 11 pp. 1536 · MDPI AG

View at Publisher Save 10.3390/w10111536

Abstract

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction of the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists. Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods. This survey can be used as a guideline for hydrologists as well as climate scientists in choosing the proper ML method according to the prediction task.

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References

228

[1]

Scholz "Predicting dam failure risk for sustainable flood retention basins: A generic case study for the wider greater manchester area" Comput. Environ. Urban Syst. (2012) 10.1016/j.compenvurbsys.2012.02.003

[2]

Xie "Evacuation zone modeling under climate change: A data-driven method" J. Infrastruct. Syst. (2017) 10.1061/(asce)is.1943-555x.0000369

[3]

Pitt, M. (2008). Learning Lessons from the 2007 Floods, Cabinet Office.

[4]

Lohani "Improving real time flood forecasting using fuzzy inference system" J. Hydrol. (2014) 10.1016/j.jhydrol.2013.11.021

[5]

Mosavi, A., Bathla, Y., and Varkonyi-Koczy, A. (2017). Predicting the Future Using Web Knowledge: State of the Art Survey. Recent Advances in Technology Research and Education, Springer. 10.1007/978-3-319-67459-9_42

[6]

Zhao "Representation and prediction of the indian ocean dipole in the poama seasonal forecast model" Q. J. R. Meteorol. Soc. (2009) 10.1002/qj.370

[7]

Borah "Hydrologic procedures of storm event watershed models: A comprehensive review and comparison" Hydrol. Process. (2011) 10.1002/hyp.8075

[8]

Costabile "A storm event watershed model for surface runoff based on 2D fully dynamic wave equations" Hydrol. Process. (2013) 10.1002/hyp.9237

[9]

Cea "Experimental validation of two-dimensional depth-averaged models for forecasting rainfall–runoff from precipitation data in urban areas" J. Hydrol. (2010) 10.1016/j.jhydrol.2009.12.020

[10]

"Rainfall/runoff simulation with 2D full shallow water equations: Sensitivity analysis and calibration of infiltration parameters" J. Hydrol. (2016) 10.1016/j.jhydrol.2016.03.021

[11]

Murillo "Influence of mesh structure on 2D full shallow water equations and SCS curve number simulation of rainfall/runoff events" J. Hydrol. (2012)

[12]

Costabile "Comparative analysis of overland flow models using finite volume schemes" J. Hydroinform. (2012) 10.2166/hydro.2011.077

[13]

Xia "An efficient and stable hydrodynamic model with novel source term discretization schemes for overland flow and flood simulations" Water Resour. Res. (2017) 10.1002/2016wr020055

[14]

Liang "A simple hydrologically based model of land surface water and energy fluxes for general circulation models" J. Geophys. Res. Atmos. (1994) 10.1029/94jd00483

[15]

Costabile "Enhancing river model set-up for 2-D dynamic flood modelling" Environ. Model. Softw. (2015) 10.1016/j.envsoft.2015.01.009

[16]

Nayak, P., Sudheer, K., Rangan, D., and Ramasastri, K. (2005). Short-term flood forecasting with a neurofuzzy model. Water Resour. Res., 41. 10.1029/2004wr003562

[17]

Kim "Urban flood modeling with porous shallow-water equations: A case study of model errors in the presence of anisotropic porosity" J. Hydrol. (2015) 10.1016/j.jhydrol.2015.01.059

[18]

McAneney "The 2011 brisbane floods: Causes, impacts and implications" Water (2011) 10.3390/w3041149

[19]

Lee "Operational rainfall prediction on meso-γ scales for hydrologic applications" Water Resour. Res. (1996) 10.1029/95wr03814

[20]

Shrestha "Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose" Hydrol. Earth Syst. Sci. (2013) 10.5194/hess-17-1913-2013

[21]

Bellos "A hybrid method for flood simulation in small catchments combining hydrodynamic and hydrological techniques" J. Hydrol. (2016) 10.1016/j.jhydrol.2016.06.040

[22]

Bout "The validity of flow approximations when simulating catchment-integrated flash floods" J. Hydrol. (2018) 10.1016/j.jhydrol.2017.11.033

[23]

Costabile "Flood mapping using lidar dem. Limitations of the 1-D modeling highlighted by the 2-D approach" Nat. Hazards (2015) 10.1007/s11069-015-1606-0

[24]

Valipour "Parameters estimate of autoregressive moving average and autoregressive integrated moving average models and compare their ability for inflow forecasting" J. Math. Stat. (2012) 10.3844/jmssp.2012.330.338

[25]

Adamowski, J., Fung Chan, H., Prasher, S.O., Ozga-Zielinski, B., and Sliusarieva, A. (2012). Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in montreal, Canada. Water Resour. Res., 48. 10.1029/2010wr009945

[26]

Valipour "Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir" J. Hydrol. (2013) 10.1016/j.jhydrol.2012.11.017

[27]

Chow, V.T., Maidment, D.R., and Larry, W. (1988). Mays. Applied hydrology, International Edition; MacGraw-Hill, Inc.

[28]

Aziz "Application of artificial neural networks in regional flood frequency analysis: A case study for australia" Stoch. Environ. Res. Risk Assess. (2014) 10.1007/s00477-013-0771-5

[29]

Kroll "Probability distribution of low streamflow series in the united states" J. Hydrol. Eng. (2002) 10.1061/(asce)1084-0699(2002)7:2(137)

[30]

Mackey "Ensemble forecast of a typhoon flood event" Weather Forecast. (2001) 10.1175/1520-0434(2001)016<0399:efoatf>2.0.co;2

[31]

Haddad "Regional flood frequency analysis in eastern australia: Bayesian GLS regression-based methods within fixed region and ROI framework–quantile regression vs. Parameter regression technique" J. Hydrol. (2012) 10.1016/j.jhydrol.2012.02.012

[32]

Thompson, S.A. (2017). Hydrology for Water Management, CRC Press. 10.1201/9780203751435

[33]

Kerkhoven "A modified ISBA surface scheme for modeling the hydrology of Athabasca river basin with GCM-scale data" Adv. Water Resour. (2006) 10.1016/j.advwatres.2005.07.016

[34]

Burnash, R.J., Ferral, R.L., and McGuire, R.A. (1973). A Generalized Streamflow Simulation System, Conceptual Modeling for Digital Computers, Stanford University.

[35]

A physically based description of floodplain inundation dynamics in a global river routing model

Dai Yamazaki, Shinjiro Kanae, Hyungjun Kim et al.

Water Resources Research 10.1029/2010wr009726

[36]

Fawcett "A comparison of two seasonal rainfall forecasting systems for Australia" Aust. Meteorol. Oceanogr. J. (2010) 10.22499/2.6001.002

[37]

Mekanik "Multiple regression and artificial neural network for long-term rainfall forecasting using large scale climate modes" J. Hydrol. (2013) 10.1016/j.jhydrol.2013.08.035

[38]

Mosavi, A., Rabczuk, T., and Varkonyi-Koczy, A.R. (2017). Reviewing the novel machine learning tools for materials design. Recent Advances in Technology Research and Education, Springer. 10.1007/978-3-319-67459-9_7

[39]

Abbot "Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks" Atmos. Res. (2014) 10.1016/j.atmosres.2013.11.002

[40]

Fox "A bayesian quantitative precipitation nowcast scheme" Weather Forecast. (2005) 10.1175/waf845.1

[41]

Merz "Fluvial flood risk management in a changing world" Nat. Hazards Earth Syst. Sci. (2010) 10.5194/nhess-10-509-2010

[42]

Xu "Short-term inflow forecasting using an artificial neural network model" Hydrol. Process. (2002) 10.1002/hyp.1013

[43]

"Accurate precipitation prediction with support vector classifiers: A study including novel predictive variables and observational data" Atmos. Res. (2014) 10.1016/j.atmosres.2014.01.012

[44]

Kim "A real-time forecast model using artificial neural network for after-runner storm surges on the Tottori Coast, Japan" Ocean Eng. (2016) 10.1016/j.oceaneng.2016.06.017

[45]

Mosavi, A., and Edalatifar, M. (2018). A.; Edalatifar, M. A Hybrid Neuro-Fuzzy Algorithm for Prediction of Reference Evapotranspiration. Recent Advances in Technology Research and Education, Springer. 10.1007/978-3-319-99834-3_31

[46]

Dineva, A., Várkonyi-Kóczy, A.R., and Tar, J.K. (2014, January 3–5). Fuzzy expert system for automatic wavelet shrinkage procedure selection for noise suppression. Proceedings of the 2014 IEEE 18th International Conference on Intelligent Engineering Systems (INES), Tihany, Hungary. 10.1109/ines.2014.6909361

[47]

Least Squares Support Vector Machine Classifiers

J.A.K. Suykens, J. Vandewalle

Neural Processing Letters 1999 10.1023/a:1018628609742

[48]

Gizaw "Regional flood frequency analysis using support vector regression under historical and future climate" J. Hydrol. (2016) 10.1016/j.jhydrol.2016.04.041

[49]

Mosavi "Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network" Eng. Appl. Comput. Fluid Mech. (2018)

[50]

Kasiviswanathan "Potential application of wavelet neural network ensemble to forecast streamflow for flood management" J. Hydrol. (2016) 10.1016/j.jhydrol.2016.02.044

Showing 50 of 228 references

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IEEE Access

Metrics

1,063

Citations

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References

Details

Published: Oct 27, 2018
Vol/Issue: 10(11)
Pages: 1536
License: View

Authors

A

Amir Mosavi

Department of Computer Science (IDI), Norwegian University of Science and Technology (NTNU), NO-7491 Trondheim, Norway

P

Pinar Ozturk

Department of Computer Science (IDI), Norwegian University of Science and Technology (NTNU), NO-7491 Trondheim, Norway

K

Kwok-Wing Chau

Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China

Cite This Article

Amir Mosavi, Pinar Ozturk, Kwok-Wing Chau (2018). Flood Prediction Using Machine Learning Models: Literature Review. Water, 10(11), 1536. https://doi.org/10.3390/w10111536

Flood Prediction Using Machine Learning Models: Literature Review

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