Flood Stage Forecasting Using Machine-Learning Methods: A Case Study on the Parma River (Italy)

Real-time river flood forecasting models can be useful for issuing flood alerts and reducing or preventing inundations. To this end, machine-learning (ML) methods are becoming increasingly popular thanks to their low computational requirements and to their reliance on observed data only. This work aimed to evaluate the ML models’ capability of predicting flood stages at a critical gauge station, using mainly upstream stage observations, though downstream levels should also be included to consider backwater, if present. The case study selected for this analysis was the lower stretch of the Parma River (Italy), and the forecast horizon was extended up to 9 h. The performances of three ML algorithms, namely Support Vector Regression (SVR), MultiLayer Perceptron (MLP), and Long Short-term Memory (LSTM), were compared herein in terms of accuracy and computational time. Up to 6 h ahead, all models provided sufficiently accurate predictions for practical purposes (e.g., Root Mean Square Error 0.99), while peak levels were poorly predicted for longer lead times. Moreover, the results suggest that the LSTM model, despite requiring the longest training time, is the most robust and accurate in predicting peak values, and it should be preferred for setting up an operational forecasting system.

[1]  Jian Wu,et al.  Flash Flood Forecasting Based on Long Short-Term Memory Networks , 2019, Water.

[2]  Mohammad Ali Ghorbani,et al.  Comparison of three artificial intelligence techniques for discharge routing , 2011 .

[3]  Jun-Haeng Heo,et al.  Hourly Water Level Forecasting at Tributary Affected by Main River Condition , 2017 .

[4]  Linda See,et al.  Applying soft computing approaches to river level forecasting , 1999 .

[5]  A. Soldati,et al.  River flood forecasting with a neural network model , 1999 .

[6]  W. Lee,et al.  Simultaneous hydrological prediction at multiple gauging stations using the NARX network for Kemaman catchment, Terengganu, Malaysia , 2016 .

[7]  Zaher Mundher Yaseen,et al.  An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction , 2019, Journal of Hydrology.

[8]  Nitin Muttil,et al.  Runoff Estimation by Machine Learning Methods and Application to the Euphrates Basin in Turkey , 2014 .

[9]  Changjun Liu,et al.  A review of advances in China’s flash flood early-warning system , 2018, Natural Hazards.

[10]  Lothar Fuchs,et al.  An ensemble neural network model for real-time prediction of urban floods , 2019, Journal of Hydrology.

[11]  R. Muñoz‐Carpena,et al.  Performance evaluation of hydrological models: Statistical significance for reducing subjectivity in goodness-of-fit assessments , 2013 .

[12]  George P. Karatzas,et al.  Statistical analysis and ANN modeling for predicting hydrological extremes under climate change scenarios: the example of a small Mediterranean agro-watershed. , 2015, Journal of environmental management.

[13]  G. Pender,et al.  A deep convolutional neural network model for rapid prediction of fluvial flood inundation , 2020, Journal of Hydrology.

[14]  Niranjan Pramanik,et al.  Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model , 2010, Comput. Geosci..

[15]  K. P. Sudheer,et al.  Short‐term flood forecasting with a neurofuzzy model , 2005 .

[16]  Li-Chiu Chang,et al.  Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting , 2020 .

[17]  Kwok-wing Chau,et al.  Flood Prediction Using Machine Learning Models: Literature Review , 2018, Water.

[18]  Hui Li,et al.  Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation , 2018, Water.

[19]  Huicheng Zhou,et al.  Flash Flood Forecasting Using Support Vector Regression Model in a Small Mountainous Catchment , 2019, Water.

[20]  Jungwook Kim,et al.  Development of Water Level Prediction Models Using Machine Learning in Wetlands: A Case Study of Upo Wetland in South Korea , 2019 .

[21]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[22]  J. Aerts,et al.  Global exposure to river and coastal flooding - long term trends and changes , 2012 .

[23]  Gwo-Fong Lin,et al.  Real-Time Water-Level Forecasting Using Dilated Causal Convolutional Neural Networks , 2019, Water Resources Management.

[24]  Paul Leahy,et al.  Structural optimisation and input selection of an artificial neural network for river level prediction , 2008 .

[25]  Giha Lee,et al.  Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting , 2019, Water.

[26]  Ozgur Kisi,et al.  Two hybrid Artificial Intelligence approaches for modeling rainfall–runoff process , 2011 .

[27]  S. Pistre,et al.  Identification of spatial and temporal contributions of rainfalls to flash floods using neural network modelling: case study on the Lez basin (southern France) , 2015 .

[28]  J. Monaghan.  Smoothed Particle Hydrodynamics and Its Diverse Applications , 2012 .

[29]  F. Granata,et al.  Support Vector Regression for Rainfall-Runoff Modeling in Urban Drainage: A Comparison with the EPA’s Storm Water Management Model , 2016 .

[30]  Hitoshi Tanaka,et al.  Developing a hybrid multi‐model for peak flood forecasting , 2009 .

[31]  L. Chua,et al.  Influence of lag time on event-based rainfall–runoff modeling using the data driven approach , 2012 .

[32]  Hyun Il Kim,et al.  Flood Evacuation Routes Based on Spatiotemporal Inundation Risk Assessment , 2020, Water.

[33]  Jerónimo Puertas,et al.  A rapid flood inundation model for hazard mapping based on least squares support vector machine regression , 2019, Journal of Flood Risk Management.

[34]  Zaher Mundher Yaseen,et al.  Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review , 2017, Theoretical and Applied Climatology.

[35]  Christian W. Dawson,et al.  Hydrological modelling using artificial neural networks , 2001 .

[36]  I-Fan Chang,et al.  Support vector regression for real-time flood stage forecasting , 2006 .

[37]  Eric Huang,et al.  Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control , 2014 .

[38]  Vijay P. Singh,et al.  Flood Hydrograph Prediction Using Machine Learning Methods , 2018, Water.

[39]  Albert S. Chen,et al.  Longitudinal stage profiles forecasting in rivers for flash floods , 2010 .

[40]  A. R. Mahmud,et al.  An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia , 2012, Environmental Earth Sciences.

[41]  K. P. Sudheer,et al.  Potential application of wavelet neural network ensemble to forecast streamflow for flood management , 2016 .

[42]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[43]  Yen-Chang Chen,et al.  Flood forecasting using radial basis function neural networks , 2001, IEEE Trans. Syst. Man Cybern. Part C.

[44]  Qing Zhang,et al.  Neurocomputing in surface water hydrology and hydraulics: A review of two decades retrospective, current status and future prospects , 2020 .