Abstract
Forecasting systems for foreseeing water levels and flow rates have become necessary to mitigate climate change negative impacts. Most of these systems are based on powerful tools such as Artificial Intelligence (AI) methods. This paper presents a comprehensive review of AI methods for high-flow extremes prediction. The review starts with an overview of the state-of-the-art AI techniques and examples of their application, followed by a SWOT analysis to benchmark their predictive capability based on set of criteria. Finally, the most suitable AI methods for short-term and/or long-term prediction, based on a rigorous suitability assessment are proposed. As a result, Fourteen AI methods have been identified. Their evaluation revealed that the methods that averagely behave the best for achieving high-flow extremes prediction are ANNs, SVMs, wavelets and Bayesian methods, at all-time scales. The latter, as stochastic methods, have the privilege by their cheap computation cost, their reliability and ability to handle hydrological uncertainty, and their capacity to perform causal relationships between features. This study also urges researchers to further explore the predictive potential of decision trees, ensembles, CNNs, MARS, GP and agent-based methods for high-flow extremes.
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The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
References
Abirami S, Chitra P (2020) Chapter Fourteen - Energy-efficient edge based real-time healthcare support system. In: Raj P, Evangeline PBT-A (eds) The Digital Twin Paradigm for Smarter Systems and Environments: The Industry Use Cases. Elsevier, pp 339–368. https://doi.org/10.1016/bs.adcom.2019.09.007
Aliyev R, Salehi S, Aliyev R (2019) Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting. Sustain 11. https://doi.org/10.3390/su11030793
Aydın A, Yucedag I, Eker R, FLOOD FORECASTING USING TRANSBOUNDARY DATA WITH THE FUZZY INFERENCE SYSTEM (2018) THE MARITZA (MERIÇ) RIVER. Int J Adv Res 6:568–579. https://doi.org/10.21474/IJAR01/8175
Bafitlhile TM, Li Z (2019) Applicability of ε-Support Vector Machine and Artificial Neural Network for Flood Forecasting in Humid, Semi-Humid and Semi-Arid Basins in China. Water 11
Cai Z, Liu L, Chen B, Wang Y (2021) Artificial Intelligence: From Beginning to Date. World Scientific
Chen C, Li K, Duan M, Li K (2017) Chap. 6 - Extreme Learning Machine and Its Applications in Big Data Processing. In: Hsu H-H, Chang C-Y (eds) Hsu C-HBT-BDA for S-NCI (eds) Intelligent Data-Centric Systems. Academic Press, pp 117–150
Chen C, Hui Q, Xie W et al (2021) Convolutional Neural Networks for forecasting flood process in Internet-of-Things enabled smart city. Comput Networks 186:107744. https://doi.org/10.1016/j.comnet.2020.107744
Choubin B, Darabi H, Rahmati O et al (2018) River suspended sediment modelling using the CART model: A comparative study of machine learning techniques. Sci Total Environ 615:272–281. https://doi.org/10.1016/j.scitotenv.2017.09.293
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297. https://doi.org/10.1007/BF00994018
Cui F, Salih SQ, Choubin B et al (2020) Newly explored machine learning model for river flow time series forecasting at Mary River, Australia. Environ Monit Assess 192:761. https://doi.org/10.1007/s10661-020-08724-1
D’Addona DM (2014) In: Laperrière L, Reinhart G (eds) Neural Network BT - CIRP Encyclopedia of Production Engineering. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 911–918
Danandeh Mehr A, Ghadimi S, Marttila H, Torabi Haghighi A (2022) A new evolutionary time series model for streamflow forecasting in boreal lake-river systems. Theor Appl Climatol. https://doi.org/10.1007/s00704-022-03939-3
Goli I, Azadi H, Nooripoor M et al (2021) Evaluating the Productivity of Paddy Water Resources through SWOT Analysis: The Case of Northern Iran. Water 13
Goodarzi L, Banihabib ME, Roozbahani A (2019) A decision-making model for flood warning system based on ensemble forecasts. J Hydrol 573:207–219. https://doi.org/10.1016/j.jhydrol.2019.03.040
Goz E, Yuceer M, Karadurmus E (2019) Total Organic Carbon Prediction with Artificial Intelligence Techniques. In: Kiss AA, Zondervan E, Lakerveld R, Özkan LBT-CACE (eds) 29 European Symposium on Computer Aided Process Engineering. Elsevier, pp 889–894
Herath HMVV, Chadalawada J, Babovic V (2021) Genetic programming for hydrological applications: to model or to forecast that is the question. J Hydroinformatics 23:740–763. https://doi.org/10.2166/hydro.2021.179
Hussain F, Wu R-S, Wang J-X (2021) Comparative study of very short-term flood forecasting using physics-based numerical model and data-driven prediction model. Nat Hazards 107:249–284. https://doi.org/10.1007/s11069-021-04582-3
Izadkhah H (2022) Chap. 12 - Recurrent neural networks: generating new molecules and proteins sequence classification. In: Izadkhah HBT-DL in B (ed). Academic Press, pp 321–346
Jain P, Coogan SCP, Subramanian SG et al (2020) A review of machine learning applications in wildfire science and management. Environ Rev 28:478–505. https://doi.org/10.1139/er-2020-0019
Kasiviswanathan KS, He J, Sudheer KP, Tay J-H (2016) Potential application of wavelet neural network ensemble to forecast streamflow for flood management. J Hydrol 536:161–173. https://doi.org/10.1016/j.jhydrol.2016.02.044
Katsavrias C, Papadimitriou C, Hillaris A, Balasis G (2022) Application of Wavelet Methods in the Investigation of Geospace Disturbances: A Review and an Evaluation of the Approach for Quantifying Wavelet Power. Atmos 13. https://doi.org/10.3390/atmos13030499
Kinage C, Kalgutkar A, Parab A et al (2019) Performance Evaluation of Different Machine Learning Based Algorithms for Flood Prediction and Model for Real Time Flood Prediction. In: 2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA). pp 1–7
Koprinska I, Wu D, Wang Z (2018) Convolutional Neural Networks for Energy Time Series Forecasting. In: 2018 International Joint Conference on Neural Networks (IJCNN). pp 1–8
Kumar P (2021) Sports Biomechanics and Kinesiology. Friends Publications (India)
Le X-H, Ho HV, Lee G, Jung S (2019) Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting. Water 11
Li Y, Chen W (2020) A Comparative Performance Assessment of Ensemble Learning for Credit Scoring. Math 8. https://doi.org/10.3390/math8101756
Linghu B, Chen F (2014) An Intelligent Multi-agent Approach for Flood Disaster Forecasting Utilizing Case Based Reasoning. In: 2014 Fifth International Conference on Intelligent Systems Design and Engineering Applications. pp 182–185
Linh NTT, Ruigar H, Golian S et al (2021) Flood prediction based on climatic signals using wavelet neural network. Acta Geophys 69:1413–1426. https://doi.org/10.1007/s11600-021-00620-7
Lu P, Abedi V, Mei Y et al (2015) Chap. 1 - Supervised Learning with the Artificial Neural Networks Algorithm for Modeling Immune Cell Differentiation. In: Tran QN (ed) Arabnia Bioinformatics, and Systems Biology HBT-ET in CB (eds) Emerging Trends in Computer Science and Applied Computing. Morgan Kaufmann, Boston, pp 1–18
Maes P (1995) Artificial Life Meets Entertainment: Lifelike Autonomous Agents. Commun ACM 38:108–114. https://doi.org/10.1145/219717.219808
Molina J-L, Zazo S, Martín-Casado A-M, Patino-Alonso M-C (2020) Rivers’ temporal sustainability through the evaluation of predictive runoff methods. Sustainability 12:1720. https://doi.org/10.3390/su12051720
Mosavi A, Ozturk P, Chau K (2018) Flood Prediction Using Machine Learning Models: Literature Review. Water 10. https://doi.org/10.3390/w10111536
Petousi I, Fountoulakis M, Papadaki A, Sabathianakis I, Daskalakis G, Nikolaidis N, Manios T (2017) Assessment of Water Management Measures through SWOT Analysis: The Case of Crete Island, Greece. Int J Environ Sci 2:59–62
Rezaie-balf M, Naganna SR, Ghaemi A, Deka PC (2017) Wavelet coupled MARS and M5 Model Tree approaches for groundwater level forecasting. J Hydrol 553:356–373. https://doi.org/10.1016/j.jhydrol.2017.08.006
Rocha J (2017) Introductory Chapter: Multi-Agent Systems. In: Boavida-Portugal I (ed). IntechOpen, Rijeka, p Ch. 1. https://doi.org/10.5772/intechopen.70241
Sahoo BB, Jha R, Singh A, Kumar D (2019) Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting. Acta Geophys 67:1471–1481. https://doi.org/10.1007/s11600-019-00330-1
Samuel OW, Asogbon GM, Sangaiah AK, Li G (2018) Chap. 5 - Computational Intelligence Enabling the Development of Efficient Clinical Decision Support Systems: Case Study of Heart Failure. In: Sangaiah AK, Sheng M, Zhang (eds) ZBT-CI for MBD on the C with EA (eds) Intelligent Data-Centric Systems. Academic Press, pp 123–133
Satapathy SK, Dehuri S, Jagadev AK, Mishra S (2019) Chap. 1 - Introduction. In: Satapathy SK, Dehuri S, Jagadev AK, Mishra SBT-EEGBSC for ESDD (eds). Academic Press, pp 1–25. https://doi.org/10.1016/B978-0-12-817426-5.00001-6
Sawangnate C, Chaisri B, Kittipongvises S (2022) Flood Hazard Mapping and Flood Preparedness Literacy of the Elderly Population Residing in Bangkok, Thailand. Water 14
Schoppa L, Disse M, Bachmair S (2020) Evaluating the performance of random forest for large-scale flood discharge simulation. J Hydrol 590:125531. https://doi.org/10.1016/j.jhydrol.2020.125531
Shajun Nisha S, Nagoor Meeral M (2021) 9 - Applications of deep learning in biomedical engineering. In: Balas VE, Mishra BK, Kumar RBT-H of DL in BE (eds). Academic Press, pp 245–270
Simoen E, Lombaert G (2016) In: Chatzi E, Papadimitriou C (eds) Bayesian Parameter Estimation BT - Identification Methods for Structural Health Monitoring. Springer International Publishing, Cham, pp 89–115
Stegmaier J, Mikut R (2017) Fuzzy-based propagation of prior knowledge to improve large-scale image analysis pipelines. PLoS ONE 12:e0187535–e0187535. https://doi.org/10.1371/journal.pone.0187535
Terzi Ö, Ergin G (2014) Forecasting of monthly river flow with autoregressive modeling and data-driven techniques. Neural Comput Appl 25:179–188. https://doi.org/10.1007/s00521-013-1469-9
Teuwen J, Moriakov N (2020) Chap. 20 - Convolutional neural networks. In: Zhou SK, Rueckert D, Fichtinger GBT-H of MIC and CAI (eds) The Elsevier and MICCAI Society Book Series. Academic Press, pp 481–501
Wang H, Wang H, Wu Z, Zhou Y (2021) Using Multi-Factor Analysis to Predict Urban Flood Depth Based on Naive Bayes.Water13
Wang H, Hu Y, Guo Y et al (2022) Urban flood forecasting based on the coupling of numerical weather model and stormwater model: A case study of Zhengzhou city. J Hydrol Reg Stud 39. https://doi.org/10.1016/j.ejrh.2021.100985
Wei W, Jia X, Liu Y, Yu X (2018) In: Cai Y, Ishikawa Y, Xu J (eds) Travel Time Forecasting with Combination of Spatial-Temporal and Time Shifting Correlation in CNN-LSTM Neural Network BT - Web and Big Data. Springer International Publishing, Cham, pp 297–311
Wu J, Liu H, Wei G et al (2019) Flash Flood Forecasting Using Support Vector Regression Model in a Small Mountainous Catchment. https://doi.org/10.3390/w11071327. Water 11
Wu Z, Zhou Y, Wang H, Jiang Z (2020) 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.2020.137077
Yunpeng PSD X (2004) M5 Model Trees and Neural Networks: Application to Flood Forecasting in the Upper Reach of the Huai River in China. J Hydrol Eng 9:491–501. https://doi.org/10.1061/(ASCE)1084-0699(2004)9:6(491)
Zhao G, Pang B, Xu Z et al (2018) Mapping flood susceptibility in mountainous areas on a national scale in China. Sci Total Environ 615:1133–1142. https://doi.org/10.1016/j.scitotenv.2017.10.037
Zounemat-Kermani M, Batelaan O, Fadaee M, Hinkelmann R (2021) Ensemble machine learning paradigms in hydrology: A review. J Hydrol 598:126266. https://doi.org/10.1016/j.jhydrol.2021.126266
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The financial support received to M.H. from the International Centre for Advanced Mediterranean Agronomic Studies (CIHEAM) comprised a scholarship for developing a Master Thesis.
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M.H. and J.L.M. conceived, designed, and led the research and paper editing; Both. made the research conceptualization and analytical development. J.L.M supervised all actions.
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Hamitouche, M., Molina, JL. A Review of AI Methods for the Prediction of High-Flow Extremal Hydrology. Water Resour Manage 36, 3859–3876 (2022). https://doi.org/10.1007/s11269-022-03240-y
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DOI: https://doi.org/10.1007/s11269-022-03240-y