Lake water-level fluctuation forecasting using machine learning models: a systematic review
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REVIEW ARTICLE
Lake water-level fluctuation forecasting using machine learning models: a systematic review Senlin Zhu 1,2
&
Hongfang Lu 3 & Mariusz Ptak 4 & Jiangyu Dai 2 & Qingfeng Ji 1
Received: 3 July 2020 / Accepted: 17 September 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Lake water-level fluctuation is a complex and dynamic process, characterized by high stochasticity and nonlinearity, and difficult to model and forecast. In recent years, applications of machine learning (ML) models have yielded substantial progress in forecasting lake water-level fluctuations. This paper presents a comprehensive review of the applications of ML models for modeling water-level dynamics in lakes. Among the many existing ML models, seven popular ML model types are reviewed: (1) artificial neural network (ANN); (2) support vector machine (SVM); (3) artificial neuro-fuzzy inference system (ANFIS); (4) hybrid models, such as hybrid wavelet-artificial neural network (WA-ANN) model, hybrid wavelet-artificial neuro-fuzzy inference system (WA-ANFIS) model, and hybrid wavelet-support vector machine (WA-SVM) model; (5) evolutionary models, such as gene expression programming (GEP) and genetic programming (GP); (6) extreme learning machine (ELM); and (7) deep learning (DL). Model inputs, data split, model performance criteria, and model inter-comparison as well as the associated issues are discussed. The advantages and limitations of the established ML models are also discussed. Some specific directions for future research are also offered. This review provides a new vision for hydrologists and water resources planners for sustainable management of lakes. Keywords Lakes . Water-level modeling . Stochasticity . Nonlinearity . Machine learning
Abbreviations ADP AI AIC ANFIS ANN AR ARMA BNN
Absolute deviation percent Artificial intelligence Akaike information criterion Adaptive neuro-fuzzy inference system Artificial neural network Autoregressive Autoregressive moving average Bayesian neural network
CC DL ELM ERM ESN FA FFNN GAANN GEP
Correlation coefficient Deep learning Extreme learning machine Empirical risk minimization Echo state network Firefly algorithm Feed-forward neural network Genetic algorithm artificial neural network Gene expression programming
Responsible editor: Marcus Schulz * Senlin Zhu [email protected] * Jiangyu Dai [email protected]
1
College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225127, China
2
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
3
Division of Construction Engineering and Management, Purdue University, West Lafayette, IN 47907, USA
4
Department of Hydrology and Water Management, Adam Mickiewicz University, Krygowskiego 10, 61-680 Poznan, Poland
* Qingfeng Ji [email protected] Hongfang Lu [email protected] Mariusz Ptak [email protected]
Environ Sci Pollut Res
GMDH GP KGE LLNF LMI LSTM MA MAE MAPE ML MLPNN MLPNN-FA MSE MSRE MS4E NMSE NRMSD NSC NWN P
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