PMID- 32978734
OWN - NLM
STAT- MEDLINE
DCOM- 20201126
LR  - 20210615
IS  - 1614-7499 (Electronic)
IS  - 0944-1344 (Linking)
VI  - 27
IP  - 36
DP  - 2020 Dec
TI  - Lake water-level fluctuation forecasting using machine learning models: a 
      systematic review.
PG  - 44807-44819
LID - 10.1007/s11356-020-10917-7 [doi]
AB  - 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.
FAU - Zhu, Senlin
AU  - Zhu S
AUID- ORCID: 0000-0001-7561-8372
AD  - College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, 
      225127, China. slzhu@nhri.cn.
AD  - State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, 
      Nanjing Hydraulic Research Institute, Nanjing, 210029, China. slzhu@nhri.cn.
FAU - Lu, Hongfang
AU  - Lu H
AD  - Division of Construction Engineering and Management, Purdue University, West 
      Lafayette, IN, 47907, USA.
FAU - Ptak, Mariusz
AU  - Ptak M
AD  - Department of Hydrology and Water Management, Adam Mickiewicz University, 
      Krygowskiego 10, 61-680, Poznan, Poland.
FAU - Dai, Jiangyu
AU  - Dai J
AD  - State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, 
      Nanjing Hydraulic Research Institute, Nanjing, 210029, China. jydai@nhri.cn.
FAU - Ji, Qingfeng
AU  - Ji Q
AD  - College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, 
      225127, China. qfji@yzu.edu.cn.
LA  - eng
GR  - 2018YFC0407203/National Key R&D Program of China/
GR  - 2018M640499/China Postdoctoral Science Foundation/
PT  - Journal Article
PT  - Review
PT  - Systematic Review
DEP - 20200925
PL  - Germany
TA  - Environ Sci Pollut Res Int
JT  - Environmental science and pollution research international
JID - 9441769
SB  - IM
MH  - Forecasting
MH  - *Lakes
MH  - *Machine Learning
MH  - Neural Networks, Computer
MH  - Support Vector Machine
OTO - NOTNLM
OT  - Lakes
OT  - Machine learning
OT  - Nonlinearity
OT  - Stochasticity
OT  - Water-level modeling
EDAT- 2020/09/27 06:00
MHDA- 2020/11/27 06:00
CRDT- 2020/09/26 05:31
PHST- 2020/07/03 00:00 [received]
PHST- 2020/09/17 00:00 [accepted]
PHST- 2020/09/27 06:00 [pubmed]
PHST- 2020/11/27 06:00 [medline]
PHST- 2020/09/26 05:31 [entrez]
AID - 10.1007/s11356-020-10917-7 [pii]
AID - 10.1007/s11356-020-10917-7 [doi]
PST - ppublish
SO  - Environ Sci Pollut Res Int. 2020 Dec;27(36):44807-44819. doi: 
      10.1007/s11356-020-10917-7. Epub 2020 Sep 25.