Development of Water Level Prediction Models Using Machine Learning in Wetlands: A Case Study of Upo Wetland in South Korea

Choi, Changhyun; Kim, Jung-Wook; Han, Heechan; Han, Dejun; Kim, Hung Soo

doi:10.3390/w12010093

Cited by 84 publications

(44 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For SVR, penalty and kernel parameters were selected as the hyperparameters [46]. According to Choi et al [54], the two main parameters to be determined in an RFR model are N split and N tree , where N split is the minimum number of samples required to split. For MLPR, a single hidden layer is used because of its relatively wide application.…”

Section: Analysis Of Model Training Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of River Stage Using Multistep-Ahead Machine Learning Techniques for a Tidal River of Taiwan

Guo

Chen

Yeh

et al. 2021

Water

View full text Add to dashboard Cite

Time-series prediction of a river stage during typhoons or storms is essential for flood control or flood disaster prevention. Data-driven models using machine learning (ML) techniques have become an attractive and effective approach to modeling and analyzing river stage dynamics. However, relatively new ML techniques, such as the light gradient boosting machine regression (LGBMR), have rarely been applied to predict the river stage in a tidal river. In this study, data-driven ML models were developed under a multistep-ahead prediction framework and evaluated for river stage modeling. Four ML techniques, namely support vector regression (SVR), random forest regression (RFR), multilayer perceptron regression (MLPR), and LGBMR, were employed to establish data-driven ML models with Bayesian optimization. The models were applied to simulate river stage hydrographs of the tidal reach of the Lan-Yang River Basin in Northeastern Taiwan. Historical measurements of rainfall, river stages, and tidal levels were collected from 2004 to 2017 and used for training and validation of the four models. Four scenarios were used to investigate the effect of the combinations of input variables on river stage predictions. The results indicated that (1) the tidal level at a previous stage significantly affected the prediction results; (2) the LGBMR model achieves more favorable prediction performance than the SVR, RFR, and MLPR models; and (3) the LGBMR model could efficiently and accurately predict the 1–6-h river stage in the tidal river. This study provides an extensive and insightful comparison of four data-driven ML models for river stage forecasting that can be helpful for model selection and flood mitigation.

show abstract

Section: Analysis Of Model Training Resultsmentioning

confidence: 99%

“…The advantages of RFR are its simplicity and the low number of required parameters. The RFR algorithm, as shown in Figure 1b, is summarized as follows [20,26,27,54]:…”

Section: Rfrmentioning

confidence: 99%

Prediction of River Stage Using Multistep-Ahead Machine Learning Techniques for a Tidal River of Taiwan

Guo

Chen

Yeh

et al. 2021

Water

View full text Add to dashboard Cite

show abstract

“…Support vector machines can apply kernel functions to map original data into a higher dimension and to the input for minimizing the ε-insensitive loos function [48]. The typical kernel functions include Polynomial, Sigmoid, and Radial Basis Function (RBF).…”

Section: Support Vector Machinementioning

confidence: 99%

“…The typical kernel functions include Polynomial, Sigmoid, and Radial Basis Function (RBF). This study used the RBF to SVM model since it is a very popular kernel function used in SVM model and shows high reliability [48]. The SVM model is applicable not only to classification but also to regression for prediction by applying an ε-insensitive loss function to the SVM.…”

Section: Support Vector Machinementioning

confidence: 99%

Deep Learning with Long Short Term Memory Based Sequence-to-Sequence Model for Rainfall-Runoff Simulation

Han

Choi

Jung

et al. 2021

Water

Self Cite

View full text Add to dashboard Cite

Accurate runoff prediction is one of the important tasks in various fields such as agriculture, hydrology, and environmental studies. Recently, with massive improvements of computational system and hardware, the deep learning-based approach has recently been applied for more accurate runoff prediction. In this study, the long short-term memory model with sequence-to-sequence structure was applied for hourly runoff predictions from 2015 to 2019 in the Russian River basin, California, USA. The proposed model was used to predict hourly runoff with lead time of 1–6 h using runoff data observed at upstream stations. The model was evaluated in terms of event-based performance using the statistical metrics including root mean square error, Nash-Sutcliffe Efficiency, peak runoff error, and peak time error. The results show that proposed model outperforms support vector machine and conventional long short-term memory models. In addition, the model has the best predictive ability for runoff events, which means that it can be effective for developing short-term flood forecasting and warning systems. The results of this study demonstrate that the deep learning-based approach for hourly runoff forecasting has high predictive power and sequence-to-sequence structure is effective method to improve the prediction results.

show abstract

“…In Choi et al (2020), authors compare some implementations of machine learning algorithms to predict water level temporal series in a Korean wetland, with emphasis in water level peaks. They tested algorithms like neural network, decision tree random forest and support vector machine concluding that the random forest had the better prediction performance.…”

Section: Literature Review Of Hydrological Predictionmentioning

confidence: 99%

Aprendizado Conjunto Aplicado Na Predição Do Mercado De Ações Brasileiro

Queiroz¹,

Meinerz²,

Souza³

2021

Tecnologias, Métodos E Teorias Na Engenharia De Computação 2

View full text Add to dashboard Cite

Direitos para esta edição cedidos à Atena Editora pelos autores. Todo o conteúdo deste livro está licenciado sob uma Licença de Atribuição Creative Commons. Atribuição-Não-Comercial-NãoDerivativos 4.0 Internacional (CC BY-NC-ND 4.0).O conteúdo dos artigos e seus dados em sua forma, correção e confiabilidade são de responsabilidade exclusiva dos autores, inclusive não representam necessariamente a posição oficial da Atena Editora. Permitido o download da obra e o compartilhamento desde que sejam atribuídos créditos aos autores, mas sem a possibilidade de alterá-la de nenhuma forma ou utilizá-la para fins comerciais. Todos os manuscritos foram previamente submetidos à avaliação cega pelos pares, membros do Conselho Editorial desta Editora, tendo sido aprovados para a publicação com base em critérios de neutralidade e imparcialidade acadêmica.A Atena Editora é comprometida em garantir a integridade editorial em todas as etapas do processo de publicação, evitando plágio, dados ou resultados fraudulentos e impedindo que interesses financeiros comprometam os padrões éticos da publicação. Situações suspeitas de má conduta científica serão investigadas sob o mais alto padrão de rigor acadêmico e ético.

show abstract

Development of Water Level Prediction Models Using Machine Learning in Wetlands: A Case Study of Upo Wetland in South Korea

Cited by 84 publications

References 39 publications

Prediction of River Stage Using Multistep-Ahead Machine Learning Techniques for a Tidal River of Taiwan

Prediction of River Stage Using Multistep-Ahead Machine Learning Techniques for a Tidal River of Taiwan

Deep Learning with Long Short Term Memory Based Sequence-to-Sequence Model for Rainfall-Runoff Simulation

Aprendizado Conjunto Aplicado Na Predição Do Mercado De Ações Brasileiro

Contact Info

Product

Resources

About