Real-Time Water-Level Forecasting Using Dilated Causal Convolutional Neural Networks

Wang, Jhih-Huang; Lin, Gong−Ru; Chang, Ming-Jui; Huang, I-Hang; Chen, Yu-Ren

doi:10.1007/s11269-019-02342-4

Cited by 60 publications

(28 citation statements)

References 30 publications

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“…where t is the current time, ∆t is the lead time,Ĥ t+∆t is the forecasted river stage at time t + ∆t, L denotes the lag length of the input variables, R t−L is the antecedent rainfall at time t − L, H t−L is the antecedent river stage at time t-L, and S t−L is the antecedent tidal level at time t − L. Following the approach adopted by Wang et al [45], the lag length was set as 6 h in the present study; this lag length takes into consideration the concentration time of a watershed. To investigate the lead time, the lead time commonly applied in hydrology modeling of 1-6 h was used in this study [45][46][47].…”

Section: Methodology 21 Data-driven Model For River Stage Forecastingmentioning

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

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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.

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Section: Methodology 21 Data-driven Model For River Stage Forecastingmentioning

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

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“…Khac-Tien [16] combines the neural network with a fuzzy inference system for daily water levels forecasting. Other authors [31,34] apply the neural network model to predict flood with collected gauge measurements. Those models, implementing neural network models for one dimension, did not take into account the spatial correlations.…”

Section: Related Workmentioning

confidence: 99%

Hydrological Process Surrogate Modelling and Simulation with Neural Networks

Ruixi

Zen

Xing

et al. 2020

Advances in Knowledge Discovery and Data Mining

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Environmental sustainability is a major concern for urban and rural development. Actors and stakeholders need economic, effective and efficient simulations in order to predict and evaluate the impact of development on the environment and the constraints that the environment imposes on development. Numerical simulation models are usually computation expensive and require expert knowledge. We consider the problem of hydrological modelling and simulation. With a training set consisting of pairs of inputs and outputs from an off-the-shelves simulator, We show that a neural network can learn a surrogate model effectively and efficiently and thus can be used as a surrogate simulation model. Moreover, we argue that the neural network model, although trained on some example terrains, is generally capable of simulating terrains of different sizes and spatial characteristics.

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“…The ratio of training data to testing data is about 2 to 1 (a total of 1265 and 560). To further evaluate the prediction performance of the NN-based models, the root mean square error (RMSE) [38], mean absolute error (MAE) [39], determination coefficient (R 2 ) [38], and coefficient of efficiency (CE) [39] were applied to this research, which indicates the discrepancy between observed and forecasted air-conditioning electricity consumption. RMSE and MAE represent the errors between two sets of data; meanwhile, R 2 , and CE represent the consistency between the observed and predicted air-conditioning electricity, and the greater the consistency, the better the results.…”

Section: Model Constructing and Data Processingmentioning

confidence: 99%

Prediction of Air-Conditioning Energy Consumption in R&D Building Using Multiple Machine Learning Techniques

Liao

Chang

2020

Energies

Self Cite

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With the global increase in demand for energy, energy conservation of research and development buildings has become of primary importance for building owners. Knowledge based on the patterns in energy consumption of previous years could be used to predict the near-future energy usage of buildings, to optimize and facilitate more effective energy consumption. Hence, this research aimed to develop a generic model for predicting energy consumption. Air-conditioning was used to exemplify the generic model for electricity consumption, as it is the process that often consumes the most energy in a public building. The purpose of this paper is to present this model and the related findings. After causative factors were determined, the methods of linear regression and various machine learning techniques—including the earlier machine learning techniques of support vector machine, random forest, and multilayer perceptron, and the later machine learning techniques of deep neural network, recurrent neural network, long short-term memory, and gated recurrent unit—were applied for prediction. Among them, the prediction of random forest resulted in an R2 of 88% ahead of the first month and 81% ahead of the third month. These experimental results demonstrate that the prediction model is reliable and significantly accurate. Building owners could further enrich the model for energy conservation and management.

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Real-Time Water-Level Forecasting Using Dilated Causal Convolutional Neural Networks

Cited by 60 publications

References 30 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

Hydrological Process Surrogate Modelling and Simulation with Neural Networks

Prediction of Air-Conditioning Energy Consumption in R&D Building Using Multiple Machine Learning Techniques

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