Water Level Prediction at Drainnage Pump Stations in Low Lands Using LSTM Model

Kimura, Nobuaki; Nakata, Toru; Kiri, Hirohide; Sekijima, Kenji; Azechi, Issaku; Yoshinaga, Ikuo; Baba, Daichi

doi:10.2208/jscejhe.75.2_i_139

Cited by 4 publications

(2 citation statements)

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“…For example, Kimura et al (2018) reported that water-level and discharge predictions by an MLP model in a small lowland with a single drainage pumping station were in good agreement with referenced values produced by pseudo-rainfalls using a physical model calibrated to the small lowland for supplementing insufficient observed datasets. Another example is a comparison study between MLP and LSTM models in a complicated drainage management system in a mid-size lowland (Kimura et al 2019). This study showed that the LSTM model predicted long-term water levels more accurately than the MLP model by several percent during the highest peak period.…”

Section: Introductionmentioning

confidence: 95%

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Recurrent Neural Network Predictions for Water Levels at Drainage Pumping Stations in an Agricultural Lowland

Kimura

Yoshinaga

Sekijima

et al. 2021

JARQ

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Drainage management in a complicated system in an agricultural lowland must operate pumps flexibly and quickly, based on the water level at the pumping station. A data-driven model without any physical-based information was implemented in a complicated drainage management system to predict the water level of a lagoon near a main drainage pumping station. We employed a long shortterm memory (LSTM) model as an advanced neural network model to utilize the field datasets obtained from water-related facilities and sensors over about eight years as model input data. We performed sensitivity tests for model accuracy with different types of data and locations of data using cross-validation with an error quantity between observed and predicted water levels at the main drainage pumping station. The results showed that the LSTM model with the input of all available datasets predicted better than the models using several parts of datasets or it was roughly equivalent to those for water levels over the entire observed period in 3-h and 6-h lead times. In addition, the LSTM with only inputs of the water level and rainfall observed by drainage pumping stations performed better for the observed subperiod, including the severest flood event.

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Section: Introductionmentioning

confidence: 95%

“…Therefore, the LSTM model was also employed in this study to predict water levels. This LSTM structure was extended with multiple hidden layers from a conventional hidden-layer structure of the LSTM described by Kimura et. al.…”

Section: Introductionmentioning

confidence: 99%