Prediction of Water Level and Water Quality Using a CNN-LSTM Combined Deep Learning Approach

Sang-Soo Baek,Jong Ahn Chun,Jongcheol Pyo

doi:10.3390/w12123399

Sang-Soo Baek, Jong Ahn Chun + Show 1 more

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https://doi.org/10.3390/w12123399

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Abstract

A Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) combined with a deep learning approach was created by combining CNN and LSTM networks simulated water quality including total nitrogen, total phosphorous, and total organic carbon. Water level and water quality data in the Nakdong river basin were collected from the Water Resources Management Information System (WAMIS) and the Real-Time Water Quality Information, respectively. The rainfall radar image and operation information of estuary barrage were also collected from the Korea Meteorological Administration. In this study, CNN was used to simulate the water level and LSTM used for water quality. The entire simulation period was 1 January 2016–16 November 2017 and divided into two parts: (1) calibration (1 January 2016–1 March 2017); and (2) validation (2 March 2017–16 November 2017). This study revealed that the performances of both of the CNN and LSTM models were in the “very good” range with above the Nash–Sutcliffe efficiency value of 0.75 and that those models well represented the temporal variations of the pollutants in Nakdong river basin (NRB). It is concluded that the proposed approach in this study can be useful to accurately simulate the water level and water quality.

Highlights

One of the main sources of the freshwater supply for the uses of domestic and industrial water and agricultural water are rivers
This study revealed that the performances of both of the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models were in the “very good” range with above the Nash–Sutcliffe efficiency value of 0.75 and that those models well represented the temporal variations of the pollutants in Nakdong river basin (NRB)
The median values of water level and TOC were close to the mean values of water level and TOC, while the median values of total nitrogen (TN) and total phosphorus (TP) were appreciably different from the mean values of those

Summary

Introduction

One of the main sources of the freshwater supply for the uses of domestic and industrial water and agricultural water are rivers. Baek et al [3] improved the low-impact development module in the SWMM model to accurately simulate total suspended solids (TSS), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) in an urban watershed in the Republic of Korea (hereafter South Korea). Even though these conventional process-based models are capable of accurately simulating water quality, large input data and parameters that require high computational costs are often required. These limitations may become substantially larger for a river basin with complex hydraulic structures and various water uses, because input data and parameters for these all processes in a complex basin are practically not possible to obtain

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Conclusion