A study on water quality prediction by a hybrid CNN-LSTM model with attention mechanism

Atwill, Yurong Y.; Xiong, Qingyu; Wu, Chao; Zou, Qinghong; Yu, Yang; Yi, Hualing; Gao, Min

A study on water quality prediction by a hybrid CNN-LSTM model with attention mechanism

2021

The water environment plays an essential role in the mangrove wetland ecosystem. Predicting water quality will help us better protect water resources from pollution, allowing the mangrove ecosystem to perform its normal ecological role. New approaches to solve such nonlinear problems need further research since the complexity of water quality data and they are easily affected by the noise. In this paper, we propose a water quality prediction model named CNN-LSTM with Attention (CLA) to predict the water quality variables. We conduct a case study on the water quality dataset of Beilun Estuary to predict pH and NH₃-N. Linear interpolation and wavelet techniques are used for missing data filling and data denoising, respectively. The hybrid model CNN-LSTM is highly capable of resolving nonlinear time series prediction problems, and the attention mechanism captures longer time dependence. The experimental results show that our model outperforms other ones, and can predict with different time lags in a stable manner.

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