Developing a deep learning model for the simulation of micro-pollutants in a watershed

Abstract

In recent years, as agricultural activities and types of crops have become diverse, the occurrence of micro-pollutants has been reported more frequently in rural areas. These pollutants have detrimental effects on human health and ecological systems; thus, it is important to manage and monitor their presence in the environment. The modeling approach could be an effective way to understand and manage these pollutants. This study predicts the concentrations of micro-pollutants (MPs) using deep learning (DL) models, and the results are then compared with simulation results obtained from the soil water assessment tool (SWAT) model. The SWAT model showed an unacceptable performance owing to the resulting negative NasheSutcliffe efficiency (NSE) values for the simulations. This may be caused by the limitations of SWAT, which pertains to adopting simplified equations to simulate micro-pollutants. In addition, the ambiguous plan of pesticide application increased the model uncertainty, thereby deteriorating the model result. Here, we developed two different DL models: long short-term memory (LSTM) and convolutional neural network (CNN). LSTM exhibited the highest model performance, with NSE values of 0.99 and 0.75 for the training and validation steps, respectively. In the multi-target MP model, the error decreased as the number of simulated pollutants increased. The simulation of the four pollutants had the highest error, while the six-target simulation had the lowest error. In conclusion, this study demonstrated that the LSTM model has the potential to improve the prediction of MPs in aquatic systems.

Uncertainty in Environmental Micropollutant Modeling

“…LSTM models have also been recently used for predicting MP concentrations. Yun et al (2021) used LSTM and CNN to predict six different MPs, such as acetamiprid, in a watershed. More recently, Tran et al (2023) used LSTM and other ML models to predict microplastics in peatlands.…”

Long short-term memory models of water quality in inland water environments

“…The use of SLR to interpret the internal variables of LSTM can be extended to water quality modeling. LSTM has been successfully used to model micropollutants and harmful algal blooms in surface waters ( Yun et al., 2021 ; Zheng et al., 2021a ). However, these models only provide target prediction at a specific site and do not indicate whether the LSTM has learned a representative related-phenomenon.…”

Inland harmful algal blooms (HABs) modeling using internet of things (IoT) system and deep learning

“…During optimization, the learning rate was converged from the large value to the small value, implying that our model preferred the small step size when adjusting the weight and bias. Jang et al [89] and Yun et al [90] also recommended the smaller learning rate to simulate the water quality. In addition, the lookback also was the influential factor to the model result.…”