Water Quality Predictions Based on Grey Relation Analysis Enhanced LSTM Algorithms

Abstract

With the growth of industrialization in recent years, the quality of drinking water has been a great concern due to increasing water pollution from industries and industrial farming. Many monitoring stations are constructed near drinking water sources for the purpose of fast reactions to water pollution. Due to the relatively low sampling frequencies in practice, mathematic prediction models are clearly needed for such monitoring stations to reduce the delay between the time points of pollution occurrences and water quality assessments. In this work, 2190 sets of monitoring data from automatic water quality monitoring stations in the Qiandao Lake, China from 2019 to 2020 were collected, and served as training samples for prediction models. A grey relation analysis-enhanced long short-term memory (GRA-LSTM) algorithm was used to predict the key parameters of drinking water quality. In comparison with conventional LSTM models, the mean absolute errors (MAEs) to predict the four parameters of water quality, i.e., dissolved oxygen (DO), permanganate index (COD), total phosphorus (TP), and potential of hydrogen (pH), were reduced by 23.03%, 10.71%, 7.54%, and 43.06%, respectively, using our GRA-LSTM algorithm, while the corresponding root mean square errors (RMSEs) were reduced by 24.47%, 5.28%, 6.92%, and 35.89%, respectively. Such an algorithm applies to predictions of events with small amounts of data, but with high parametric dimensions. The GRA-LSTM algorithm offers data support for subsequent water quality monitoring and early warnings of polluting water sources, making significant contributions to real-time water management in basins.

Water Quality Prediction Based on the KF-LSTM Encoder-Decoder Network: A Case Study with Missing Data Collection

“…XGBoost's optimization hyperparameters were learning rate (0.001, 0.01, 0.1), maximum tree depth (3,5,7,9), and number of trees used by the model (100, 200, 300, 400, 500). The hyperparameters for KF-GRU and KF-LSTM models included sequence structure in the Kalman filter, target quantity in the model (1), number of feature columns(4), hidden layer size (1,2,3,4), encoder-decoder layer numbers (1,2,3,4), dropout rate regularization size (0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8), encoder and decoder size (1,2,3,4), batch size (2,4,8,16,32,64,128), and learning rate (0.1, 0.01, 0.001). In the next section, we will discuss the performance of each model on the training and test sets.…”

“…From the formula, it can be seen that the learning rate and batch size are closely related and mutually influence the final effect of the model. We continuously varied the model parameters batch size (2,4,8,16,32,64,128) and learning rate (0.1, 0.01, 0.001) over multiple experiments to obtain the results shown in Figure 12. We normalised the MAE results of each experiment to a range between 0 and 1 and mapped them to corresponding colours.…”

“…Traditional water quality analysis methods rely heavily on a large number of manual operations, requiring the selection of monitoring points in the river to be tested and the collection of samples at regular intervals, which are then taken to the laboratory for analysis and identification. Although the traditional analysis methods can reflect the pollution situation of water quality to a certain extent, there are some shortcomings that make the traditional testing methods unable to achieve timely detection of water pollution and difficult to prevent enterprises from discharging industrial wastewater exceeding the standard into the river, for example, the testing process requires a lot of human and material resources and time, the sample collection and processing process is easily disturbed, thus causing errors, the testing is also difficult to prevent enterprises from discharging industrial wastewater exceeding the standard into the river [8].…”