Predicting the Trend of Dissolved Oxygen Based on the kPCA-RNN Model

Abstract

Water quality forecasting is increasingly significant for agricultural management and environmental protection. Enormous amounts of water quality data are collected by advanced sensors, which leads to an interest in using data-driven models for predicting trends in water quality. However, the unpredictable background noises introduced during water quality monitoring seriously degrade the performance of those models. Meanwhile, artificial neural networks (ANN) with feed-forward architecture lack the capability of maintaining and utilizing the accumulated temporal information, which leads to biased predictions in processing time series data. Hence, we propose a water quality predictive model based on a combination of Kernal Principal Component Analysis (kPCA) and Recurrent Neural Network (RNN) to forecast the trend of dissolved oxygen. Water quality variables are reconstructed based on the kPCA method, which aims to reduce the noise from the raw sensory data and preserve actionable information. With the RNN’s recurrent connections, our model can make use of the previous information in predicting the trend in the future. Data collected from Burnett River, Australia was applied to evaluate our kPCA-RNN model. The kPCA-RNN model achieved R 2 scores up to 0.908, 0.823, and 0.671 for predicting the concentration of dissolved oxygen in the upcoming 1, 2 and 3 hours, respectively. Compared to current data-driven methods like Feed-forward neural network (FFNN), support vector regression (SVR) and general regression neural network (GRNN), the predictive accuracy of the kPCA-RNN model was at least 8%, 17% and 12% better than the comparative models in these three cases. The study demonstrates the effectiveness of the kPAC-RNN modeling technique in predicting water quality variables with noisy sensory data.

A Review of the Artificial Neural Network Models for Water Quality Prediction

“…In contrast, feed-forward neural network can predict water quality with relatively little data. In addition to being able to perform prediction tasks, GRNN is also suitable for small data sets (28,32,61,151,159, 265 samples) compared with other types of ANNs [24,[39][40][41][42][43], so researchers should pay some attention to it. The artificial neural network has been widely used in water quality prediction.…”

“…MLPs with only three layers are the most widely used architectures [59] in many types of feedforward ANNs (see Figure 2), followed by BPNNs [37] which use the back-propagation algorithms to train networks. Other commonly used feed-forward network architectures in water quality prediction include TDNNs [36], RBFNNs [60], GRNNs [61], WNNs [62], ELMs [5], CCNN [63] and MNN [50]. TDNNs is a subclass of MLPs that learns temporal behavior from continuous past and present signals [36].…”

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

“…These temporal variations have been well simulated in previous studies. For example, Zhang et al [43] predicted the temporal variations of DO in the Burnett river Figure 5 shows the comparison between the observed pollutant values and the simulated results of the LSTM model. The R 2 of TP and TN for the training period were 0.92 and 0.95, respectively, while those in the validation period were 0.87 and 0.97, respectively (Table 2).…”

Machine learning approaches to improve and predict water quality data

“…Computations derived from the earlier input are fed back into the network, which is critical in learning the non-linear relationships between multiple water quality parameters. We proposed a predictive water quality model based on a combination of a kernel principal component analysis (kPCA) and recurrent neural network (RNN) (Zhang et al (2019b)). Water quality parameters are reconstructed based on kPCA method, which aims to reduce the noise from the raw sensory data and preserve redundant information.…”