A survey on river water quality modelling using artificial intelligence models: 2000–2020

Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.

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

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.

https://www.mdpi.com/2073-4441/12/2/585/pdf

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

As an important factor affecting the mangrove wetland ecosystem, water quality has become the focus of attention in recent years. Therefore, many studies have focused on the prediction of water quality to help establish a regulatory framework for the assessment and management of water pollution and ecosystem health. To make a more accurate and comprehensive forecast analysis of water quality, we propose a method for water quality prediction based on the multi-time scale bidirectional LSTM network. In the method, we improve data integrity and data volume through data preprocessing. And the network processes input data forward and backward and considers the dependencies at multiple time scales. Besides, we use the Box-Behnken experimental design method to adjust hyper-parameters in the process of modeling. In this study, we apply this method to the water quality prediction research of Beilun Estuary, and the performance of our proposed model is evaluated and compared with other models. The experiment results show that this model has better performance in water quality prediction than that of using LSTM or bidirectional LSTM alone. Graphical Abstract Schematic of research work.

A water quality prediction method based on the multi-time scale bidirectional long short-term memory network.

The application of renewable energy conversion devices is considered as one of the effective ways to alleviate the energy shortage and environmental pollution. Designing electrocatalysts with excellent performance and affordable price is promising to accelerate the reaction process and large‐scale application. At present, ruthenium (Ru)‐based nanomaterials have shown similar catalytic activity but superior price demand compared to commercial Pt/C. This undoubtedly makes Ru‐based nanomaterials a perfect candidate to replace advanced Pt catalysts. Significant progress is made in the rational design of Ru‐based electrocatalysts, but an in‐depth understanding of the engineering strategies and induced effects is still at an early stage. This review summarizes the modification strategies for enhancing the catalytic activity of Ru, including surface structure, metal element, nonmetal element, size, bimetallic oxides, and heterostructure engineering strategies. Then the induced electronic modulation effects generated by the intramolecular and intermolecular of the Ru‐based nanomaterials are elucidated. Further, the application progress of engineered Ru‐based nanomaterials for hydrogen and oxygen conversion reactions is highlighted, and the correlations of engineering strategies, catalytic activity, and reaction pathways are elaborated. Finally, challenges and prospects are presented for the future development and practical application of Ru‐based nanomaterials.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/aenm.202202119

Recent Advances in Engineered Ru‐Based Electrocatalysts for the Hydrogen/Oxygen Conversion Reactions

Platinum (Pt), as a commonly used electrocatalyst in direct methanol fuel cells (DMFCs), suffers from sluggish kinetics of both the methanol oxidation reaction (MOR) and oxygen reduction reaction (ORR). Geometric engineering has been proven effective for improving the MOR and ORR activities. Thus, by modulating the Pt precursor and poly(vinylpyrrolidone) (PVP) dosages, different porous PtCu nanotubes constructed by hollow nanospheres, solid alloy, and Pt-rich skinned nanoparticles, respectively, are successfully synthesized. Among them, the solid PtCu alloy nanoparticle coherent nanotubes exhibit the specific activity 9.42 times higher than Pt/C toward MOR, while the hollow PtCu alloy nanosphere coherent nanotubes show the specific activity 4.85 times higher than Pt/C toward ORR. The different Pt:Cu ratios of hollow nanospheres, solid alloy, and Pt-rich skinned nanoparticles cause the differences in electron transfer from Cu to Pt as well as electronic structures of Pt. As a result, the binding energies of intermediates can be regulated, leading to the enhancement in MOR and ORR.

Geometric Engineering of Porous PtCu Nanotubes with Ultrahigh Methanol Oxidation and Oxygen Reduction Capability.

Due to increasingly serious deterioration of surface water quality, effective water quality prediction technique for real-time early warning is essential to guarantee the emergency response ability in advance for sustainable water management. In this study, an effective data-driven model for surface water quality prediction is developed to analyze the inherent water quality variation tendencies and provide real-time early warnings according to the historical observation data. The developed data-driven model is integrated by an improved genetic algorithm (IGA) for selecting optimal initial weight parameters of neural a network and a back-propagation neural network (BPNN) for adjusting appropriate connection architectures of neural network. First, improved genetic algorithm is used to optimize the reasonable initial weight parameters and prevent the developed model from selecting a local optimal result. Second, BPNN is applied to adjust appropriate connection architectures and identify the features of water quality variation. The developed model is then applied to forecast the surface water quality variations for real-time early warning in Ashi River, China, comparing with simple BPNN model. The prediction results demonstrate that the developed data-driven model can significantly improve the prediction performance both in prediction accuracy and reliability, and effectively provide real-time early warning for emergency response.

Shaobin Cai

Papers

Geometric Engineering of Porous PtCu Nanotubes with Ultrahigh Methanol Oxidation and Oxygen Reduction Capability.

A data-driven model for real-time water quality prediction and early warning by an integration method

Pore-scale modelling on hydrogen transport in porous media: Implications for hydrogen storage in saline aquifers