A hybrid XGBoost-ISSA-LSTM model for accurate short-term and long-term dissolved oxygen prediction in ponds

An ensemble model for accurate prediction of key water quality parameters in river based on deep learning methods

Deep learning models provide a more powerful method for accurate and stable prediction of water quality in rivers, which is crucial for the intelligent management and control of the water environment. To increase the accuracy of predicting the water quality parameters and learn more about the impact of complex spatial information based on deep learning models, this study proposes two ensemble models TNX (with temporal attention) and STNX (with spatio-temporal attention) based on seasonal and trend decomposition (STL) method to predict water quality using geo-sensory time series data. Dissolved oxygen, total phosphorus, and ammonia nitrogen were predicted in short-step (1 h, and 2 h) and long-step (12 h, and 24 h) with seven water quality monitoring sites in a river. The ensemble model TNX improved the performance by 2.1%-6.1% and 4.3%-22.0% relative to the best baseline deep learning model for the short-step and long-step water quality prediction, and it can capture the variation pattern of water quality parameters by only predicting the trend component of raw data after STL decomposition. The STNX model, with spatio-temporal attention, obtained 0.5%-2.4% and 2.3%-5.7% higher performance compared to the TNX model for the short-step and long-step water quality prediction, and such improvement was more effective in mitigating the prediction shift patterns of long-step prediction. Moreover, the model interpretation results consistently demonstrated positive relationship patterns across all monitoring sites. However, the significance of seven specific monitoring sites diminished as the distance between the predicted and input monitoring sites increased. This study provides an ensemble modeling approach based on STL decomposition for improving short-step and long-step prediction of river water quality parameter, and understands the impact of complex spatial information on deep learning model.

Ultra-early prediction of the process parameters of coal chemical production

Most accidents in a chemical process are caused by abnormal or deviations of the process parameters, and the existing research is focused on short-term prediction. When the early warning time is advanced, many false and missing alarms will occur in the system, which will cause certain problems for on-site personnel; how to ensure the accuracy of early warning as much as possible while the early warning time is a technical problem requiring an urgent solution. In the present work, a bidirectional long short-term memory network (BiLSTM) model was established according to the temporal variation characteristics of process parameters, and the Whale optimization algorithm (WOA) was used to optimize the model's hyperparameters automatically. The predicted value was further constructed as a Modified Inverted Normal Loss Function (MINLF), and the probability of abnormal fluctuations of process parameters was calculated using the residual time theory. Finally, the WOA-BiLSTM-MINLF process parameter prediction model with inherent risk and trend risk was established, and the fluctuation process of the process parameters was transformed into dynamic risk values. The results show that the prediction model alarms 16 min ahead of distributed control systems (DCS), which can reserve enough time for operators to take safety protection measures in advance and prevent accidents.

Predicting dissolved oxygen level using Young's double-slit experiment optimizer-based weighting model

Accurate prediction of the dissolved oxygen level (DOL) is important for enhancing environmental conditions and facilitating water resource management. However, the irregularity and volatility inherent in DOL pose significant challenges to achieving precise forecasts. A single model usually suffers from low prediction accuracy, narrow application range, and difficult data acquisition. This study proposes a new weighted model that avoids these problems, which could increase the prediction accuracy of the DOL. The weighting constructs of the proposed model (PWM) included eight neural networks and one statistical method and utilized Young's double-slit experimental optimizer as an intelligent weighting tool. To evaluate the effectiveness of PWM, simulations were conducted using real-world data acquired from the Tualatin River Basin in Oregon, United States. Empirical findings unequivocally demonstrated that PWM outperforms both the statistical model and the individual machine learning models, and has the lowest mean absolute percentage error among all the weighted models. Based on two real datasets, the PWM can averagely obtain the mean absolute percentage errors of 1.0216%, 1.4630%, and 1.7087% for one-, two-, and three-step predictions, respectively. This study shows that the PWM can effectively integrate the distinctive merits of deep learning methods, neural networks, and statistical models, thereby increasing forecasting accuracy and providing indispensable technical support for the sustainable development of regional water environments.

Assessing industrial wastewater effluent toxicity using boosting algorithms in machine learning: A case study on ecotoxicity prediction and control strategy development

Trace heavy metals have a tendency to persist in the effluent of industrial wastewater treatment facilities, leading to toxic effects on downstream water bodies. Traditional assessment methods relied on animal testing, but ethical concerns have rendered them unacceptable. An alternative solution is to evaluate wastewater toxicity using trophic-level aquatic organisms as bioassays. However, these bioassay methods involve costly and time-consuming chemical and biological analytical experiments. In this study, an artificial intelligence-powered water quality assessment (AiWA) approach is proposed for predicting industrial effluent ecotoxicity to further enhance the quick and cost-effective ecotoxicity assessment process. Initially, 99 samples were collected from industrial wastewater treatment plants representing 21 different industries in the Republic of Korea. Fourteen parameters were measured, encompassing both physicochemical and ecotoxicological aspects. Boosting algorithms, especially extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost), were employed for model development. XGBoost outperformed AdaBoost in terms of model performance. Feature selection analysis revealed that conductivity, copper, lead, selenium, pH, and zinc concentrations were the most suitable inputs for training the boosting model. The innovated XGBoost-based AiWA model demonstrated significantly higher performance (i.e., up to 80%) compared to conventional models with an R2 value of exceeding 0.94 and root mean square error of 3.5 toxicity unit for predicting the integrated toxicity unit (ITU). Additionally, pH and conductivity emerged as crucial indicators for reflecting ecotoxicity levels. Specially, this case study indicated that non-toxic/directly dischargeable levels (TU ≤ 1) were achieved when the pH ranged from 6.8 to 8.4 and the conductivity remained below 1651 μS/cm. These findings are expected to facilitate rapid and cost-effective detection of heavy metal ecotoxicity in industrial wastewater effluents, aiding decision-making in wastewater management.

Data-driven prediction of greenhouse aquaponics air temperature based on adaptive time pattern network

Greenhouse aquaponics system (GHAP) improves productivity by harmonizing internal environments. Keeping a suitable air temperature of GHAP is essential for the growth of plant and fish. However, the disturbance of various environmental factors and the complexity of temporal patterns affect the accuracy of the microclimate time-series forecasting. This work proposed an Adaptive Time Pattern Network (ATPNet) to predict GHAP air temperature, which consists of deep temporal feature (DTF) module, multiple temporal pattern convolution (MTPC) module, and spatial attention mechanism (SAM) module. The DTF module has a wide sensory range and can capture information over a long-time span. The MTPC module is designed to improve model response performance by exploiting the effective temporal information of different environmental factors at different times. At the same time, the SAM can explore the correlations among different environmental factors. The ATPNet found that air temperature of GHAP has a strong correlation with other temperature-related parameters (external air temperature, external soil temperature, and water temperature). Compared with the best performance of three baseline models (multilayer perceptron (MLP), recurrent neural network (RNN), and Temporal Convolutional Network (TCN)), the ATPNet enhanced overall prediction performance for the following 24 h by 7.44% for root mean squared error (RMSE), 2.53% for mean absolute error (MAE), and 3.15% for mean absolute percentage error (MAPE), respectively.

Short-term wind speed prediction based on FEEMD-PE-SSA-BP

As one of the renewable energy power generation methods, wind power generation shows a high growth trend. However, while wind power is connected to the grid, the volatility and instability of wind power make the power system produce a lot of uncertain fluctuations, which greatly affects the power quality and jeopardizes the stable operation of the power system. Therefore, high wind speed forecasting accuracy can provide a solid basis for grid management, improve the power system's ability to consume wind power, and ensure the safety and stabilization of the power system. In order to solve the problem of inaccurate prediction caused by the non-linearity and unsteadiness of wind speed series, this paper proposes a Fractal Ensemble Empirical Mode Decomposition (FEEMD)-Permutation Entropy (PE)-Sparrow Search Algorithm (SSA)-Error Back Propagation (BP) neural network method for short-term wind speed prediction. This method first uses FEEMD to decompose the original wind speed in order from high to low frequency; then calculates the entropy value of each component, and merges the components with similar entropy values to effectively reduce the computation; and finally, the new sub-series are predicted by SSA-BP model, and the predicted value of the merged new sub-sequences are accumulated to obtain the final wind speed prediction results. The simulation study shows that the proposed prediction model is not only fast and accurate, but also suitable for short-term wind speed prediction.

Industrial Internet of things-based solar photo voltaic cell waste management in next generation industries

Nowadays, modern industries generate their energy by using renewable solar. The rapid increase in photovoltaic (PV) module installations provides a better energy conversion, but their life cycle is a major concern. This research paper focuses on the recycling process for solar PV modules using the Internet of Things in industries. The smart bin with the Internet of Things (IoT) utilizes a machine learning approach to collect solar waste. The proposed smart bin uses k-Nearest Neighbor's algorithm (k-NN) and Long Short-Term Memory (LSTM), a network-based learning algorithm. These algorithms are useful in updating the level of the bin via alert messages. It also helps in identifying the type of waste material. The k-NN algorithm provides 83% accuracy in predicting the bin level in a real-time testing environment. The smart dust bin classifies the waste materials, and notifies its level to the collection center through the IoT platform when the level reaches a prescribed threshold, the signal corresponding to the level is passed to the common waste collection unit. IoT is connected to Cloud Server. It helps to predict the level of the smart bin. Delay is introduced in the order of 3-8 s while the alert message is sent to the common waste collection unit. The system monitors the smart bin levels and sends the notifications to alert and initiate the collection unit. Real-time mobile app monitors the bin's level and location. The cloud IoT analytics analyze the solar e-waste in a different locations in industries.The proposed system works better and provides accurate results by using machine learning approach.

Modeling Multistep Ahead Dissolved Oxygen Concentration Using Improved Support Vector Machines by a Hybrid Metaheuristic Algorithm

Dissolved oxygen (DO) concentration is an important water-quality parameter, and its estimation is very important for aquatic ecosystems, drinking water resources, and agro-industrial activities. In the presented study, a new support vector machine (SVM) method, which is improved by hybrid firefly algorithm–particle swarm optimization (FFAPSO), is proposed for the accurate estimation of the DO. Daily pH, temperature (T), electrical conductivity (EC), river discharge (Q) and DO data from Fountain Creek near Fountain, the United States, were used for the model development. Various combinations of pH, T, EC, and Q were used as inputs to the models to estimate the DO. The outcomes of the proposed SVM–FFAPSO model were compared with the SVM–PSO, SVM–FFA, and standalone SVM with respect to the root mean square errors (RMSE), the mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and determination coefficient (R2), and graphical methods, such as scatterplots, and Taylor and violin charts. The SVM–FFAPSO showed a superior performance to the other methods in the estimation of the DO. The best model of each method was also assessed in multistep-ahead (from 1- to 7-day ahead) DO, and the superiority of the proposed method was observed from the comparison. The general outcomes recommend the use of SVM–FFAPSO in DO modeling, and this method can be useful for decision-makers in urban water planning and management.