Utilizing artificial neural network to simulate and predict the hydraulic performance of free water surface constructed wetlands

The nonlinear regression trees for retrieving missed data during sea-level measurement

Sea surface displacement (SSD) is a crucial parameter in environmental engineering. The measurements of SSD are susceptible to the failure of instruments and equipment, data losses, and other unpredictable events. In this study, we developed an innovative nonlinear regression trees (NRT) technique to retrieve the missing data of SSD. The model was used on the record of SDD for the ADCPs deployed in the Gdansk Gulf along the Vistula Lagoon. The NRT suggests using a nonlinear machine learning algorithm instead of linear regression at the end nodes of a tree. Two different NRT models were developed. One of them is based on the support vector regression (SVR) and the other on adaptive neuro-fuzzy inference system (ANFIS). The performance of both models was validated by comparing their results with the state-of-the-art algorithms. The models were trained using four input parameters, including the pressure and SDD of two other ADCPs, which recorded complete time series data. The analysis shows that NRT methods, with an average RMSE of 0.019 m, provide about 71.13% more accurate prediction than random forest (RF). Among all models, the NRT-SVR, with the lowest RMSE of 0.009 m and MAE of 0.002 m, and the highest R2 of 0.997, NSE of 0.997, and IA of 0.999, is ranked as the most accurate model. The simplicity, as well as the high efficiency of the developed NRT models, enable us to apply them for pattern recognition of other environmental and engineering problems.

Comparative analysis of ecological sensitivity assessment using the coefficient of variation method and machine learning

Ecological sensitivity is an essential indicator for measuring the ecological environment's level, and its assessment results have significant reference value for regional ecological environment protection and resource development and utilization. Taking Xifeng County as the study area, we selected a total of 12 assessment factors in terms of ecological environment, geological environment, and human environment, including average annual rainfall, average annual temperature, average annual wind speed, river density, vegetation coverage, soil erodibility, elevation, slope, geological disaster susceptibility, road density, land use, and night light index, and explored the spatial distribution patterns of ecological sensitivities and the characteristics of the differences in the study area based on the coefficient of variation method and machine learning. The results show that the overall spatial distribution pattern of ecological sensitivity in Xifeng County shows a low sensitivity in the north and a high sensitivity in the south. 41.90% of the regional ecological sensitivity intensity is classified as very high and high sensitivity, mainly distributed in mountainous and hilly areas, while 35.51% is classified as very low and low sensitivity, mainly distributed in plains and low mountainous areas. The assessment results are consistent with the actual situation, enriching the ecological sensitivity assessment methods and models.

Studying the evolutions, differences, and water security impacts of water demands under shared socioeconomic pathways: A SEMs-bootstrap-ANN approach applied to Sichuan Province

In this study, a SEMs-bootstrap-ANN method was presented for constructing prediction intervals (PIs) of water demand under shared socioeconomic pathways (SSPs). The primary objective was to examine the evolution, disparities, and impacts on water security. Initially, a bootstrap algorithm and an artificial neural network (ANN) were combined to form a bootstrap-ANN model, which determined the centres and widths of the PIs at a specified significance level by estimating the distributions of prediction values and errors. The water demand factors in SSPs were projected using socioeconomic models like Cobb-Douglas, based on the narratives of the International Institute for Applied Systems Analysis (IIASA). By incorporating these factors into the bootstrap-ANN model, the study obtained the temporal changes of water demand PIs in SSPs, while quantifying the differences and water security implications using the interval difference index (IDI) and surface water exploration index (SWEI). The case study focused on Sichuan province, and the model performance was evaluated via the evaluation indices and cross-validation. The results demonstrated five key findings. Firstly, the proposed method showed a greater PICP of 0.985, slightly larger PIRAW of 9.83%, and higher MAIS than other methods in the historical dataset, indicating a small disadvantage in width in return for better accuracy and overall performance. Secondly, the reliability of the results in the SSP period was supported by the PIRAWs (mostly within 15%), the cross errors (approximately 5%), and their performance in 2021 (the PIs in SSP2 almost covered all true values). Thirdly, the total water demands in all SSPs within Sichuan Province exhibited a consistent upward trajectory, with SSP5 displaying the highest increase of 44-63% compared to current water usage. Fourthly, among the four SSPs, the most substantial disparities were observed between SSP5 and SSP3, reaching a maximum difference of 32%. Conversely, the disparities between SSP2 and SSP1 fluctuated around zero, transitioning from negative to positive trends. Notably, from an environmental perspective, SSP1 was considered preferable to SSP2. Lastly, the SWEIs, which reflected water security conditions in Sichuan Province under the four SSPs, ranked in the following order: SSP3, SSP1, SSP2, and SSP5, indicating a progressively worsening situation. Despite not reaching stress thresholds even during dry years until 2100, the water security conditions could deteriorate by 28-46% compared to historical extremes and by 3-16% compared to extended extremes in dry years.

Quantitative prediction of the hydraulic performance of free water surface constructed wetlands by integrating numerical simulation and machine learning

Quantitative prediction of the design parameter-influenced hydraulic performance is significant for optimizing free water surface constructed wetlands (FWS CWs) to reduce point and non-point source pollution and improve land utilization. However, owing to limitations of the test conditions and data scale, a quantitative prediction model of the hydraulic performance under multiple design parameters has not yet been established. In this study, we integrated field test data, mechanism model, statistical regression, and machine learning (ML) to construct such quantitative prediction models. A FWS CW numerical model was established by integrating 13 groups of trace data from field tests. Subsequently, training, test and extension datasets comprising 125 (5^3), 25 (L₂₅(5⁶)) and 16 (L₁₆(4⁴)) data points, respectively, were generated via numerical simulation of multi-level value combination of three quantitative design parameters, namely, water depth, hydraulic loading rate (HLR), and aspect ratio. The short circuit index (φ₁₀), Morrill dispersion index (MDI), hydraulic efficiency (λ) and moment index (MI) were used as representative hydraulic performance indicators. Training set with large samples were analyzed to determine the variation rules of different hydraulic indicators. Based on the control variable method, φ₁₀, λ, and MI grew exponentially with increasing aspect ratio whereas MDI showed a decreasing trend; with increasing water depth, φ₁₀, λ, and MI showed polynomial decreases whereas MDI increased; with increasing HLR, φ₁₀, λ, and MI slowly increased linearly whereas MDI showed the opposite trend. Finally, we constructed models based on multivariate nonlinear regression (MNLR) and ML (random forest (RF), multilayer perceptron (MLP), and support vector regression. The coefficients of determination (R²) of the MNLR and ML models fitting the training and test sets were all greater than 0.9; however, the generalization abilities of different models in the extension set were different. The most robust MLP, MNLR without interaction term, and RF models were recommended as the preferred models to hydraulic performance prediction. The extreme importance of aspect ratio in hydraulic performance was revealed. Thus, gaps in the current understanding of multivariate quantitative prediction of the hydraulic performance of FWS CWs are addressed while providing an avenue for researching FWS CWs in different regions according to local conditions.

Quantification of interfacial interaction related with adhesive membrane fouling by genetic algorithm back propagation (GABP) neural network

Since adhesive membrane fouling is critically determined by the interfacial interaction between a foulant and a rough membrane surface, efficient quantification of the interfacial interaction is critically important for adhesive membrane fouling mitigation. As a current available method, the advanced extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) theory involves complicated rigorous thermodynamic equations and massive amounts of computation, restricting its application. To solve this problem, artificial intelligence (AI) visualization technology was used to analyze the existing literature, and the genetic algorithm back propagation (GABP) artificial neural network (ANN) was employed to simplify thermodynamic calculation. The results showed that GABP ANN with 5 neurons could obtain reliable prediction performance in seconds, versus several hours or even days time-consuming by the advanced XDLVO theory. Moreover, the regression coefficient (R) of GABP reached 0.9999, and the error between the prediction results and the simulation results was less than 0.01%, indicating feasibility of the GABP ANN technique for quantification of interfacial interaction related with adhesive membrane fouling. This work provided a novel strategy to efficiently optimize the thermodynamic prediction of adhesive membrane fouling, beneficial for better understanding and control of adhesive membrane fouling.