Application of backpropagation artificial neural network prediction model for the PAH bioremediation of polluted soil

Where developmental toxicity meets explainable artificial intelligence: state-of-the-art and perspectives

INTRODUCTION

The application of Artificial Intelligence (AI) to predictive toxicology is rapidly increasing, particularly aiming to develop non-testing methods that effectively address ethical concerns and reduce economic costs. In this context, Developmental Toxicity (Dev Tox) stands as a key human health endpoint, especially significant for safeguarding maternal and child well-being.

AREAS COVERED

This review outlines the existing methods employed in Dev Tox predictions and underscores the benefits of utilizing New Approach Methodologies (NAMs), specifically focusing on eXplainable Artificial Intelligence (XAI), which proves highly efficient in constructing reliable and transparent models aligned with recommendations from international regulatory bodies.

EXPERT OPINION

The limited availability of high-quality data and the absence of dependable Dev Tox methodologies render XAI an appealing avenue for systematically developing interpretable and transparent models, which hold immense potential for both scientific evaluations and regulatory decision-making.

Integrating artificial intelligence, machine learning, and deep learning approaches into remediation of contaminated sites: A review

The growing number of contaminated sites across the world pose a considerable threat to the environment and human health. Remediating such sites is a cumbersome process with the complexity originating from the need for extensive sampling and testing during site characterization. Selection and design of remediation technology is further complicated by the uncertainties surrounding contaminant attributes, concentration, as well as soil and groundwater properties, which influence the remediation efficiency. Additionally, challenges emerge in identifying contamination sources and monitoring the affected area. Often, these problems are overly simplified, and the data gathered is underutilized rendering the remediation process inefficient. The potential of artificial intelligence (AI), machine-learning (ML), and deep-learning (DL) to address these issues is noteworthy, as their emergence revolutionized the process of data management/analysis. Researchers across the world are increasingly leveraging AI/ML/DL to address remediation challenges. Current study aims to perform a comprehensive literature review on the integration of AI/ML/DL tools into contaminated site remediation. A brief introduction to various emerging and existing AI/ML/DL technologies is presented, followed by a comprehensive literature review. In essence, ML/DL based predictive models can facilitate a thorough understanding of contamination patterns, reducing the need for extensive soil and groundwater sampling. Additionally, AI/ML/DL algorithms can play a pivotal role in identifying optimal remediation strategies by analyzing historical data, simulating scenarios through surrogate models, parameter-optimization using nature inspired algorithms, and enhancing decision-making with AI-based tools. Overall, with supportive measures like open-data policies and data integration, AI/ML/DL possess the potential to revolutionize the practice of contaminated site remediation.

A review on control and abatement of soil pollution by heavy metals: Emphasis on artificial intelligence in recovery of contaminated soil

"Save Soil Save Earth" is not just a catchphrase; it is a necessity to protect soil ecosystem from the unwanted and unregulated level of xenobiotic contamination. Numerous challenges such as type, lifespan, nature of pollutants and high cost of treatment has been associated with the treatment or remediation of contaminated soil, whether it be either on-site or off-site. Due to the food chain, the health of non-target soil species as well as human health were impacted by soil contaminants, both organic and inorganic. In this review, the use of microbial omics approaches and artificial intelligence or machine learning has been comprehensively explored with recent advancements in order to identify the sources, characterize, quantify, and mitigate soil pollutants from the environment for increased sustainability. This will generate novel insights into methods for soil remediation that will reduce the time and expense of soil treatment.

Using Machine Learning to make nanomaterials sustainable

Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.

Modelling of n-Hexadecane bioremediation from soil by slurry bioreactors using artificial neural network method

Diesel oil is known to be one of the major petroleum products that can pollute water and soil. Soil pollution caused by petroleum hydrocarbons has substantially impacted the environment, especially in the Middle East. In this study, modeling and optimization of hexadecane removal from soil was performed using two pure cultures of Acinetobacter and Acromobacter and consortium culture of both bacterial species using artificial neural network (ANN) method. Then the best ANN structure was proposed based on mean square error (MSE) as well as correlation coefficient (R) for pure cultures of Acinetobacter and Acromobacter as well as their consortium. The results showed that the correlations between the actual data and the data predicted by ANN (R2) in Acromobacter, Acinetobacter and consortium of both cultures were 0.50, 0.47 and 0.63, respectively. Despite the low correlation between the experimental data and the data predicted by the ANN, the correlation coefficient and the precision of ANN for the consortium was higher. As a result, ANN had desirable precision to predict hexadecan removal by the cobsertium culture of Ochromobater and Acintobacter.

Evaluation of Contemporary Computational Techniques to Optimize Adsorption Process for Simultaneous Removal of COD and TOC in Wastewater

This study was aimed at evaluating the artificial neural network (ANN), genetic algorithm (GA), adaptive neurofuzzy interference (ANFIS), and the response surface methodology (RSM) approaches for modeling and optimizing the simultaneous adsorptive removal of chemical oxygen demand (COD) and total organic carbon (TOC) in produced water (PW) using tea waste biochar (TWBC). Comparative analysis of RSM, ANN, and ANFIS models showed mean square error (MSE) as 5.29809, 1.49937, and 0.24164 for adsorption of COD and MSE of 0.11726, 0.10241, and 0.08747 for prediction of TOC adsorption, respectively. The study showed that ANFIS outperformed the ANN and RSM in terms of fast convergence, minimum MSE, and sum of square error for prediction of adsorption data. The adsorption parameters were optimized using ANFIS-surface plots, ANN-GA hybrid, RSM-GA hybrid, and RSM optimization tool in design expert (DE) software. Maximum COD (88.9%) and TOC (98.8%) removal were predicted at pH of 7, a dosage of 300 mg/L, and contact time of 60 mins using ANFIS-surface plots. The optimization approaches showed the performance in the following order: ANFIS-surface plots>ANN-GA>RSM-GA>RSM.

Prediction of the Maturity of Greenhouse Grapes Based on Imaging Technology

To predict grape maturity in solar greenhouses, a plant phenotype-monitoring platform (Phenofix, France) was used to obtain RGB images of grapes from expansion to maturity. Horizontal and longitudinal diameters, compactness, soluble solid content (SSC), titratable acid content, and the SSC/acid of grapes were measured and evaluated. The color values (R, G, B, H, S, and I) of the grape skin were determined and subjected to a back-propagation neural network algorithm (BPNN) to predict grape maturity. The results showed that the physical and chemical properties (PCP) of the three varieties of grapes changed significantly during the berry expansion stage and the color-changing maturity stage. According to the normalized rate of change of the PCP indicators, the ripening process of the three varieties of grapes could be divided into two stages: an immature stage (maturity coefficient Mc < 0.7) and a mature stage (after which color changes occurred) (0.7 ≤ Mc < 1). When predicting grape maturity based on the R, G, B, H, I, and S color values, the R, G, and I as well as G, H, and I performed well for Drunk Incense, Muscat Hamburg, and Xiang Yue grape maturity prediction. The GPI ranked in the top three (up to 0.87) when the above indicators were used in combination with BPNN to predict the grape Mc by single-factor and combined-factor analysis. The results showed that the prediction accuracy (RG and HI) of the two-factor combination was better for Drunk Incense, Muscat Hamburg, and Xiang Yue grapes (with recognition accuracies of 79.3%, 78.2%, and 79.4%, respectively), and all of the predictive values were higher than those of the single-factor predictions. Using a confusion matrix to compare the accuracy of the Mc's predictive ability under the two-factor combination method, the prediction accuracies were in the following order: Xiang Yue (88%) > Muscat Hamburg (81.3%) > Drunk Incense (76%). The results of this study provide an effective way to predict the ripeness of grapes in the greenhouse.

Chemo-metric engineering designs for deciphering the biodegradation of polycyclic aromatic hydrocarbons

Polycyclic aromatic hydrocarbons (PAHs) are non-polar organic compounds that are omnipresent in the environment and released due to anthropogenic activities through emissions and discharges. PAHs, being xenobiotic and exerts health impacts, thus they attract serious concern by the environmentalists. The stringent regulations and the need of sustainable development urges the hunt for a technically feasible and cost-effective wastewater treatment. Although the conventional physico-chemical treatment are widely preferred, they cause secondary pollution problems and demand subsequent treatment options. This comprehensive review intends to address the (a) different PAHs and their associated toxicity, (b) the remedial strategies, particularly biodegradation. The biological wastewater treatment techniques that involve microbial systems are highly influenced by the different physio-chemical and environmental parameters. Therefore, suitable optimization techniques are prerequisite for effective functioning of the biological treatment that sustains judiciously and interpreted in a lesser time. Here we have aimed to discuss (a) different chemo-metric tools involved in the design of experiments (DoE), (b) design equations and models, (c) tools for evaluating the model's adequacy and (d) plots for graphically interpreting the chemo-metric designs. However, to best of our knowledge, this is a first review to discuss the PAHs biodegradation that are tailored by chemo-metric designs. The associated challenges, available opportunities and techno-economic aspects of PAHs degradation using chemo-metric engineering designs are explained. Additionally, the review highlights how well these DoE tools can be suited for the sustainable socio-industrial sectors. Concomitantly, the futuristic scope and prospects to undertake new areas of research exploration were emphasized to unravel the least explored chemo-metric designs.

Applying artificial neural networks (ANNs) to solve solid waste-related issues: A critical review

Artificial neural networks (ANNs) have recently attracted significant attention in environmental areas because of their great self-learning capability and good accuracy in mapping complex nonlinear relationships. These properties of ANNs benefit their application in solving different solid waste-related issues. However, the configurations, including ANN framework, algorithm, data set partition, input parameters, hidden layer, and performance evaluation, vary and have not reached a consensus among relevant studies. To address the current state of the art of ANN application in the solid waste field and identify the commonalities of ANNs, this critical review was conducted by focusing on a modeling perspective and using 177 relevant papers published over the last decade (2010-2020). We classified the reviewed studies into four categories in terms of research scales. ANNs were found to be applied widely in waste generation and technological parameter prediction and proven effective in solving meso-microscale and microscale issues, including waste conversion, emissions, and microbial and dynamic processes. Given the difficulty of data collection in many solid waste-related issues, most studies included a data size of 101-150. For mathematical optimization, dividing the data into training-validation-test sets is preferable, and the training set is supposed to account for ~70%. A single hidden layer is usually sufficient, and the optimal numbers of hidden layer nodes most likely range from 4 to 20. This review is supposed to contribute basic and comprehensive knowledge to the researchers in general waste management and specialized ANN study on solid waste-related issues.

Application of neural network and time series modeling to study the suitability of drain water quality for irrigation: a case study from Egypt

Limited water resources are one of the major challenges facing Egypt during the current stage. The agricultural drainage water is an important water resource which can be reused for agriculture. Thus, the current study aims to assess the quality of drainage water for irrigation purpose through monitoring and predicting its suitability for irrigation. The chemical composition of Bahr El-Baqr water drain, especially salinity, as well as ions are mainly involved in calculating indicators of water suitability for irrigation, i.e., Ca²⁺, Mg²⁺, Na⁺, K⁺, HCO^-3, Cl^-, and SO₄^2-. Further analysis was carried out to evaluate the irrigation water quality index (IWQI) through integrated approaches and artificial neural network (ANN) model. Further, ARIMA models were developed to forecast IWQI of Bahr El-Baqr drain in Egypt. The results indicated that the computed IWQI values ranged between 46 and 81. Around 11% of the samples were classified as excellent water, while 89% of the samples were categorized as good water. The results of IWQI showed a standard deviation of 8.59 with a mean of 62.25, indicating that IWQI varied by 13.79% from the average. ANN model showed much higher prediction accuracy in IWQI modeling with R² value greater than 0.98 during training, testing and validation. A relatively good correlation was obtained, between the actual and forecasted IWQI based on the Akaike information criterion (AIC); the best fit models were ARIMA (1,0) (0,0) without seasonality. The determination coefficient (R²) of ARIMA models was 0.23. Accordingly, 23% of IWQI variability could be explained by different model parameters. These findings will support the water resources managers and decision-makers to manage the irrigation water resources that can be implemented in the future.

Artificial intelligence models to predict acute phytotoxicity in petroleum contaminated soils

Environment pollutants, especially those from total petroleum hydrocarbons (TPH), have a highly complex chemical, biological and physical impact on soils. Here we study this influence via modelling the TPH acute phytotoxicity effects on eleven samples of soils from Sakhalin island in greenhouse conditions. The soils were contaminated with crude oil in different doses ranging from the 3.0-100.0 g kg^-1. Measuring the Hordeum vulgare root elongation, the crucial ecotoxicity parameter, we have estimated. We have also investigated the contrast effect in different soils. To predict TPH phytotoxicity different machine learning models were used, namely artificial neural network (ANN) and support vector machine (SVM). The models under discussion were proved to be valid using the mean absolute error method (MAE), the root mean square error method (RMSE), and the coefficient of determination (R²). We have shown that ANN and SVR can successfully predict barley response based on soil chemical properties (pH, LOI, N, P, K, clay, TPH). The best achieved accuracy was as following: MAE - 8.44, RMSE -11.05, and R² -0.80.

Predicting the higher heating value of syngas pyrolyzed from sewage sludge using an artificial neural network

Sludge pyrolysis is a complex process including complicated reaction chemistry, phase transition, and transportation phenomena. To better evaluate the use of syngas, the monitoring and prediction of a higher heating value (HHV) is necessary. This study developed an artificial neural network (ANN) model to predict the HHV of syngas, with the process variables (i.e., sludge type, catalyst type, catalyst amount, pyrolysis temperature, and moisture content) as the inputs. In the first step, through optimizing various sets of parameters, a three-layer network including 8 input neurons, 15 hidden neurons, and 1 output neuron was established. Then, in the second step, an ANN model has been successfully used to predict the HHV of syngas, with a fitting correlation coefficient of 0.97 and a root mean square error (MSE) value of 14.62. The relative influence of input variables showed that the pyrolysis temperature and moisture content were the determining factors that affected the HHV of syngas. The results of optimization experiments showed that when temperature was 895 °C and the moisture content was 45.63 wt%, the highest HHV can be obtained as 438.22 kcal/m³-N. Moreover, the ANN model showed a higher prediction accuracy than other models like multiple linear regression and principal component regression. The model developed in this work may be used to predict the HHV of syngas using conventional operational parameters measured from in situ experiments, thus further providing predictive information for the use of syngas as energy and fuel.

Hybrid ash/biochar biocomposites as soil amendments for the alleviation of cadmium accumulation by Oryza sativa L. in a contaminated paddy field

A novel ash/biochar (A/B) biocomposite composed of 90% biomass bottom ash from agroforestry biomass direct-fired power plants, 5% animal-derived biochar from carcass pyrolysis, and 5% bentonite as an adhesive was amended in cadmium (Cd)-polluted paddy soil to alleviate cadmium accumulation by Oryza sativa L. Ash increased the soil pH and contributed exogenous available silicon. Biochar with high Ca/P components played an important role in soil cadmium immobilization. A 1-year field experiment with consecutive rice growing seasons (early and late rice) was conducted in Xiangtan, China, to examine the effects of A/B amendment in Cd-contaminated paddy soil. The A/B biocomposite was amended into soil through one-time addition at three application rates (1, 5, and 10 kg/m²). When A/B amendment was ≥5 kg/m², the soil pH increased from 4.11 to more than 6. The available silicon content in the soil even increased by 22.9 times. For early rice soil, the CaCl₂-extractable Cd(II) and toxicity characteristic leaching procedure (TCLP)-extractable Cd(II) decreased by 77.9%-96.1% and 52.4%-70.7%, respectively. A/B remarkably reduced Cd accumulation in rice organs, and this observation was related to A/B treatment rates. Ash and biochar contributed to the inhibition of Cd accumulation in rice organs and Cd translocation from roots to stems. The Cd concentrations in brown rice decreased to 0.11 and 0.12 mg/kg in early and late rice, respectively, and these values were lower than the national food safety standard limit value of China (0.2 mg/kg).

Effects of corn straw on dissipation of polycyclic aromatic hydrocarbons and potential application of backpropagation artificial neural network prediction model for PAHs bioremediation

In order to provide a viable option for remediation of PAHs-contaminated soils, a greenhouse experiment was conducted to assess the effect of corn straw amendment (1%, 2%, 4% or 6%, w/w) on dissipation of aged polycyclic aromatic hydrocarbons (PAHs) in contaminated soils. Backpropagation artificial neural network (BP-ANN) was applied to model the relationships between soil properties and PAHs concentration in soils. The removal rate of PAHs, enzyme activity (catalase and dehydrogenase), dissolved organic carbon (DOC) and microbial biomass carbon (MBC) in soils were investigated to evaluate the dissipation of PAHs under different ratio of corn straw amendment. The present study showed that corn straw amendment apparently accelerated the dissipation of PAHs after incubation of 112 days, especially under 4% and 6% treatments. Compared with non-amended soil, corn straw amendment significantly (p < 0.05) increased the removal rate of low molecular weight (LMW) PAHs and significantly (p < 0.05) enhanced the dissipation of high molecular weight (HMW) PAHs only under 6% treatment. Moreover, corn straw amendment increased activities of catalase and dehydrogenase, concentrations of DOC and MBC in soils, which are beneficial to the degradation of PAHs in soils. The performance of the BP-ANN model was assessed through the root mean square error (RMSE) and determination coefficient (R²). The results indicated that BP-ANN model could provide satisfactory prediction of PAHs concentration in soils during incubation period at R² and RMSE values of 0.948, 187.4 μg kg^-1, respectively. The results indicated that high amendment of corn straw was a potential option for remediation of PAHs-contaminated soils and that the BP-ANN model could successfully provide prompt prediction of PAHs concentration in soils.

Groundwater Level Prediction for the Arid Oasis of Northwest China Based on the Artificial Bee Colony Algorithm and a Back-propagation Neural Network with Double Hidden Layers

Groundwater is crucial for economic and agricultural development, particularly in arid areas where surface water resources are extremely scarce. The prediction of groundwater levels is essential for understanding groundwater dynamics and providing scientific guidance for the rational utilization of groundwater resources. A back propagation (BP) neural network based on the artificial bee colony (ABC) optimization algorithm was established in this study to accurately predict groundwater levels in the overexploited arid areas of Northwest China. Recharge, exploitation, rainfall, and evaporation were used as input factors, whereas groundwater level was used as the output factor. Results showed that the fitting accuracy, convergence rate, and stabilization of the ABC-BP model are better than those of the particle swarm optimization (PSO-BP), genetic algorithm (GA-BP), and BP models, thereby proving that the ABC-BP model can be a new method for predicting groundwater levels. The ABC-BP model with double hidden layers and a topology structure of 4-7-3-1, which overcame the overfitting problem, was developed to predict groundwater levels in Yaoba Oasis from 2019 to 2030. The prediction results of different mining regimes showed that the groundwater level in the study area will gradually decrease as exploitation quantity increases and then undergo a decline stage given the existing mining condition of 40 million m3/year. According to the simulation results under different scenarios, the most appropriate amount of groundwater exploitation should be maintained at 31 million m3/year to promote the sustainable development of groundwater resources in Yaoba Oasis.

New model for prediction binary mixture of antihistamine decongestant using artificial neural networks and least squares support vector machine by spectrophotometry method

In the present study, artificial neural networks (ANNs) and least squares support vector machines (LS-SVM) as intelligent methods based on absorption spectra in the range of 230-300nm have been used for determination of antihistamine decongestant contents. In the first step, one type of network (feed-forward back-propagation) from the artificial neural network with two different training algorithms, Levenberg-Marquardt (LM) and gradient descent with momentum and adaptive learning rate back-propagation (GDX) algorithm, were employed and their performance was evaluated. The performance of the LM algorithm was better than the GDX algorithm. In the second one, the radial basis network was utilized and results compared with the previous network. In the last one, the other intelligent method named least squares support vector machine was proposed to construct the antihistamine decongestant prediction model and the results were compared with two of the aforementioned networks. The values of the statistical parameters mean square error (MSE), Regression coefficient (R²), correlation coefficient (r) and also mean recovery (%), relative standard deviation (RSD) used for selecting the best model between these methods. Moreover, the proposed methods were compared to the high- performance liquid chromatography (HPLC) as a reference method. One way analysis of variance (ANOVA) test at the 95% confidence level applied to the comparison results of suggested and reference methods that there were no significant differences between them.