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In recent decades, the discovery, extraction, and export of petroleum have significantly strengthened the economy. However, the processes of petroleum exploration, development and production have localized negative impacts on the atmosphere, soil, sediments, surface, groundwater, marine environment, and terrestrial ecosystems. The presence of petroleum hydrocarbons and waste streams has led to environmental pollution that poses risks to human health, affects socioeconomic conditions, and impacts communities in oil-producing countries. It is therefore crucial to promote a deeper understanding of petroleum contamination in soil, water, and atmosphere—an understanding that is actively evolving. The literature will mark a new milestone in the study of petroleum contamination and highlight significant advances in this environmental field. This comprehensive review examines the wide-ranging impacts of petroleum contamination on soil, water, and the atmosphere and aims to identify potential mitigation strategies that can reduce the impact on the environment and human health. Focusing on the latest technologies and practices for petroleum spill monitoring, remediation, and prevention, the report addresses all facets of this issue and helps researchers identify opportunities and gaps. It provides an assessment of the different treatment approaches for the period from 2010 to 2022 and discusses the advantages and disadvantages of each technique. Finally, it addresses the challenges that need to be overcome in the detection and treatment of oil spills. [ABSTRACT FROM AUTHOR]

Global groundwater resources have been threatened by both climate change and anthropogenic activities. Both factors could lead to groundwater depletion that might seriously threaten the living environment and food security. As one of the world's most water-stressed countries, Saudi Arabia has experienced long-term groundwater depletion due to excessive groundwater abstraction to meet the irrigation water demand. Moreover, rainfall and groundwater recharge are considered extremely low in most places in the Kingdom. Hence, a comprehensive assessment of groundwater risk in Saudi Arabia is necessary to avoid a worse scenario. The main objective of this study is to use the composite index to evaluate the groundwater risk in the Arabian Basin in Saudi Arabia. To achieve the objective, multiple variables, such as groundwater storage variations, groundwater reserves, total cropland area, and cropland expansion were integrated. The integration between physical hydrogeological assessment and anthropogenic factors is assumed to be a comprehensive risk measurement. Based on the final score, results demonstrated that Jouf and Najran could be classified as high-risk (17/100) and low-risk areas (71/100), respectively. The groundwater risk status was affected mainly by anthropogenic factors. Results of this study could serve as a diagnostic tool for decision-makers to prioritize and develop sustainable schemes, especially in high-risk areas. [ABSTRACT FROM AUTHOR]

In light of recent improvements in flood susceptibility mapping using machine learning models, there remains a lack of research focusing on employing ensemble algorithms like Light Gradient Boosting on Elastic-net Predictions (Light GBM) and Elastic-net Classifier (L2/Binomial Deviance) for mapping flood susceptibility in Qaa'Jahran, Yemen. This study aims to bridge this knowledge gap through the development and comparative performance of these models. This approach created the flood inventory map using satellite images and field observations. A geodatabase was used to create flood predictors such as aspect, altitude, distance to rivers, topographic wetness index (TWI), flow accumulation, lithology, distance to road, land use, profile curvature, plan curvature, slope, rainfall, soil type, Topographic Position Index (TPI), and Terrain Ruggedness Index (TRI). The developed models were trained using 80% of the data and evaluated using the remaining 20% to create a flood susceptibility map. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the map's accuracy. The results of this study indicated that the traditional (Elastic-net Classifier) model possessed high accuracy (AUC = 0.9457, F1 = 0.8916, Sensitivity = 0.9024, and Precision = 0.881) than the ensemble algorithm (Light Gradient Boosting on Elastic-net Predictions) (AUC = 0.9629, F1 = 0.9538, Sensitivity = 0.9688, and Precision = 0.9394). Based on the results of this study it can be concluded that these algorithms has a strong potential to offer a practical and affordable method for geospatial modeling of flood vulnerability. This information can be used to assess the flood emergency, early warning system and provide insights for planning and response purposes. [ABSTRACT FROM AUTHOR]

The aim of this research is to assess sustainable green building awareness in Kano State, in a case study of the Gwale local government area. This research makes use of both primary and secondary data to address these offered solutions. Descriptive and quantitative analysis using the BREAM and LEAD evaluation standards was used to analyze the case study and 251 questionnaires were distributed. To ensure a fair trial of each of the 251 building samples, they were chosen at random from various parts of the Gwale Yan-Alawa ward. A case study of a selected green building was chosen and analyzed. The logical comparison with Saudi Arabia was made. It is concluded that the Nigerian government at the national level should put more effort into encouraging green building construction through public awareness programs and incentives and subsidizing the green system. [ABSTRACT FROM AUTHOR]

A reliable traffic noise prediction model is one of the decision-making tools used in providing a noise friendly environment. In this study, four linear–nonlinear hybrid models were proposed to capture both linear and nonlinear patterns of the data by summing up the predicted traffic noise from the multilinear regression (MLR) and estimated residuals from four artificial intelligence-based models. The input variables for the models were volumes of cars, medium vehicles, buses, heavy vehicles, and average speed. Prior to the development of the hybrid model, the potential of boosted regression tree, feed forward neural network, Gaussian process regression (GPR), support vector regression and linear regression models for traffic noise prediction was evaluated and compared with each other. The performances of the single and hybrid models were evaluated using the Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), mean absolute error (MAE) and relative root mean square error (rRMSE). MLR-GPR hybrid demonstrated better prediction capability than all other models with NSE, RMSE, MAE and rRMSE values of 0.9312, 0.0427, 0.0347 and 7.4%, respectively. The study found that the efficiency of the linear models could be improved up to 27.26% when they are hybridized with the nonlinear models. [ABSTRACT FROM AUTHOR]

Floods are significant global hazards that generally lead to loss of lives and billions worth of properties, especially in flood-prone regions. Various excellent artificial intelligence algorithms that are aimed at predicting streamflow to minimize the effects of floods are proposed by researchers. Most of these models depend on the assumption that both training and testing data set are similar and sufficient. However, in reality, many of these data set varies along with time and are insufficient in some new basins. Motivated by the success of transfer learning in natural language processing, image processing and time series forecasting. In this paper, we proposed two hybrid transfer learning models for streamflow forecasting. The proposed models, which integrate Gated Recurrent Unit (GRU) with transfer learning and integration of Long Short Term Memory (LSTM) with transfer learning, are compared to our reference model. The proposed coupled Transfer Learning with the GRU (TL+GRU) model outperforms the baseline models, i.e., the transfer learning model and the coupled Transfer Learning with LSTM model (TL+LSTM) for most of the basins when streamflow and precipitation data set from Model Parameter Estimation Experiment (MOPEX) basins in the United States of America is used. As a result, we can finally conclude that, with Artificial Neural Networks' (ANN) integration to transfer learning, more enhanced performance are obtained. [ABSTRACT FROM AUTHOR]

EViews is a software designed for conducting econometric data analysis. There exists a one-way communication between EViews and R, as the former can run the code of the latter, but the reverse is not the case. We describe EviewsR, an R package which allows users of R, R Markdown and Quarto to execute EViews code. In essence, EviewsR does not only provide functions for base R, but also adds EViews to the existing knitr's knit-engines. We also show how EViews equation, graph, series, and table objects can be imported and customised dynamically and reproducibly in R, R Markdown and Quarto document. Therefore, EviewsR seeks to improve the accuracy, transparency and reproducibility of research conducted with EViews and R. [ABSTRACT FROM AUTHOR]

The degradation of groundwater (GW) quality due to seawater intrusion (SWI) is a major water security issue in water-scarce regions. This study aims to delineate the impact of SWI on the GW quality of a multilayered aquifer system in the eastern coastal region of Saudi Arabia. The physical and chemical properties of the GW were determined via field investigations and laboratory analyses. Irrigation indices (electrical conductivity (EC), potential salinity (PS), sodium adsorption ratio (SAR), Na%, Kelly's ratio (KR), magnesium adsorption ratio (MAR), and permeability index (PI)) and a SWI index (fsea) were obtained to assess the suitability of GW for irrigation. K-mean clustering, correlation analysis, and principal component analysis (PCA) were used to determine the relationship between irrigation hazard indices and the degree of SWI. The tested GW samples were grouped into four clusters (C1, C2, C3, and C4), with average SWI degrees of 15%, 8%, 5%, and 2%, respectively. The results showed that the tested GW was unsuitable for irrigation due to salinity hazards. However, a noticeable increase in sodium and magnesium hazards was also observed. Moreover, increasing the degree of SWI (fsea) was associated with increasing salinity, sodium, and magnesium, with higher values observed in the GW samples in cluster C1, followed by clusters C2, C3, and C4. The correlation analysis and PCA results illustrated that the irrigation indices, including EC, PS, SAR, and MAR, were grouped with the SWI index (fsea), indicating the possibility of using them as primary irrigation indices to reflect the impact of SWI on GW quality in coastal regions. The results of this study will help guide decision-makers toward proper management practices for SWI mitigation and enhancing GW quality for irrigation. [ABSTRACT FROM AUTHOR]

Precise Air temperature modeling is crucial for a sustainable environment. In this study, a novel binary optimized machine learning model, the random vector functional link (RVFL) with the integration of Moth Flame Optimization Algorithm (MFO) and Water Cycle Optimization Algorithm (WCA) is examined to estimate the monthly and daily temperature time series of Rajshahi Climatic station in Bangladesh. Various combinations of temperature and precipitation were used to predict the temperature time series. The prediction ability of the novel binary optimized machine learning model (RVFL-WCAMFO) is compared with the single optimized machine learning models (RVFL-WCA and RVFL-MFO) and the standalone machine learning model (RVFL). Root mean square errors (RMSE), the mean absolute error (MAE), the Nash–Sutcliffe efficiency (NSE), and the determination coefficient (R2) statistical indexes were utilized to access the prediction ability of the selected models. The proposed binary optimized machine learning model (RVFL-WCAMFO) outperformed the other single optimized and standalone machine learning models in prediction of air temperature time series on both scales, i.e., daily and monthly scale. Cross-validation technique was applied to determine the best testing dataset and it was found that the M3 dataset provided more accurate results for the monthly scale, whereas the M1 dataset outperformed the other two datasets on the daily scale. On the monthly scale, periodicity input was also added to see the effect on prediction accuracy. It was found that periodicity input improved the prediction accuracy of the models. It was also found that precipitation-based inputs did not provided very accurate results in comparison to temperature-based inputs. The outcomes of the study recommend the use of RVFL-WCAMFO in air temperature modeling. [ABSTRACT FROM AUTHOR]

Antiretroviral therapy (ART) is the common hope for HIV/AIDS-treated patients. Total commitments from individuals and the entire community are the major challenges faced during treatment. This study investigated the progress of ART in the Federal Teaching Hospital in Gombe state, Nigeria by using various records of patients receiving treatment in the ART hospital unit. We combined artificial intelligence (AI)-based models and correspondence analysis (CA) techniques to predict and visualize the progress of ART from the beginning to the end. The AI models employed are artificial neural networks (ANNs), adaptive neuro-fuzzy inference systems (ANFISs) and support-vector machines (SVMs) and a classical linear regression model of multiple linear regression (MLR). According to the outcome of this study, ANFIS in both training and testing outperformed the remaining models given the R2 (0.903 and 0.904) and MSE (7.961 and 3.751) values, revealing that any increase in the number of years of taking ART medication will provide HIV/AIDS-treated patients with safer and elongated lives. The contingency results for the CA and the chi-square test did an excellent job of capturing and visualizing the patients on medication, which gave similar results in return, revealing there is a significant association between ART drugs and the age group, while the association between ART drugs and marital status (93.7%) explained a higher percentage of variation compared with the remaining variables. [ABSTRACT FROM AUTHOR]

This study aims to assess, compare, and attribute the effects due to separate and combined land use/land cover (LULC) and climate changes on hydrological processes in a tropical catchment. The Soil and Water Assessment Tool (SWAT) model is set up and calibrated for a small contributing sub-basin of the Tana River Basin (TRB) in Kenya. The model is then applied to simulate the hydrological components (i.e., streamflow (FLOW), evapotranspiration (ET), soil water (SW), and water yield (WYLD)) for different combinations of LULC and climate scenarios. Land use data generated from Land Satellite 5 Thematic Mapper (Landsat 5TM) images for two different periods (1987 and 2011) and satellite-based precipitation data from the African Rainfall Climatology version 2 (ARC2) dataset are utilized as inputs to the SWAT model. The Nash–Sutcliffe model efficiency (NSE), coefficient of determination (R2), percent bias (PBIAS), and the ratio of root mean square error to the standard deviation (RSR) for daily streamflow were 0.73, 0.76, 3.16%, and 0.51 in calibration period, respectively, and 0.45, 0.54, 12.53%, and 0.79 in validation period, respectively, suggesting that the model performed relatively good. An analysis of the LULC data for the catchment showed that there was an increase in agricultural, grassland, and forested land with a concomitant decrease in woodland and shrubland. Simulation results revealed that change in climate had a more significant effect on the simulated parameters than the change in LULC. It is shown that changes in LULC only had very minor effects in the simulated parameters. The monthly mean FLOW and WYLD decreased by 0.02% and 0.11%, respectively, while ET and SW increased by a monthly mean of 0.2% and 2.2%. Varying the catchment climate and holding the land use constant reduced FLOW, ET, SW, and WYLD by an average monthly mean of 43.2%, 21%, 13%, and 70%, respectively, indicating that climate changes have more significant effects on the catchment hydrological processes than changes in LULC. Thus, it is necessary to evaluate and identify the isolated and combined effects of LULC and climatic changes when assessing impacts on the TRB's hydrological processes. [ABSTRACT FROM AUTHOR]

Wastewater treatment and reuse are being regarded as the most effective strategy for combating water scarcity threats. This study examined and reported the applications of the Internet of Things (IoT) and artificial intelligence in the phytoremediation of wastewater using Salvinia molesta plants. Water quality (WQ) indicators (total dissolved solids (TDS), temperature, oxidation-reduction potential (ORP), and turbidity) of the S. molesta treatment system at a retention time of 24 h were measured using an Arduino IoT device. Finally, four machine learning tools (ML) were employed in modeling and evaluating the predicted concentration of the total dissolved solids after treatment (TDSt) of the water samples. Additionally, three nonlinear error ensemble methods were used to enhance the prediction accuracy of the TDSt models. The outcome obtained from the modeling and prediction of the TDSt depicted that the best results were observed at SVM-M1 with 0.9999, 0.0139, 1.0000, and 0.1177 for R2, MSE, R, and RMSE, respectively, at the training stage. While at the validation stage, the R2, MSE, R, and RMSE were recorded as 0.9986, 0.0356, 0.993, and 0.1887, respectively. Furthermore, the error ensemble techniques employed significantly outperformed the single models in terms of mean square error (MSE) and root mean square error (RMSE) for both training and validation, with 0.0014 and 0.0379, respectively. [ABSTRACT FROM AUTHOR]

The emergence of health informatics opens new opportunities and doors for different disease diagnoses. The current work proposed the implementation of five different stand-alone techniques coupled with four different novel hybridized paradigms for the clinical prediction of hepatitis C status among patients, using both sociodemographic and clinical input variables. Both the visualized and quantitative performances of the stand-alone algorithms present the capability of the Gaussian process regression (GPR), Generalized neural network (GRNN), and Interactive linear regression (ILR) over the Support Vector Regression (SVR) and Adaptive neuro-fuzzy inference system (ANFIS) models. Hence, due to the lower performance of the stand-alone algorithms at a certain point, four different novel hybrid data intelligent algorithms were proposed, including: interactive linear regression-Gaussian process regression (ILR-GPR), interactive linear regression-generalized neural network (ILR-GRNN), interactive linear regression-Support Vector Regression (ILR-SVR), and interactive linear regression-adaptive neuro-fuzzy inference system (ILR-ANFIS), to boost the prediction accuracy of the stand-alone techniques in the clinical prediction of hepatitis C among patients. Based on the quantitative prediction skills presented by the novel hybridized paradigms, the proposed techniques were able to enhance the performance efficiency of the single paradigms up to 44% and 45% in the calibration and validation phases, respectively. [ABSTRACT FROM AUTHOR]

Floods, one of the most common natural hazards globally, are challenging to anticipate and estimate accurately. This study aims to demonstrate the predictive ability of four ensemble algorithms for assessing flood risk. Bagging ensemble (BE), logistic model tree (LT), kernel support vector machine (k-SVM), and k-nearest neighbour (KNN) are the four algorithms used in this study for flood zoning in Jeddah City, Saudi Arabia. The 141 flood locations have been identified in the research area based on the interpretation of aerial photos, historical data, Google Earth, and field surveys. For this purpose, 14 continuous factors and different categorical are identified to examine their effect on flooding in the study area. The dependency analysis (DA) was used to analyse the strength of the predictors. The study comprises two different input variables combination (C1 and C2) based on the features sensitivity selection. The under-the-receiver operating characteristic curve (AUC) and root mean square error (RMSE) were utilised to determine the accuracy of a good forecast. The validation findings showed that BE-C1 performed best in terms of precision, accuracy, AUC, and specificity, as well as the lowest error (RMSE). The performance skills of the overall models proved reliable with a range of AUC (89–97%). The study can also be beneficial in flash flood forecasts and warning activity developed by the Jeddah flood disaster in Saudi Arabia. [ABSTRACT FROM AUTHOR]

Seawater intrusion (SWI) is the main threat to fresh groundwater (GW) resources in coastal regions worldwide. Early identification and delineation of such threats can help decision-makers plan for suitable management measures to protect water resources for coastal communities. This study assesses seawater intrusion (SWI) and GW salinization of the shallow and deep coastal aquifers in the Al-Qatif area, in the eastern region of Saudi Arabia. Field hydrogeological and hydrochemical investigations coupled with laboratory-based hydrochemical and isotopic analyses (18O and 2H) were used in this integrated study. Hydrochemical facies diagrams, ionic ratio diagrams, and spatial distribution maps of GW physical and chemical parameters (EC, TDS, Cl−, Br−), and seawater fraction (fsw) were generated to depict the lateral extent of SWI. Hydrochemical facies diagrams were mainly used for GW salinization source identification. The results show that the shallow GW is of brackish and saline types with EC, TDS, Cl−, Br− concentration, and an increasing fsw trend seaward, indicating more influence of SWI on shallow GW wells located close to the shoreline. On the contrary, deep GW shows low fsw and EC, TDS, Cl−, and Br−, indicating less influence of SWI on GW chemistry. Moreover, the shallow GW is enriched in 18O and 2H isotopes compared with the deep GW, which reveals mixing with recent water. In conclusion, the reduction in GW abstraction in the central part of the study area raised the average GW level by three meters. Therefore, to protect the deep GW from SWI and salinity pollution, it is recommended to implement such management practices in the entire region. In addition, continuous monitoring of deep GW is recommended to provide decision-makers with sufficient data to plan for the protection of coastal freshwater resources. [ABSTRACT FROM AUTHOR]

The design and construction of a structurally and functionally stable pavement are pivotal for sustainable mobility. The need for a structurally stable and flexible pavement involves the assessment of the various engineering properties of asphalt. The use of the Witczak model is useful in assisting pavement designers with limited laboratory tests in the estimation of asphalt concrete dynamic modulus (E*). This is because the existing regression and artificial neural networks (ANN) model training using Witczak model input parameter has not exceeded 91% correlation between the measured and predicted E* and the huge error which could constitute a significant increase in pavement cost. In this research, five machine learning models were used to model E* and Log E*. To achieve the aim of this research, Witczak Model was adopted. Witczak model was used to input the obtained parameters and the database containing 7400 data points. The performance of the machine learning models was compared with the Witczak model. A global sensitivity analysis (GSA) was carried out to ascertain the model parameter importance to the output variance using the easyGSA MATLAB tool. The results of the research revealed that the Gaussian process regression (GPR) have a high predictive capability, with the highest coefficient of determination (R2) of 0.95 and 0.93 for E* and Log E*, respectively. The results strongly suggest that the GPR model could be used as an alternative to Witczak regression and ANN models. The GSA results showed that the gradation, volumetric properties and the phase angle have a significant effect on the E* prediction where the volumetric properties and cumulative weight retained on the 1.9 cm sieve induced the maximum effect on the prediction of Log E*. The outcome of this research will be of immense benefit to transportation engineers, highway engineers, researchers and construction workers on the use of this model for the prediction of the dynamic modulus of flexible pavement for sustainable mobility. [ABSTRACT FROM AUTHOR]

Unconsolidated earthen surface materials can retain heavy metals originating from different sources. These metals are dangerous to humans as well as the immediate environment. This danger leads to the need to assess various geochemical conditions of the materials. In this study, the assessment of topsoil materials' contamination with heavy metals (HMs) was conducted. The material's representative spatial samples were taken from various sources: agricultural, industrial, and residential areas. The materials include topsoil, eolian deposits, and other unconsolidated earthen materials. The samples were analyzed using the ICP-OES. The obtained results based on the experimental procedure indicated that the average levels of the heavy metals were: As (1.21 ± 0.69 mg/kg), Ba (110.62 ± 262 mg/kg), Hg (0.08 ± 0.18 mg/kg), Pb (6.34 ± 14.55 mg/kg), Ni (8.95 ± 5.66 mg/kg), V (9.98 ± 6.08 mg/kg), Cd (1.18 ± 4.33 mg/kg), Cr (31.79 ± 37.9 mg/kg), Cu (6.76 ± 12.54 mg/kg), and Zn (23.44 ± 84.43 mg/kg). Subsequently, chemometrics modeling and a prediction of Cr concentration (mg/kg) were performed using three different modeling techniques, including two artificial intelligence (AI) techniques, namely, generalized neural network (GRNN) and Elman neural network (Elm NN) models, as well as a classical multivariate statistical technique (MST). The results indicated that the AI-based models have a superior ability in estimating the Cr concentration (mg/kg) than MST, whereby GRNN can enhance the performance of MST up to 94.6% in the validation step. The concentration levels of most metals were found to be within the acceptable range. The findings indicate that AI-based models are cost-effective and efficient tools for trace metal estimations from soil. [ABSTRACT FROM AUTHOR]

This study presents optimization and prediction of tribological behaviour of filled polytetrafluoroethylene (PTFE) composites using hybrid Taguchi and support vector regression (SVR) models. To achieve the optimization, Taguchi Deng was employed considering multiple responses and process parameters relevant to the tribological behaviour. Coefficient of friction (µ) and specific wear rate (Ks) were measured using pin-on-disc tribometer. In this study, load, grit size, distance and speed were the process parameters. An L27 orthogonal array was applied for the Taguchi experimental design. A set of optimal parameters were obtained using the Deng approach for multiple responses of µ and KS. Analysis of variance was performed to study the effect of individual parameters on the multiple responses. To predict µ and Ks, SVR was coupled with novel Harris Hawks' optimization (HHO) and swarm particle optimization (PSO) forming SVR-HHO and SVR-PSO models respectively, were employed. Four model evaluation metrics were used to appraise the prediction accuracy of the models. Validation results revealed enhancement under optimal test conditions. Hybrid SVR models indicated superior prediction accuracy to single SVR model. Furthermore, SVR-HHO outperformed SVR-PSO model. It was found that Taguchi Deng, SVR-PSO and SVR-HHO models led to optimization and prediction with low cost and superior accuracy. [ABSTRACT FROM AUTHOR]

Natural streams longitudinal dispersion coefficient (Kx) is an essential indicator for pollutants transport and its determination is very important. Kx is influenced by several parameters, including river hydraulic geometry, sediment properties, and other morphological characteristics, and thus its calculation is a highly complex engineering problem. In this research, three relatively explored machine learning (ML) models, including Random Forest (RF), Gradient Boosting Decision Tree (GTB), and XGboost-Grid, were proposed for the Kx determination. The modeling scheme on building the prediction matrix was adopted from the well-established literature. Several input combinations were tested for better predictability performance for the Kx. The modeling performance was tested based on the data division for the training and testing (70–30% and 80–20%). Based on the attained modeling results, XGboost-Grid reported the best prediction results over the training and testing phase compared to RF and GTB models. The development of the newly established machine learning model revealed an excellent computed-aided technology for the Kx simulation. [ABSTRACT FROM AUTHOR]

The increases in extreme rainfall could increase the probable maximum flood (PMF) and pose a severe threat to the critical hydraulic infrastructure such as dams and flood protection structures. This study is conducted to assess the impact of climate change on PMF in a tropical catchment. Climate and inflow data of the Tenmengor reservoir, located in the state of Perak in Malaysia, have been used to calibrate and validate the hydrological model. The projected rainfall from regional climate model is used to generate probable maximum precipitation (PMP) for future periods. A hydrological model was used to simulate PMF from PMP estimated for the historical and two future periods, early (2031 − 2045) and late (2060 − 2075). The results revealed good performance of the hydrological model with Nash–Sutcliffe efficiency, 0.74, and the relative standard error, 0.51, during validation. The estimated rainfall depths were 89.5 mm, 106.3 mm, and 143.3 mm, respectively, for 5, 10, and 50 years of the return period. The study indicated an increase in PMP by 162% to 507% and 259% to 487% during early and late periods for different return periods ranging from 5 to 1000 years. This would cause an increase in PMF by 48.9% and 122.6% during early and late periods. A large increase in PMF indicates the possibility of devastating floods in the future in his tropical catchment due to climate change. [ABSTRACT FROM AUTHOR]

Groundwater resources (GWR) are vital to agricultural crop production, everyday life, and economic development. As a result, accurate groundwater level (GWL) prediction would aid in the long-term management of GWR. A comparative analysis was performed to test the predictive capabilities of models based on non-linear autoregressive with exogenous inputs (NARX) and extreme learning machine (ELM). Long-term predictions were performed using GWL and rainfall time series measured in two sites, Bogra and Dinajpur, in Bangladesh's northwest region from 1981 to 2017. The delay between precipitation and GWL was assessed through the cross-correlation function, computing an input delay equal to 2 months for both sites. Furthermore, the auto-correlation of the GWL was also performed to evaluate the optimal feedback delay, showing the seasonality of GWL fluctuations with a peak at 12 months for both sites. However, the sensitivity to changes in the feedback delay was also assessed, comparing the predictions produced for a feedback delay equivalent to 12 months with those computed for delays ranging from 1 to 11 months. Outputs of the two proposed models were evaluated using different metrics: the root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and correlation coefficient (CC). The results revealed that NARX models outperformed ELM models. NARX models were able to provide accurate long-term predictions, with R2 equal to 0.918 and 0.947 for Bogra and Dinajpur sites, respectively, with a forecasting horizon τ = 12 months. ELM models also provided good forecasts for Dinajpur, but less accurate for Bogra, with R2 respectively equal to 0.825 and 0.675 with τ = 12 months. This research would provide a practical and efficient approach to GWL prediction that could aid policymakers in implementing long-term GWR management. [ABSTRACT FROM AUTHOR]

Background: Hormone production by the thyroid gland is a prime aspect of maintaining body homeostasis. In this study, the ability of single artificial intelligence (AI)-based models, namely multi-layer perceptron (MLP), support vector machine (SVM), and Hammerstein–Weiner (HW) models, were used in the simulation of thyroidism status. The study's primary aim is to unveil the best performing model for the simulation of thyroidism status using hepatic enzymes and hormones as the independent variables. Three statistical metrics were used in evaluating the performance of the models, namely determination coefficient (R2), correlation coefficient (R), and mean squared error (MSE). Results: Considering the quantitative and visual presentation of the results obtained, it has been observed that the MLP model showed higher performance skills than SVM and HW, which improved their performances up to 3.77% and 12.54%, respectively, in the testing stages. Furthermore, to boost the performance of the single AI-based models, three different ensemble approaches were employed, including neural network ensemble (NNE), weighted average ensemble (WAE), and simple average ensemble (SAE). The quantitative predictive performance of the NNE technique boosts the performance of SAE and WAE approaches up to 2.85% and 1.22%, respectively, in the testing stage. Conclusions: Comparative performance of the ensemble techniques over the single models showed that NNE outperformed all the three AI-based models (MLP, SVM, and HW) and boosted their performance accuracy up to 7.44%, 11.212%, and 19.98%, respectively, in the testing stages. [ABSTRACT FROM AUTHOR]

Accurate stream flow quantification and prediction are essential for the local and global planning and management of basins to cope with climate change. The ability to forecast streamflow is crucial, as it can help mitigate flood risks. Long-term stream flow data records are needed for hydropower plant construction, flood prediction, watershed management, and long-term water supply use. An accurate assessment of streamflow is considered as very challenging and critical tasks. A new predicting model is developed in this research, combining the technique of sunflower optimization (SFA) as an evolutionary algorithm with the multi-layer perceptron (MLP) algorithm to predict streamflow in Malaysia's Jam Seyed Omar (JSO) and Muda Di Jeniang (MDJ) stations. Principal component analysis (PCA) was performed on Q (t) (t: the number of the current day) before model creation to pick essential inputs for a maximum of 6 lags. With the classical MLP and two other hybrid MLP models (MLP-particle swarm optimization (MLP-PSO) and MLP-genetic algorithm (MLP-GA)), the results of the MLP-sunflower algorithm (SFA) were benchmarked. As compared to other models, the MLP-SFA could be able to reduce the Root Mean Square Error (RMSE) by a value of between 12 and 21% at the JSO station and between 8 and 24% at the MDJ station. In conclusion, this research found that combining MLP with optimization algorithms improved the precision of the stand-alone MLP model, with SFA integration being the most efficient. [ABSTRACT FROM AUTHOR]

In the present research, the information on compressive strength of self-compacting concrete (SCC) containing rice husk ash (RHA) and calcium carbide waste (CCW) as an admixture cured for 28 days was provided. The research applied feedforward propagation neural network (FFNN), emotional neural network (EANN), and conventional linear regression (LR) in the prediction of compressive in which FFNN, EANN, and LR models were trained on the experimental data obtained from addition of 0%–10% RHA and 0%–20% CCW in the SCC mixtures. The results revealed that inclusion of CCW reduces the workability of SCC mixtures and increases in compressive strength at 28 days were observed for SCC mixture containing 10% RHA and 0% CCW against the reference mixtures. The results also indicated that all the AI models (FFNN, EANN, and LR) performed very well with R2-values higher than 0.8951 in both the testing and training stages. The results showed that EANN-M3, FFNN-M3, and LR-M3 combination has the highest performance evaluation criteria of R2 = 0.9733 and 0.9610, R2 = 0.9440 and 0.9454 and R2 = 0.9117 and 0.9205 in both training and testing stages, respectively. It indicates the proposed models' high accuracy in predicting the compressive strength σ of self-compacting concrete with rice husk ash as cement replacement and calcium carbide waste as supplementary materials. The result also suggested that other models, like emerging algorithms, hybrid models, and optimization methods, could enhance the models' performance. [ABSTRACT FROM AUTHOR]

Runoff–sediment process modeling is highly variable and nonlinear in nature. For sediment yield prediction, the difficulty of rainfall–runoff–sediment yield hydrological processes remains challenging. The present study uses a simple nonlinear dynamic (NLD) model to predict daily sediment yields, taking into account the degree of daily–sediment yield in catchment areas, and its findings were compared to three widely used models including artificial neural networks (ANN), support vector machine (SVM), and gene expression programming (GEP). The daily measured discharge–sediment data for 25 years were obtained from Shakkar Watershed; Central India as in the current study. The coefficient of correlation (CC), Nash-Sutcliff (NS), and root-mean-square error (RMSE) were employed to assess the performance of the models. The results show that the NLD model was found better than ANN, SVM, and GEP model. These models had correlation coefficient (CC = 0.975, 0.887, 0.843, and 0.901), root-mean-square error (RMSE = 0.748, 1.751, 1.961, and 1.545), and Nash–Sutcliffe efficiency (0.952, 0.784, 0.673, and 0.814) correspondingly. Hence, the NLD model can be used for predicting sediment. In order to implement appropriate measures of soil conservation in the watershed to reduce the sediment load in the river, predicting the sediment yield is very necessary to maximize the life of the structure. [ABSTRACT FROM AUTHOR]

Accurate prediction of dissolved oxygen (DO) concentration is important for managing healthy aquatic ecosystems. This study investigates the comparative potential of the emotional artificial neural network-genetic algorithm (EANN-GA), and three ensemble techniques, i.e. emotional artificial neural network (EANN), feedforward neural network (FFNN), and neural network ensemble (NNE), to predict DO concentration in the Kinta River basin of Malaysia. The performance of EANN-GA, EANN, FFNN, and NNE models in predicting DO was evaluated using statistical metrics and visual interpretation. Appraisal of the results revealed a promising performance of the NNE-M3 model (Nash-Sutcliffe efficiency (NSE) = 0.8743/0.8630, correlation coefficient (CC) = 0.9351/0.9113, mean square error (MSE) = 0.5757/0.6833 mg/L, root mean square error (RMSE) = 0.7588/0.8266 mg/L, and mean absolute percentage error (MAPE) = 20.6581/14.1675) during the calibration/validation period compared to EANN-GA, EANN, and FFNN models in DO prediction in the study basin. [ABSTRACT FROM AUTHOR]

This study aimed at employing three data-driven models, namely the Hammerstein-Weiner (HW) model, support vector machine (SVM), and feedforward back propagation neural network (FFBPNN) and traditional multi-linear regression, as well as two non-linear ensemble techniques viz: HW-ensemble and FFBPNN-ensemble, were employed to predict chemical oxygen demand (CODeff). For the prediction of the CODeff, two types of data were used, the first one being environmental data from the new Nicosia waste water treatment plant conductivity (Condinf), including total nitrogen (TNinf), total phosphorus (TPinf) and one-effluent parameter CODeff as M1, where the second was meteorology data from the National Aeronautics and Space Administration (NASA) (at 2 m above the Earth's surface), such as relative humidity (R2H), maximum temperature (T2M_M) and mean temperature (T2M) as M2, in a hybrid model M3, which was a combination of both the meteorology and environmental data M1 and M2. According to the performance criteria RMSE and DC of the single models, values of HW-M1 (0.0308 and 0.9686), HW-M2 (0.0322 and 0.9093) and SVM-M3 (0.025 and 0.9486) were recorded. The ensemble technique improved the performance of the single models in the verification phase by 12% and 19% for HW-E and FFBPNN-E, respectively. [ABSTRACT FROM AUTHOR]

In this article, three data-driven approaches were explored, including two artificial intelligence (AI) based models namely; Extreme Learning Machine (ELM) and Hammerstein–Weiner (HW) models and a trivial linear model namely; multilinear regression (MLR). In this context, the models were developed using the onset of diarrhoea, the total number of wet faeces, total number of faeces, weight of intestinal content (g) and length of the small intestine (cm) as the independent variables. In contrast, distance travelled by charcoal meal (C) and volume of intestinal content (I) were considered as the dependent variables for the prediction of the intestinal hypermotility and secretory inhibitory effects of the methanol leaf extract of Combretum hypopilinum (MECH). Three different performance indicators including; mean absolute percentage error (MAPE), Nash–Sutcliffe efficiency (NSE) and Root mean square error (RMSE) were employed in this research to calculate and determine the performance skills of the models. The obtained results indicated the reliable capability of ELM and HW over MLR model having NSE-values higher than 0.90 in both the calibration and verification stages. The results further demonstrated that, in terms of MAPE and RMSE, ELM and HW models showed higher performance efficiency than the MLR model. Even though HW outperformed the ELM and MLR models in the prediction of I. Whereas, ELM outperformed HW and MLR models in the prediction of C. Overall; the results proved the satisfactory ability of the AI-based models (HW and ELM) for modelling the Intestinal hypermotility and secretory inhibitory effects of MECH. [ABSTRACT FROM AUTHOR]

The dynamic and complex municipal wastewater treatment plant (MWWTP) process should be handled efficiently to safeguard the excellent quality of effluents characteristics. Most of the available mathematical models do not efficiently capture the MWWTP process, in such cases, the datadriven models are reliable and indispensable for effective modeling of effluents characteristics. In the present research, two nonlinear system identification (NSI) models namely; Hammerstein-Wiener model (HW) and nonlinear autoregressive with exogenous (NARX) neural network model, and a classical autoregressive (AR) model were proposed to predict the characteristics of the effluent of total suspended solids (TSSeff) and pHeff from Nicosia MWWTP in Cyprus. In order to attain the optimal models, two different combinations of input variables were cast through auto-correlation function and partial auto-correlation analysis. The prediction accuracy was evaluated using three statistical indicators the determination coefficient (DC), root mean square error (RMSE) and correlation coefficient (CC). The results of the appraisal indicated that the HW model outperformed NARX and AR models in predicting the pHeff, while the NARX model performed better than the HW and AR models for TSSeff prediction. It was evident that the accuracy of the HW increased averagely up to 18% with regards to the NARX model for pHeff. Likewise, the TSSeff performance increased averagely up to 25% with regards to the HW model. Also, in the validation phase, the HW model yielded DC, RMSE, and CC of 0.7355, 0.1071, and 0.8578 for pHeff, while the NARX model yielded 0.9804, 0.0049 and 0.9902 for TSSeff, respectively. For comparison with the traditional AR, the results showed that both HW and NARX models outperformed in (TSSeff) and pHeff prediction at the study location. Hence, the outcomes determined that the NSI model (i.e., HW and NARX) are reliable and resilient modeling tools that could be adopted for pHeff and TSSeff prediction. [ABSTRACT FROM AUTHOR]

In order to develop a tool for modeling the efficiency of municipal wastewater treatment plants (MWWTP), a reliable prediction tool is essential. In this research, two scenarios (I and II) were investigated for modeling the performance of Nicosia MWWTP. The extreme learning machine (ELM), which is a newly developed black-box model, combined with principal component analysis was developed in scenario I and two principal components (PCs) variables were generated, while in scenario II, traditional multi-layer perceptron (MLP) neural network and multiple linear regression (MLR) models were established for comparison. The daily measured data obtained from new Nicosia MWWTP includes (pHinf, Conductivityinf, BODinf, CODinf, Total-Ninf, Total-Pinf, NH4-Ninf, SSinf and TSSinf) as the input variables and (BODeff, CODeff, Total-Neff, Total-Peff) as the corresponding outputs. Taylor diagrams, box and whisker were also utilized to examine the similarities and comparisons between the observed and predicted values for both the ELM and PCs-ELM in scenario I. The obtained results based on the performance indices showed that the PCs-ELM model has higher performance accuracy than the novel ELM model. The results also showed increases of the PCs-ELM of about 12%, 2%, 20% and 6% for BODeff, CODeff, TNeff (total nitrogen) and TPeff (total phosphorite) with regard to the ELM model. Also, the comparison results demonstrated that ELM and MLP revealed higher prediction accuracy than the MLR model, and the ELM model comparably outperformed the MLP model. The overall results indicated that both the PCs-ELM and two scenarios could be alternative reliable tools for modeling the performance of Nicosia MWWTP. [ABSTRACT FROM AUTHOR]

Certain aspects of the dynamics of wastewater treatment plants appear to be chaotic, which makes modeling of the process of wastewater treatment plants extremely difficult. An appropriate model is key for the optimal operation of the plant. Conventional prediction techniques are not good enough to produce the desired results and determination of the suitable structure of using either fuzzy, artificial neural network or adaptive neuro-fuzzy interface system becomes cumbersome. This article proposed the application of advanced machine learning methodologies, for example, extreme learning machine (ELM), support vector machine (SVM) for modeling the Bunus regional sewage treatment plant. These advanced machine learning methods were also compared with conventional autoregressive integrated moving average (ARIMA). Observed data from the Bunus regional wastewater treatment plant was used for the modeling. The simulation results indicated that the ELM model performed better than the SVM and ARIMA models with a decrease in mean absolute percentage error by 19% and 29% than SVM and ARIMA models respectively. As the choice of input parameters often affects the modeling performance different combinations of input variables were selected. It was observed that influent biological oxygen demand, chemical oxygen demand, suspended solids, ammonium iron (NH4) were able to model the process better than other input parameter combinations. [ABSTRACT FROM AUTHOR]

Reliable simulation of retention factor (k) is crucial in high-performance liquid chromatography (HPLC) method development. In this research, three different Artificial intelligence (AI) based models, namely the multi-layer perceptron (MLP), Support vector machine (SVM) and Hammerstein–Weiner (HW) models, were employed as well as three ensemble techniques, i.e., neural network ensemble (NNE), weighted average ensemble (WAE) and simple average ensemble (SAE) to predict k for HPLC method development. In this context, the pH and composition of the mobile phase (methanol) are used as the input variables with the corresponding Methyclothiazide (M) and Amiloride (A) as antihypertensive target analytes. The performance efficiency of the models was evaluated using mean square error (MSE), determination coefficient (R2), and correlation coefficient (R). The results obtained from the single models showed that MLP outperformed the other two models and increased the prediction accuracy up to 1% and 3% for the HW and SVM models, respectively, for the prediction of M. However, for the prediction of A, SVM outperformed the other two models and increased the prediction accuracy up to 7% and 6% for HW and MLP, respectively. In the ensemble technique, the results obtained for the prediction of both M and A demonstrated that NNE increased the performance accuracy by 14% of the single models. Also, NNE proved to be superior to the two linear ensembles and improved the prediction accuracy up to 14% and 2% for SAE and WAE, respectively, for the simulation of M with R2 = 0.9962 and 0.9949 for both calibration and verification, and up to 9% and 6% for A with R2 = 0.9606 and 0.9569 for both calibration and verification phases respectively. The overall results depicted the reliability and robustness of both the AI-based models and justified the enhancement capability for ensemble techniques for both the two analytes. [ABSTRACT FROM AUTHOR]

Background: Measles is a vaccine preventable, highly transmissible viral infection that affects mostly children under five years. It has been ear marked for elimination and Nigeria adopted the measles elimination strategies of the World Health Organization (WHO) African region to reduce cases and deaths. This study was done to determine trends in measles cases in Bayelsa state, to describe cases in terms of person and place, identify gaps in the case-based surveillance data collection system and identify risk factors for measles infection.Methods: We carried out a secondary data analysis of measles case-based surveillance data for the period of January 2014 to December 2018 obtained in Microsoft Excel from the State Ministry of Health. Cases were defined according to WHO standard case definitions. We calculated frequencies, proportions, estimated odds ratios (OR), 95% confidence intervals (CI) and multivariate analysis.Results: A total of 449 cases of measles were reported. There were 245(54.6%) males and the most affected age group was 1-4 years with 288(64.1%) cases. Of all cases, 289(9.35%) were confirmed and 70 (48.27%) had received at least one dose of measles vaccine. There was an all-year transmission with increased cases in the 4th quarter of the year. Yenegoa local government area had the highest number of cases. Timeliness of specimen reaching the laboratory and the proportion of specimens received at the laboratory with results sent to the national level timely were below WHO recommended 80% respectively. Predictors of measles infection were, age less than 5 years (AOR: 0.57, 95% CI: 0.36-0.91) and residing in an urban area (AOR: 1.55, 95% CI:1.02-2.34).Conclusions: Measles infection occurred all-year round, with children less than 5 years being more affected. Measles case-based surveillance system showed high levels of case investigation with poor data quality and poor but improving indicators. Being less than 5 years was protective of measles while living in urban areas increased risk for infection. We recommended to the state government to prioritize immunization activities in the urban centers, start campaigns by the 4th quarter and continue to support measles surveillance activities and the federal government to strengthen regional laboratory capacities. [ABSTRACT FROM AUTHOR]

Forecasting of complex and chaotic phenomena such as rainfall is very challenging. Prediction of precipitation in airports helps agencies responsible for air traffic control in terms of planning and decision making. Conventional methods often provide imprecise forecasts, which may lead to flight delays and economic losses. This paper presents a comparison of linear and non-linear models for the forecasting of daily rainfall at Ercan Airport, Northern Cyprus. The study uses daily meteorological data consisting of relative humidity, minimum and maximum temperature, wind speed, solar radiation, and rainfall for 10 years (2008–2018) for Ercan Airport. The accuracy of the model is evaluated using the determination coefficient, mean square error and mean absolute percentage error performance indices. Simulation results indicate that the performance of the non-linear models is more accurate. The developed model could serve as a reliable rainfall forecasting tool for Ercan Airport. [ABSTRACT FROM AUTHOR]

One of the most challenging tasks in rainfall prediction is designing a reliable computational methodology owing the random and stochastic characteristics of time-series. In this study, the potential of five different data-driven models including Multilayer Perceptron (MLP), Least Square Support Vector Machine (LSSVM), Neuro-fuzzy, Hammerstein-Weiner (HW) and Autoregressive Integrated Moving Average (ARIMA) were employed for multi-station (Hien, Thank My, Hoi Khanh, Ai Nghia and Cai Lau) prediction of daily rainfall in the Vu Gia-Thu Bon River basin in Central Vietnam. Subsequently, hybrid ARIMA-MLP, ARIMA-LSSVM, ARIMA-NF and ARIMA-HW models were also utilized to predict the daily rainfall at these stations. The results were evaluated in terms of widely used performance criteria, viz.: determination coefficient (R2), root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (CC). Besides, the Taylor diagram is also used to examine and compare the similarity between the observed and predicted rainfall. The quantitative analysis indicated that the HW model increased the prediction accuracy by 5%, 3% and 2% at Hien, Ai Nghia and Cau Lau stations, respectively, compared to the other models. Likewise, the NF model increased the prediction accuracy at Thanh My and Hoi Khanh stations in contrast to the other models in terms of the mean absolute error. Also, the results of hybrid ARIMA-NF and ARIMA-HW models showed the best performance in terms of predictive skills and verified to increase the prediction accuracy in comparison to the single models. [ABSTRACT FROM AUTHOR]

In the present study, three different artificial intelligence based non-linear models, i.e. feed forward neural network (FFNN), adaptive neuro fuzzy inference system (ANFIS), support vector machine (SVM) approaches and a classical multi-linear regression (MLR) method were applied for predicting the performance of Nicosia wastewater treatment plant (NWWTP), in terms of effluent biological oxygen demand (BODeff), chemical oxygen demand (CODeff) and total nitrogen (TNeff). The daily data were used to develop single and ensemble models to improve the prediction ability of the methods. The obtained results of single models proved that, ANFIS model provides effective outcomes in comparison with single models. In the ensemble modeling, simple averaging ensemble, weighted averaging ensemble and neural network ensemble techniques were proposed subsequently to improve the performance of the single models. The results showed that in prediction of BODeff, the ensemble models of simple averaging ensemble (SAE), weighted averaging ensemble (WAE) and neural network ensemble (NNE), increased the performance efficiency of artificial intelligence (AI) modeling up to 14%, 20% and 24% at verification phase, respectively, and less than or equal to 5% for both CODeff and TNeff in calibration phase. This shows that NNE model is more robust and reliable ensemble method for predicting the NWWTP performance due to its non-linear averaging kernel. [ABSTRACT FROM AUTHOR]

In this study, adaptive neuro-fuzzy inference system, and feed forward neural network as two artificial intelligence-based models along with conventional multiple linear regression model were used to predict the multi-station modelling of dissolve oxygen concentration at the downstream of Mathura City in India. The data used are dissolved oxygen, pH, biological oxygen demand and water temperature at upper, middle and downstream of the river. To predict outlet of dissolved oxygen of the river in each station, considering different input combinations as i) 11 inputs parameters for all three locations except, dissolved oxygenat the downstream ii) 7 inputs for middle and downstream except dissolved oxygen, at the target location and lastly iii) 3 inputs for downstream location. To determine the accuracy of the model, root mean square error and determination coefficient were employed. The simulated results of dissolved oxygen at three stations indicated that, multi-linear regression is found not to be efficient for predicting dissolved oxygen. In addition, both artificial intelligence models were found to be more capable and satisfactory for the prediction. Adaptive neuro fuzzy inference system model demonstrated high prediction ability as compared to feed forward neural network model. The results indicated that adaptive neuro fuzzy inference system model has a slight increment in performance than feed forward neural network model in validation step. Adaptive neuro fuzzy inference system proved high improvement in efficiency performance over multilinear regression modeling up to 18% in calibration phase and 27% in validation phase for the best models. [ABSTRACT FROM AUTHOR]

Groundwater (GW) is a critical freshwater resource for billions of individuals worldwide. Rapid anthropogenic exploitation has increasingly deteriorated GW quality and quantity. Reliable estimation of complex hydrochemical properties of GW is crucial for sustainable development. Real field and experimental studies in an agricultural area from the significant sandstone aquifers (Wajid Aquifer) were conducted. For the modelling purpose, three types of computational models, including the emerging Hammerstein–Wiener (HW), back propagation neural network (BPNN), and statistical multi-variate regression (MVR), were developed for the multi-station estimation of total dissolved solids (TDS) (mg/L) and total hardness (TH) (mg/L). A geographic information system (GIS) was used for the spatial variability assessment of 32 hydrochemical and physical properties of the GW aquifer. A comprehensive visualized literature review spanning several decades was conducted in order to gain an understanding of the existing research and debates relevant to a particular GW and artificial intelligence (AI) study. The experimental data, pre-processing, and feature selection were conducted to determine the most dominant variables for AI-based modelling. The estimation results were evaluated using determination coefficient (DC), mean bias error (MBE), mean square error (MSE), and root mean square error (RMSE). The outcomes proved that TDS (mg/L) and TH (mg/L) correlated more than 90% and 70–85% with Ca2+, Cl−, Br−, NO3−, and Fe, and Na+, SO42−, Mg2+, and F− combinations, respectively. HW-M1 justified promising among all the models with MBE = 1.41 × 10−11, 1.14 × 10−14, and MSE = 7.52 × 10−2, 3.88 × 10−11 for TDS (mg/L), TH (mg/L), respectively. The accuracy proved merit for the overall development of and practical estimation of hydrochemical variables (TDS, TH) (mg/L) and decision-making benchmarks. [ABSTRACT FROM AUTHOR]

Increasing anthropogenic emissions due to rapid industrialization have triggered environmental pollution and pose a threat to the well-being of the ecosystem. In this study, the first scenario involved the spatio-temporal assessment of topsoil contamination with trace metals in the Dammam region, and samples were taken from 2 zones: the industrial (ID), and the agricultural (AG) area. For this purpose, more than 130 spatially distributed samples of topsoil were collected from residential, industrial, and agricultural areas. Inductively coupled plasma—optical emission spectroscopy (ICP-OES)—was used to analyze the samples for various trace metals. The second scenario involved the creation of different artificial intelligence (AI) models, namely an artificial neural network (ANN) and a support vector regression (SVR), for the estimation of zinc (Zn), copper (Cu), chromium (Cr), and lead (Pb) using feature-based input selection. The experimental outcomes depicted that the average concentration levels of HMs were as follows: Chromium (Cr) (31.79 ± 37.9 mg/kg), Copper (Cu) (6.76 ± 12.54 mg/kg), Lead (Pb) (6.34 ± 14.55 mg/kg), and Zinc (Zn) (23.44 ± 84.43 mg/kg). The modelling accuracy, based on different evaluation criteria, showed that agricultural and industrial stations showed performance merit with goodness-of-fit ranges of 51–91% and 80–99%, respectively. This study concludes that AI models could be successfully applied for the rapid estimation of soil trace metals and related decision-making. [ABSTRACT FROM AUTHOR]