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 (BOD
), chemical oxygen demand (COD
) and total nitrogen (TN
). 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 BOD
, 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 COD
and TN
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.
Concerning the significant increase in the negative effects of flash-floods worldwide, the main goal of this research is to evaluate the power of the Analytical Hierarchy Process (AHP), fi (kNN), ...K-Star (KS) algorithms and their ensembles in flash-flood susceptibility mapping. To train the two stand-alone models and their ensembles, for the first stage, the areas affected in the past by torrential phenomena are identified using remote sensing techniques. Approximately 70% of these areas are used as a training data set along with 10 flash-flood predictors. It should be remarked that the remote sensing techniques play a crucial role in obtaining eight out of 10 flash-flood conditioning factors. The predictive capability of predictors is evaluated through the Information Gain Ratio (IGR) method. As expected, the slope angle results in the factor with the highest predictive capability. The application of the AHP model implies the construction of ten pair-wise comparison matrices for calculating the normalized weights of each flash-flood predictor. The computed weights are used as input data in kNN–AHP and KS–AHP ensemble models for calculating the Flash-Flood Potential Index (FFPI). The FFPI also is determined through kNN and KS stand-alone models. The performance of the models is evaluated using statistical metrics (i.e., sensitivity, specificity and accuracy) while the validation of the results is done by constructing the Receiver Operating Characteristics (ROC) Curve and Area Under Curve (AUC) values and by calculating the density of torrential pixels within FFPI classes. Overall, the best performance is obtained by the kNN–AHP ensemble model.
•Artificial Intelligence models were applied for water quality parameter modeling.•Data from 3 stations along Yamuna river, India were used for the modeling.•Three ensemble models were applied to ...improve prediction of single models.•Results showed that neural based ensemble model improve prediction up to 14%.
In this study, three single Artificial Intelligence (AI) based models i.e., Back Propagation Neural Network (BPNN), Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Machine (SVM) and a linear Auto Regressive Integrated Moving Average (ARIMA) model as well as three different ensemble techniques i.e., Simple average ensemble (SAE), weighted average ensemble (WAE) and neural network ensemble (NNE) are applied for single and multi-step ahead modeling of dissolve oxygen (DO) in the Yamuna River, India. In this context, DO, Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Discharge (Q), pH, Ammonia (NH3), Water Temperature (WT) data for three different stations i.e., Hathnikund (SL1), Nizamuddin (SL2) and Udi (SL3) recorded by central pollution control board were used. The performance accuracy of the models was determined using Determination Coefficient (DC) and Root Mean Square Error (RMSE). The obtained results of the single models showed that, ANFIS model outperformed all other three models and increased averagely up to 7% and 19% for SL1 and SL2 in performance accuracy while for SL3, SVM model performed better than other models and increased the average performance up to 16%. In the ensemble techniques, the results showed that, for all the three stations, NNE could increase the average performance by single models up to 14% in the verification phase. This justified the reliability and robustness of NNE in multi-step ahead modeling of DO due to its promising ability in solving nonlinear processes.
Abstract
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 (K
s
) were measured using pin-on-disc tribometer. In this study, load, grit size, distance and speed were the process parameters. An L
27
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 K
S
. 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.
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 (
) 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 (
) 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 (
), 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.
Accurate runoff forecasting plays a key role in catchment water management and water resources system planning. To improve the prediction accuracy, one needs to strive to develop a reliable and ...accurate forecasting model for streamflow. In this study, the novel combination of the adaptive neuro-fuzzy inference system (ANFIS) model with the shuffled frog-leaping algorithm (SFLA) is proposed. Historical streamflow data of two different rivers were collected to examine the performance of the proposed model. To evaluate the performance of the proposed ANFIS-SFLA model, six different scenarios for the model input-output architecture were investigated. The results show that the proposed ANFIS-SFLA model (R
2
= 0.88; NS = 0.88; RMSE = 142.30 (m
3
/s); MAE = 88.94 (m
3
/s); MAPE = 35.19%) significantly improved the forecasting accuracy and outperformed the classic ANFIS model (R
2
= 0.83; NS = 0.83; RMSE = 167.81; MAE = 115.83 (m
3
/s); MAPE = 45.97%). The proposed model could be generalized and applied in different rivers worldwide.
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 (
O and
H) 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 (
) 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
trend seaward, indicating more influence of SWI on shallow GW wells located close to the shoreline. On the contrary, deep GW shows low
and EC, TDS, Cl
, and Br
, indicating less influence of SWI on GW chemistry. Moreover, the shallow GW is enriched in
O and
H 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.
Streamflow modeling is considered as an essential component for water resources planning and management. There are numerous challenges related to streamflow prediction that are facing water resources ...engineers. These challenges due to the complex processes associated with several natural variables such as non-stationarity, non-linearity, and randomness. In this study, a new model is proposed to predict long-term streamflow. Several lags that cover several years are abstracted using the potential of Extreme Gradient Boosting (XGB) then after the selected inputs variables are imposed into the predictive model (i.e., Extreme Learning Machine (ELM)). The proposed model is compared with the stand-alone schema in which the optimum lags of the variables are supplied into the XGB and ELM models. Hydrological variables including rainfall, temperature and evapotranspiration are used to build the model and predict the streamflow at Goksu-Himmeti basin in Turkey. The results showed that XGB model performed an excellent result in which can be used for predicting the streamflow pattern. Also, it is clear from the attained results that the accuracy of the streamflow prediction using XGB technique could be improved when the high number of lags was used. However, the implementation of the XGB is tree-based technique in which several issues could be raised such as overfitting problem. The proposed schema XGBELM in which XGB approach is selected the correlated inputs and ranking them according to their importance; then after, the selected inputs are supplied into the ELM model for the prediction process. The XGBELM model outperformed the stand-alone schema of both XGB and ELM models and the high-lagged schema of the XGB. It is important to indicate that the XGBELM model found to improve the prediction ability with minimum variables number.
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 (
R
2
), 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
R
2
= 0.9962 and 0.9949 for both calibration and verification, and up to 9% and 6% for A with
R
2
= 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.
Fluoride and nitrate contamination of groundwater is a major environmental issue in the world's arid and semiarid regions. This issue is severe in both developed and developing countries. This study ...aimed at assessing the concentration levels, contamination mechanisms, toxicity, and human health risks of NO3− and F− in the groundwater within the coastal aquifers of the eastern part of Saudi Arabia using a standard integrated approach. Most of the tested physicochemical properties of the groundwater exceeded their standard limits. The water quality index and synthetic pollution index evaluated the suitability of the groundwater and showed that all the samples have poor and unsuitable quality for drinking. The toxicity of F− was estimated to be higher than NO3−. Also, the health risk assessment revealed higher risks due to F− than NO3−. Younger populations had higher risks than elderly populations. For both F− and NO3−, the order of health risk was Infants > Children > Adults. Most of the samples posed medium to high chronic risks due to F− and NO3− ingestion. However, negligible health risks were obtained for potential dermal absorption of NO3−. Na–Cl and Ca–Mg–Cl water types predominate in the area. Pearson's correlation analysis, principal component analysis, regression models, and graphical plots were used to determine the possible sources of the water contaminants and their enrichment mechanisms. Geogenic and geochemical processes had greater impact he groundwater chemistry than anthropogenic activities. For the first time, these findings provide public knowledge on the overall water quality of the coastal aquifers and could help the inhabitants, water management authorities, and researchers to identify the groundwater sources that are most desirable for consumption and the human populations that are vulnerable to non-carcinogenic health risks.
Display omitted
•The tested groundwater is unsuitable for drinking.•Toxicity and human health risks of NO3– and F– were addressed.•The toxicity of F- was estimated to be higher than NO3–.•Groundwater chemistry is most affected by geological and geochemical processes.
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