Modeling Surface Water Quality Using the Adaptive Neuro-Fuzzy Inference System Aided by Input Optimization

Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony

Cardiovascular disease is one of the main causes of death worldwide which can be easily diagnosed by listening to the murmur sound of heartbeat sounds using a stethoscope. The murmur sound happens at the Lub-Dub, which indicates there are abnormalities in the heart. However, using the stethoscope for listening to the heartbeat sound requires a long time of training then only the physician can detect the murmuring sound. The existing studies show that young physicians face difficulties in this heart sound detection. Use of computerized methods and data analytics for detection and classification of heartbeat sounds will improve the overall quality of sound detection. Many studies have been worked on classifying the heartbeat sound; however, they lack the method with high accuracy. Therefore, this research aims to classify the heartbeat sound using a novel optimized Adaptive Neuro-Fuzzy Inferences System (ANFIS) by artificial bee colony (ABC). The data is cleaned, pre-processed, and MFCC is extracted from the heartbeat sounds. Then the proposed ABC-ANFIS is used to run the pre-processed heartbeat sound, and accuracy is calculated for the model. The results indicate that the proposed ABC-ANFIS model achieved 93% accuracy for the murmur class. The proposed ABC-ANFIS has higher accuracy in compared to ANFIS, PSO ANFIS, SVM, KSTM, KNN, and other existing studies. Thus, this study can assist physicians to classify heartbeat sounds for detecting cardiovascular disease in the early stages.

Development of fuzzy leachate pollution index for treatability-based classification of solid waste landfills

The fuzzy leachate pollution index (FLPI) was established to classify the landfill sites on the basis of their leachate pollution potential by considering the limitations of traditional methods. The FLPI was developed adopting 9 critical input parameters, i.e., TDS, pH, Cl, Cu, Pb, Cr, Zn, BOD, and COD, from 22 major landfill sites across India. Using these critical parameters, 3 groups, i.e., inorganic leachate strength (INLS), organic leachate strength (ORLS), and heavy metal leachate strength (HMLS), were generated to estimate the FLPI. The regression analysis, ANOVA, and sensitivity analysis were also performed to determine the significance and uncertainty of the index. The results showed that among all MFs, the triangular with overlapping open ends (TOO) MF was best fitted (R = 0.90) for FLPI estimation. Accordingly, 41% of the landfill sites showed less treatment while the others (59%) required moderate degree of treatment. The regression (R² = 0.92) and ANOVA (F value = 15.003, p = 0.000031) analyses described that the developed tool was significant (p < 0.05). The sensitivity analysis showed that Zn (R = 0.99) was the most influencing factor followed by BOD > COD > pH > Cr > Cu > Cl > Pb > TDS. The study provides an important tool that can also be used by researchers and scientists for investigating and evaluating various environmental problems.

Response of bacterial communities and function to dissolved organic matters in groundwater contaminated by landfill leachate

The migration and transformation of dissolved organic matter (DOM) caused by landfill leachate leakage affected the phylogenetic development of bacterial communities in groundwater around the landfill. Previous studies mainly focused on the hydrochemical properties of DOM in groundwater contaminated by landfill leachate and the relationships between groundwater quality parameters and bacterial communities. However, the changes in DOM components and bacterial communities caused by landfill leachate leakage and their correlations remained unclear. In this work, we analyzed the evolution characteristics of DOM and identified the bacterial communities and their corresponding functions in groundwater around the landfill. The results showed that DOM content in groundwater increased after the diffusion of landfill leachate to groundwater. Significant differences in characteristics between DOM components were presented at different locations in the landfill leachate plume due to the physical dilution and bacterial degradation of DOM. One of the obvious manifestations was the tendency of humic acid-like substances to accumulate at downstream points. Samples from the contaminated aquifer had higher diversity and abundance of bacterial communities than those in the uncontaminated aquifer. Anaerobic or facultative anaerobic bacteria played predominant roles in contaminated groundwater, due to the input of organic matter, nitrate, and ammonia nitrogen. Redundancy analysis indicated that the content of fulvic acid-like DOM had a conspicuous impact on the composition of bacterial communities in the polluted groundwater. Vogesella were the dominant bacteria at the genus level in groundwater around the landfill. Furthermore, Vogesella were significant for microbial utilization and played an important role in the production of fulvic acid-like DOM. These results indicated that landfill leachate pollution posed a potential threat to the structure and function of bacterial communities in groundwater, and provided a basis for exploring the interaction between DOM composition and bacterial communities in groundwater plume contaminated by landfill leachate.

A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions

Water quality has a significant influence on human health. As a result, water quality parameter modelling is one of the most challenging problems in the water sector. Therefore, the major factor in choosing an appropriate prediction model is accuracy. This research aims to analyse hybrid techniques and pre-processing data methods in freshwater quality modelling and forecasting. Hybrid approaches have generally been seen as a potential way of improving the accuracy of water quality modelling and forecasting compared with individual models. Consequently, recent studies have focused on using hybrid models to enhance forecasting accuracy. The modelling of dissolved oxygen is receiving more attention. From a review of relevant articles, it is clear that hybrid techniques are viable and precise methods for water quality prediction. Additionally, this paper presents future research directions to help researchers predict freshwater quality variables.

Application of Adaptive Neuro–Fuzzy Inference System for Forecasting Pavement Roughness in Laos

Laos Pavement Management System (PMS) manages 7700 km of National Roads (NRs) and estimates their Maintenance and Rehabilitation (MR) needs based on assessing pavement roughness conditions. This research aims to develop two International Roughness Index (IRI) models for Double Bituminous Surface Treatment (DBST) and Asphalt Concrete (AC) pavement sections using Adaptive Neuro-Fuzzy Inference System (ANFIS). A historical database of 14 years was employed for predicting the IRI as a function of pavement age and Cumulative Equivalent Single-Axle Load (CESAL). The optimum ANFIS structure comprises a hybrid learning algorithm with six fuzzy rules of generalized bell curve membership functions (Gbellmf) for the DBST model and nine fuzzy rules of two-sided Gaussian membership functions (Gauss2mf) for the AC model. Both models used the constant membership function for the output variable (IRI). The statistical evaluation results revealed that both ANFIS models (DBST and AC) have a good prediction capacity with high values of coefficient of determination (R2 0.93 and 0.88) and low values of Mean Absolute Error (MAE 0.28 and 0.27) and Root Mean Squared Percentage Error (RMSPE 7.03 and 9.98). In addition, results revealed that ANFIS models yielded higher prediction accuracy than Multiple Linear Regression (MLR) models previously developed under the same conditions.

Comparative Assessment of Individual and Ensemble Machine Learning Models for Efficient Analysis of River Water Quality

The prediction accuracies of machine learning (ML) models may not only be dependent on the input parameters and training dataset, but also on whether an ensemble or individual learning model is selected. The present study is based on the comparison of individual supervised ML models, such as gene expression programming (GEP) and artificial neural network (ANN), with that of an ensemble learning model, i.e., random forest (RF), for predicting river water salinity in terms of electrical conductivity (EC) and dissolved solids (TDS) in the Upper Indus River basin, Pakistan. The projected models were trained and tested by using a dataset of seven input parameters chosen on the basis of significant correlation. Optimization of the ensemble RF model was achieved by producing 20 sub-models in order to choose the accurate one. The goodness-of-fit of the models was assessed through well-known statistical indicators, such as the coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), and Nash–Sutcliffe efficiency (NSE). The results demonstrated a strong association between inputs and modeling outputs, where R2 value was found to be 0.96, 0.98, and 0.92 for the GEP, RF, and ANN models, respectively. The comparative performance of the proposed methods showed the relative superiority of the RF compared to GEP and ANN. Among the 20 RF sub-models, the most accurate model yielded the R2 equal to 0.941 and 0.938, with 70 and 160 numbers of corresponding estimators. The lowest RMSE values of 1.37 and 3.1 were yielded by the ensemble RF model on training and testing data, respectively. The results of the sensitivity analysis demonstrated that HCO3− is the most effective variable followed by Cl− and SO42− for both the EC and TDS. The assessment of the models on external criteria ensured the generalized results of all the aforementioned techniques. Conclusively, the outcome of the present research indicated that the RF model with selected key parameters could be prioritized for water quality assessment and management.

Effect of selected wastewater characteristics on estimation of SARS-CoV-2 viral load in wastewater

Wastewater-based epidemiology has been used as a tool for surveillance of COVID-19 infections. This approach is dependent on the detection and quantification of SARS-CoV-2 RNA in untreated/raw wastewater. However, the quantification of the viral RNA could be influenced by the physico-chemical properties of the wastewater. This study presents the first use of Adaptive Neuro-Fuzzy Inference System (ANFIS) to determine the potential impact of physico-chemical characteristics of wastewater on the detection and concentration of SARS-CoV-2 RNA in wastewater. Raw wastewater samples from four wastewater treatment plants were investigated over four months. The physico-chemical characteristics of the raw wastewater was recorded, and the SARS-CoV-2 RNA concentration determined via amplification with droplet digital polymerase chain reaction. The wastewater characteristics considered were chemical oxygen demand, flow rate, ammonia, pH, permanganate value, and total solids. The mean SARS-CoV-2 RNA concentrations ranged from 648.1(±514.6) copies/mL to 1441.0(±1977.8) copies/mL. Among the parameters assessed using the ANFIS model, ammonia and pH showed significant association with the concentration of SARS-CoV-2 RNA measured. Increasing ammonia concentration was associated with increasing viral RNA concentration and pH between 7.1 and 7.4 were associated with the highest SARS-CoV-2 concentration. Other parameters, such as total solids, were also observed to influence the viral RNA concentration, however, this observation was not consistent across all the wastewater treatment plants. The results from this study indicate the importance of incorporating wastewater characteristic assessment into wastewater-based epidemiology for a robust and accurate COVID-19 surveillance.

Variability of leachate quality and polluting potentials in light of leachate pollution index (LPI) - A global perspective

From a holistic perspective, this review is the first to comprehensively assess and characterise leachate quality from waste disposal facilities (WDFs), landfills and dumpsites, located in 61 countries worldwide. A continent wise grouping approach was adopted to identify the variability of leachate quality and polluting abilities in light of leachate pollution index (LPI). The literature data on leachate quality included 428 samples, with eighteen leachate parameters, classified under, organic, inorganic, and heavy metals. Statistically significant differences in LPI were found between different continents and WDFs demographic data, i.e., type, status, age, rainfall, etc. A negative correlation was found between pH and the majority of studied parameters, especially for heavy metals such as Pb, Zn, As, Hg, Cy, as the decrease in pH intensifies heavy metals' solubility. Based on the studied worldwide leachate data and WDFs age, an LPI rating was identified, where high, intermediate, and low contaminated leachate are typically classified with having an average of 26.5, 23.6 and 17.5, respectively. The provided database in this review could be of great importance in establishing a more comprehensive global databank by including other countries- and site-specific factors that are vital in enhancing the accuracy of LPI and formatting a more representative leachate diagnosis index.

Dissolved oxygen modelling of the Yamuna River using different ANFIS models

Dissolved oxygen (DO) is one of the prime parameters for assessing the water quality of any stream. Thus, the accurate estimation of DO is necessary to evolve measures for maintaining the riverine ecosystem and designing appropriate water quality improvement plans. Machine learning techniques are becoming valuable tools for the prediction and simulation of water quality parameters. A study has been performed in the Delhi stretch of the Yamuna River, India, and physiochemical parameters were examined for 5 years to simulate the DO using various machine learning techniques. Simulation and prediction competencies of adaptive neuro fuzzy inference system-grid partitioning (ANFIS-GP) and subtractive clustering (ANFIS-SC) were performed on high dimensional river characteristics. Four different models (M1, M2, M3 and M4) were developed using different combination of input parameters to predict DO. Results obtained from the models were evaluated using root mean square error and coefficient of determination (R²) to identify the appropriate combination of parameters to simulate the DO. Results suggest that both types of ANFIS models work adequately and accurately predict the DO; however, ANFIS-GP outperforms the ANFIS-SC. M4 generated R² of 0.953 from ANFIS-GP compared to 0.911 from ANFIS-SC.

Prediction of Geopolymer Concrete Compressive Strength Using Novel Machine Learning Algorithms

The innovation of geopolymer concrete (GPC) plays a vital role not only in reducing the environmental threat but also as an exceptional material for sustainable development. The application of supervised machine learning (ML) algorithms to forecast the mechanical properties of concrete also has a significant role in developing the innovative environment in the field of civil engineering. This study was based on the use of the artificial neural network (ANN), boosting, and AdaBoost ML approaches, based on the python coding to predict the compressive strength (CS) of high calcium fly-ash-based GPC. The performance comparison of both the employed techniques in terms of prediction reveals that the ensemble ML approaches, AdaBoost, and boosting were more effective than the individual ML technique (ANN). The boosting indicates the highest value of R² equals 0.96, and AdaBoost gives 0.93, while the ANN model was less accurate, indicating the coefficient of determination value equals 0.87. The lesser values of the errors, MAE, MSE, and RMSE of the boosting technique give 1.69 MPa, 4.16 MPa, and 2.04 MPa, respectively, indicating the high accuracy of the boosting algorithm. However, the statistical check of the errors (MAE, MSE, RMSE) and k-fold cross-validation method confirms the high precision of the boosting technique. In addition, the sensitivity analysis was also introduced to evaluate the contribution level of the input parameters towards the prediction of CS of GPC. The better accuracy can be achieved by incorporating other ensemble ML techniques such as AdaBoost, bagging, and gradient boosting.

Predictive Modeling Approach for Surface Water Quality: Development and Comparison of Machine Learning Models

Water pollution is an increasing global issue that societies are facing and is threating human health, ecosystem functions and agriculture production. The distinguished features of artificial intelligence (AI) based modeling can deliver a deep insight pertaining to rising water quality concerns. The current study investigates the predictive performance of gene expression programming (GEP), artificial neural network (ANN) and linear regression model (LRM) for modeling monthly total dissolved solids (TDS) and specific conductivity (EC) in the upper Indus River at two outlet stations. In total, 30 years of historical water quality data, comprising 360 TDS and EC monthly records, were used for models training and testing. Based on a significant correlation, the TDS and EC modeling were correlated with seven input parameters. Results were evaluated using various performance measure indicators, error assessment and external criteria. The simulated outcome of the models indicated a strong association with actual data where the correlation coefficient above 0.9 was observed for both TDS and EC. Both the GEP and ANN models remained the reliable techniques in predicting TDS and EC. The formulated GEP mathematical equations depict its novelty as compared to ANN and LRM. The results of sensitivity analysis indicated the increasing trend of input variables affecting TDS as HCO3− (22.33%) > Cl− (21.66%) > Mg2+ (16.98%) > Na+ (14.55%) > Ca2+ (12.92%) > SO42− (11.55%) > pH (0%), while, in the case of EC, it followed the trend as HCO3− (42.36%) > SO42−(25.63%) > Ca2+ (13.59%) > Cl− (12.8%) > Na+ (5.01%) > pH (0.61%) > Mg2+ (0%). The parametric analysis revealed that models have incorporated the effect of all the input parameters in the modeling process. The external assessment criteria confirmed the generalized outcome and robustness of the proposed approaches. Conclusively, the outcomes of this study demonstrated that the formulation of AI based models are cost effective and helpful for river water quality assessment, management and policy making.