Modeling Surface Water Quality Using the Adaptive Neuro-Fuzzy Inference System Aided by Input Optimization
20 Apr 2021-Sustainability (Multidisciplinary Digital Publishing Institute)-Vol. 13, Iss: 8, pp 4576
TL;DR: The outcome of the present study reveals that the ANFIS modeling, aided with data pre-processing and input optimization, is a suitable technique for simulating the quality of surface water.
Abstract: Modeling surface water quality using soft computing techniques is essential for the effective management of scarce water resources and environmental protection. The development of accurate predictive models with significant input parameters and inconsistent datasets is still a challenge. Therefore, further research is needed to improve the performance of the predictive models. This study presents a methodology for dataset pre-processing and input optimization for reducing the modeling complexity. The objective of this study was achieved by employing a two-sided detection approach for outlier removal and an exhaustive search method for selecting essential modeling inputs. Thereafter, the adaptive neuro-fuzzy inference system (ANFIS) was applied for modeling electrical conductivity (EC) and total dissolved solids (TDS) in the upper Indus River. A larger dataset of a 30-year historical period, measured monthly, was utilized in the modeling process. The prediction capacity of the developed models was estimated by statistical assessment indicators. Moreover, the 10-fold cross-validation method was carried out to address the modeling overfitting issue. The results of the input optimization indicate that Ca2+, Na+, and Cl− are the most relevant inputs to be used for EC. Meanwhile, Mg2+, HCO3−, and SO42− were selected to model TDS levels. The optimum ANFIS models for the EC and TDS data showed R values of 0.91 and 0.92, and the root mean squared error (RMSE) results of 30.6 µS/cm and 16.7 ppm, respectively. The optimum ANFIS structure comprises a hybrid training algorithm with 27 fuzzy rules of triangular fuzzy membership functions for EC and a Gaussian curve for TDS modeling, respectively. Evidently, the outcome of the present study reveals that the ANFIS modeling, aided with data pre-processing and input optimization, is a suitable technique for simulating the quality of surface water. It could be an effective approach in minimizing modeling complexity and elaborating proper management and mitigation measures.
Citations
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TL;DR: In this paper , various machine learning classifiers and their stacking ensemble models were used to classify the WQ data via the Water Quality Index (WQI), and the results revealed that the CATBoost model offered the most accurate classifier with a percentage of 94.51.
Abstract: Monitoring water quality is essential for protecting human health and the environment and controlling water quality. Artificial Intelligence (AI) offers significant opportunities to help improve the classification and prediction of water quality (WQ). In this study, various AI algorithms are assessed to handle WQ data collected over an extended period and develop a dependable approach for forecasting water quality as accurately as possible. Specifically, various machine learning classifiers and their stacking ensemble models were used to classify the WQ data via the Water Quality Index (WQI). The studied classifiers included Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), CATBoost, XGBoost, and Multilayer Perceptron (MLP). The dataset used in the study included 1679 samples and their meta-data collected over nine years. In addition, precision-recall curves and Receiver Operating Characteristic curves (ROC) were used to assess the performance of the various classifiers. The findings revealed that the CATBoost model offered the most accurate classifier with a percentage of 94.51. Moreover, after applying stacking ensemble models with all classifiers, accuracy reached 100% in various Meta-classifiers. Furthermore, the CATBoost achieved the highest accuracy as a primary gradient boosting algorithm and a meta classifier. Therefore, the boosting algorithm is proposed as a reliable approach for the WQ classification. The analysis presented in this article presents a framework that can support the efforts of researchers working toward water quality improvement using artificial intelligence.
44 citations
TL;DR: In this paper, 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 geopolymer concrete (GPC) is presented.
Abstract: 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.
35 citations
TL;DR: A framework for tuning the hyper-parameters of feed forward neural network and gene expression programming with particle swarm optimization with PSO is proposed, which demonstrated that the implementation of artificial intelligence models with optimization routine can lead to optimized models for accurate prediction of water quality.
29 citations
TL;DR: In this article, the authors investigated 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.
Abstract: 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%) > SO4
2?? (11.55%) > pH (0%), while, in the case of EC, it followed the trend as HCO3
??
(42.36%) > SO4
2??(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.
19 citations
TL;DR: In this paper, the authors used 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.
19 citations
References
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01 May 1993
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Abstract: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested. >
15,085 citations
"Modeling Surface Water Quality Usin..." refers methods in this paper
...See Figure 3 for ANFIS architecture adapted from [42]....
[...]
Proceedings Article•
20 Aug 1995TL;DR: The results indicate that for real-word datasets similar to the authors', the best method to use for model selection is ten fold stratified cross validation even if computation power allows using more folds.
Abstract: We review accuracy estimation methods and compare the two most common methods crossvalidation and bootstrap. Recent experimental results on artificial data and theoretical re cults in restricted settings have shown that for selecting a good classifier from a set of classifiers (model selection), ten-fold cross-validation may be better than the more expensive leaveone-out cross-validation. We report on a largescale experiment--over half a million runs of C4.5 and a Naive-Bayes algorithm--to estimate the effects of different parameters on these algrithms on real-world datasets. For crossvalidation we vary the number of folds and whether the folds are stratified or not, for bootstrap, we vary the number of bootstrap samples. Our results indicate that for real-word datasets similar to ours, The best method to use for model selection is ten fold stratified cross validation even if computation power allows using more folds.
11,185 citations
"Modeling Surface Water Quality Usin..." refers methods in this paper
...Therefore, to verify the generalized capability and reduce the overfitting problem of a learning model, the 10-fold cross-validation method has been recommended in the literature [17,44,45]....
[...]
Book•
01 Jan 1976
TL;DR: Water quality for agriculture, water quality in agriculture for agriculture as mentioned in this paper, water quality of agriculture, Water quality of water for agriculture in agriculture, مرکز فناوری اطلاعات و اسلاز رسانی
Abstract: Water quality for agriculture , Water quality for agriculture , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی
3,518 citations
TL;DR: A new, mixed aggregation function is developed for ensuring that the index score tells us something useful and does not hide important information, than the existing index used in Taiwan does.
Abstract: By exploring the behavior and limitations of conventional methods for quality evaluation, a better overall index for water quality in Taiwan and its application in Keya River are proposed. The thirteen variables are employed for the quality assessment. Numerical scales relating to the degree of quality are established for each variable to assess variations in quality and to convey findings in a comprehensive manner to others. The employed variables are categorized into three aspects referred to the principal components analysis, which are 'organics', 'particulates' and 'microorganisms'. The overall index consists of the geometric mean for the three defined aspects and multiplying by three coefficients, which reflect quality degrees of temperature, pH and toxicity, respectively. Besides, the arithmetic mean is addressed among the variables associated in the same aspect. Finally, a fresh, mixed aggregation function is developed for ensuring that the index score tells us something useful and does not hide important information. A case study of the Keya River is made to illustrate the application of this new index system. The new proposed index provides comprehensive information for water quality assessment, than the existing index used in Taiwan does.
357 citations