Water quality management using hybrid machine learning and data mining algorithms : an indexing approach
- Aslam, Bilal, Maqsoom, Ahsen, Cheema, Ali, Ullah, Fahim, Alharbi, Abdullah, Imran, Muhammad
- Authors: Aslam, Bilal , Maqsoom, Ahsen , Cheema, Ali , Ullah, Fahim , Alharbi, Abdullah , Imran, Muhammad
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Access Vol. 10, no. (2022), p. 119692-119705
- Full Text:
- Reviewed:
- Description: One of the key functions of global water resource management authorities is river water quality (WQ) assessment. A water quality index (WQI) is developed for water assessments considering numerous quality-related variables. WQI assessments typically take a long time and are prone to errors during sub-indices generation. This can be tackled through the latest machine learning (ML) techniques renowned for superior accuracy. In this study, water samples were taken from the wells in the study area (North Pakistan) to develop WQI prediction models. Four standalone algorithms, i.e., random trees (RT), random forest (RF), M5P, and reduced error pruning tree (REPT), were used in this study. In addition, 12 hybrid data-mining algorithms (a combination of standalone, bagging (BA), cross-validation parameter selection (CVPS), and randomizable filtered classification (RFC)) were also used. Using the 10-fold cross-validation technique, the data were separated into two groups (70:30) for algorithm creation. Ten random input permutations were created using Pearson correlation coefficients to identify the best possible combination of datasets for improving the algorithm prediction. The variables with very low correlations performed poorly, whereas hybrid algorithms increased the prediction capability of numerous standalone algorithms. Hybrid RT-Artificial Neural Network (RT-ANN) with RMSE = 2.319, MAE = 2.248, NSE = 0.945, and PBIAS = -0.64 outperformed all other algorithms. Most algorithms overestimated WQI values except for BA-RF, RF, BA-REPT, REPT, RFC-M5P, RFC-REPT, and ANN- Adaptive Network-Based Fuzzy Inference System (ANFIS). © 2013 IEEE.
- Authors: Aslam, Bilal , Maqsoom, Ahsen , Cheema, Ali , Ullah, Fahim , Alharbi, Abdullah , Imran, Muhammad
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Access Vol. 10, no. (2022), p. 119692-119705
- Full Text:
- Reviewed:
- Description: One of the key functions of global water resource management authorities is river water quality (WQ) assessment. A water quality index (WQI) is developed for water assessments considering numerous quality-related variables. WQI assessments typically take a long time and are prone to errors during sub-indices generation. This can be tackled through the latest machine learning (ML) techniques renowned for superior accuracy. In this study, water samples were taken from the wells in the study area (North Pakistan) to develop WQI prediction models. Four standalone algorithms, i.e., random trees (RT), random forest (RF), M5P, and reduced error pruning tree (REPT), were used in this study. In addition, 12 hybrid data-mining algorithms (a combination of standalone, bagging (BA), cross-validation parameter selection (CVPS), and randomizable filtered classification (RFC)) were also used. Using the 10-fold cross-validation technique, the data were separated into two groups (70:30) for algorithm creation. Ten random input permutations were created using Pearson correlation coefficients to identify the best possible combination of datasets for improving the algorithm prediction. The variables with very low correlations performed poorly, whereas hybrid algorithms increased the prediction capability of numerous standalone algorithms. Hybrid RT-Artificial Neural Network (RT-ANN) with RMSE = 2.319, MAE = 2.248, NSE = 0.945, and PBIAS = -0.64 outperformed all other algorithms. Most algorithms overestimated WQI values except for BA-RF, RF, BA-REPT, REPT, RFC-M5P, RFC-REPT, and ANN- Adaptive Network-Based Fuzzy Inference System (ANFIS). © 2013 IEEE.
Assessing nitrate contamination risks in groundwater : a machine learning approach
- Awais, Muhammad, Aslam, Bilal, Maqsoom, Ahsen, Khalil, Umer, Imran, Muhammad
- Authors: Awais, Muhammad , Aslam, Bilal , Maqsoom, Ahsen , Khalil, Umer , Imran, Muhammad
- Date: 2021
- Type: Text , Journal article
- Relation: Applied Sciences (Switzerland) Vol. 11, no. 21 (2021), p.
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- Reviewed:
- Description: Groundwater is one of the primary sources for the daily water requirements of the masses, but it is subjected to contamination due to the pollutants, such as nitrate, percolating through the soil with water. Especially in built-up areas, groundwater vulnerability and contamination are of major concern, and require appropriate consideration. The present study develops a novel framework for assessing groundwater nitrate contamination risk for the area along the Karakoram Highway, which is a part of the China Pakistan Economic Corridor (CPEC) route in northern Pakistan. A groundwater vulnerability map was prepared using the DRASTIC model. The nitrate concentration data from a previous study were used to formulate the nitrate contamination map. Three machine learning (ML) models, i.e., Support Vector Machine (SVM), Multivariate Discriminant Analysis (MDA), and Boosted Regression Trees (BRT), were used to analyze the probability of groundwater contamination incidence. Furthermore, groundwater contamination probability maps were obtained utilizing the ensemble modeling approach. The models were calibrated and validated through calibration trials, using the area under the receiver operating characteristic curve method (AUC), where a minimum AUC threshold value of 80% was achieved. Results indicated the accuracy of the models to be in the range of 0.82–0.87. The final groundwater contamination risk map highlights that 34% of the area is moderately vulnerable to groundwater contamination, and 13% of the area is exposed to high groundwater contamination risk. The findings of this study can facilitate decision-making regarding the location of future built-up areas properly in order to mitigate the nitrate contamination that can further reduce the associated health risks. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record**
- Authors: Awais, Muhammad , Aslam, Bilal , Maqsoom, Ahsen , Khalil, Umer , Imran, Muhammad
- Date: 2021
- Type: Text , Journal article
- Relation: Applied Sciences (Switzerland) Vol. 11, no. 21 (2021), p.
- Full Text:
- Reviewed:
- Description: Groundwater is one of the primary sources for the daily water requirements of the masses, but it is subjected to contamination due to the pollutants, such as nitrate, percolating through the soil with water. Especially in built-up areas, groundwater vulnerability and contamination are of major concern, and require appropriate consideration. The present study develops a novel framework for assessing groundwater nitrate contamination risk for the area along the Karakoram Highway, which is a part of the China Pakistan Economic Corridor (CPEC) route in northern Pakistan. A groundwater vulnerability map was prepared using the DRASTIC model. The nitrate concentration data from a previous study were used to formulate the nitrate contamination map. Three machine learning (ML) models, i.e., Support Vector Machine (SVM), Multivariate Discriminant Analysis (MDA), and Boosted Regression Trees (BRT), were used to analyze the probability of groundwater contamination incidence. Furthermore, groundwater contamination probability maps were obtained utilizing the ensemble modeling approach. The models were calibrated and validated through calibration trials, using the area under the receiver operating characteristic curve method (AUC), where a minimum AUC threshold value of 80% was achieved. Results indicated the accuracy of the models to be in the range of 0.82–0.87. The final groundwater contamination risk map highlights that 34% of the area is moderately vulnerable to groundwater contamination, and 13% of the area is exposed to high groundwater contamination risk. The findings of this study can facilitate decision-making regarding the location of future built-up areas properly in order to mitigate the nitrate contamination that can further reduce the associated health risks. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record**
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