Multi-ahead electrical conductivity forecasting of surface water based on machine learning algorithms
Abstract The present research work focused on predicting the electrical conductivity (EC) of surface water in the Upper Ganga basin using four machine learning algorithms: multilayer perceptron (MLP), co-adaptive neuro-fuzzy inference system (CANFIS), random forest (RF), and decision tree (DT). The...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-09-01
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| Series: | Applied Water Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s13201-023-02005-1 |
| _version_ | 1797669591274487808 |
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| author | Deepak Kumar Vijay Kumar Singh Salwan Ali Abed Vinod Kumar Tripathi Shivam Gupta Nadhir Al-Ansari Dinesh Kumar Vishwakarma Ahmed Z. Dewidar Ahmed A. Al‑Othman Mohamed A. Mattar |
| author_facet | Deepak Kumar Vijay Kumar Singh Salwan Ali Abed Vinod Kumar Tripathi Shivam Gupta Nadhir Al-Ansari Dinesh Kumar Vishwakarma Ahmed Z. Dewidar Ahmed A. Al‑Othman Mohamed A. Mattar |
| author_sort | Deepak Kumar |
| collection | DOAJ |
| description | Abstract The present research work focused on predicting the electrical conductivity (EC) of surface water in the Upper Ganga basin using four machine learning algorithms: multilayer perceptron (MLP), co-adaptive neuro-fuzzy inference system (CANFIS), random forest (RF), and decision tree (DT). The study also utilized the gamma test for selecting appropriate input and output combinations. The results of the gamma test revealed that total hardness (TH), magnesium (Mg), and chloride (Cl) parameters were suitable input variables for EC prediction. The performance of the models was evaluated using statistical indices such as Percent Bias (PBIAS), correlation coefficient (R), Willmott’s index of agreement (WI), Index of Agreement (PI), root mean square error (RMSE) and Legate-McCabe Index (LMI). Comparing the results of the EC models using these statistical indices, it was observed that the RF model outperformed the other algorithms. During the training period, the RF algorithm has a small positive bias (PBIAS = 0.11) and achieves a high correlation with the observed values (R = 0.956). Additionally, it shows a low RMSE value (360.42), a relatively good coefficient of efficiency (CE = 0.932), PI (0.083), WI (0.908) and LMI (0.083). However, during the testing period, the algorithm’s performance shows a small negative bias (PBIAS = − 0.46) and a good correlation (R = 0.929). The RMSE value decreases significantly (26.57), indicating better accuracy, the coefficient of efficiency remains high (CE = 0.915), PI (0.033), WI (0.965) and LMI (− 0.028). Similarly, the performance of the RF algorithm during the training and testing periods in Prayagraj. During the training period, the RF algorithm shows a PBIAS of 0.50, indicating a small positive bias. It achieves an RMSE of 368.3, R of 0.909, CE of 0.872, PI of 0.015, WI of 0.921, and LMI of 0.083. During the testing period, the RF algorithm demonstrates a slight negative bias with a PBIAS of − 0.06. The RMSE reduces significantly to 24.1, indicating improved accuracy. The algorithm maintains a high correlation (R = 0.903) and a good coefficient of efficiency (CE = 0.878). The index of agreement (PI) increases to 0.035, suggesting a better fit. The WI is 0.960, indicating high accuracy compared to the mean value, while the LMI decreases slightly to − 0.038. Based on the comparative results of the machine learning algorithms, it was concluded that RF performed better than DT, CANFIS, and MLP. The study recommended using the current month’s total hardness (TH), magnesium (Mg), and chloride (Cl) parameters as input variables for multi-ahead forecasting of electrical conductivity (ECt+1, ECt+2, and ECt+3) in future studies in the Upper Ganga basin. The findings also indicated that RF and DT models had superior performance compared to MLP and CANFIS models. These models can be applied for multi-ahead forecasting of monthly electrical conductivity at both Varanasi and Prayagraj stations in the Upper Ganga basin. |
| first_indexed | 2024-03-11T20:46:39Z |
| format | Article |
| id | doaj.art-ad85d87c948b40b1ba18cbe297672bd5 |
| institution | Directory Open Access Journal |
| issn | 2190-5487 2190-5495 |
| language | English |
| last_indexed | 2024-03-11T20:46:39Z |
| publishDate | 2023-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Water Science |
| spelling | doaj.art-ad85d87c948b40b1ba18cbe297672bd52023-10-01T11:23:51ZengSpringerOpenApplied Water Science2190-54872190-54952023-09-01131012010.1007/s13201-023-02005-1Multi-ahead electrical conductivity forecasting of surface water based on machine learning algorithmsDeepak Kumar0Vijay Kumar Singh1Salwan Ali Abed2Vinod Kumar Tripathi3Shivam Gupta4Nadhir Al-Ansari5Dinesh Kumar Vishwakarma6Ahmed Z. Dewidar7Ahmed A. Al‑Othman8Mohamed A. Mattar9Department of Agricultural Engineering, Institute of Agricultural Sciences, Banaras Hindu University, [BHU]Department of Soil and Water Conservation Engineering, Acharya Narendra Deva University of Agriculture & TechnologyCollege of Science, University of Al-QadisiyahDepartment of Agricultural Engineering, Institute of Agricultural Sciences, Banaras Hindu University, [BHU]Department of Irrigation and Drainage Engineering, Acharya Narendra Deva University of Agriculture & TechnologyDepartment of Civil, Environmental, and Natural Resources Engineering, Lulea University of TechnologyDepartment of Irrigation and Drainage Engineering, Govind Ballabh Pant University of Agriculture and TechnologyPrince Sultan Bin Abdulaziz International Prize for Water Chair, Prince Sultan Institute for Environmental, Water and Desert Research, King Saud UniversityDepartment of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud UniversityPrince Sultan Bin Abdulaziz International Prize for Water Chair, Prince Sultan Institute for Environmental, Water and Desert Research, King Saud UniversityAbstract The present research work focused on predicting the electrical conductivity (EC) of surface water in the Upper Ganga basin using four machine learning algorithms: multilayer perceptron (MLP), co-adaptive neuro-fuzzy inference system (CANFIS), random forest (RF), and decision tree (DT). The study also utilized the gamma test for selecting appropriate input and output combinations. The results of the gamma test revealed that total hardness (TH), magnesium (Mg), and chloride (Cl) parameters were suitable input variables for EC prediction. The performance of the models was evaluated using statistical indices such as Percent Bias (PBIAS), correlation coefficient (R), Willmott’s index of agreement (WI), Index of Agreement (PI), root mean square error (RMSE) and Legate-McCabe Index (LMI). Comparing the results of the EC models using these statistical indices, it was observed that the RF model outperformed the other algorithms. During the training period, the RF algorithm has a small positive bias (PBIAS = 0.11) and achieves a high correlation with the observed values (R = 0.956). Additionally, it shows a low RMSE value (360.42), a relatively good coefficient of efficiency (CE = 0.932), PI (0.083), WI (0.908) and LMI (0.083). However, during the testing period, the algorithm’s performance shows a small negative bias (PBIAS = − 0.46) and a good correlation (R = 0.929). The RMSE value decreases significantly (26.57), indicating better accuracy, the coefficient of efficiency remains high (CE = 0.915), PI (0.033), WI (0.965) and LMI (− 0.028). Similarly, the performance of the RF algorithm during the training and testing periods in Prayagraj. During the training period, the RF algorithm shows a PBIAS of 0.50, indicating a small positive bias. It achieves an RMSE of 368.3, R of 0.909, CE of 0.872, PI of 0.015, WI of 0.921, and LMI of 0.083. During the testing period, the RF algorithm demonstrates a slight negative bias with a PBIAS of − 0.06. The RMSE reduces significantly to 24.1, indicating improved accuracy. The algorithm maintains a high correlation (R = 0.903) and a good coefficient of efficiency (CE = 0.878). The index of agreement (PI) increases to 0.035, suggesting a better fit. The WI is 0.960, indicating high accuracy compared to the mean value, while the LMI decreases slightly to − 0.038. Based on the comparative results of the machine learning algorithms, it was concluded that RF performed better than DT, CANFIS, and MLP. The study recommended using the current month’s total hardness (TH), magnesium (Mg), and chloride (Cl) parameters as input variables for multi-ahead forecasting of electrical conductivity (ECt+1, ECt+2, and ECt+3) in future studies in the Upper Ganga basin. The findings also indicated that RF and DT models had superior performance compared to MLP and CANFIS models. These models can be applied for multi-ahead forecasting of monthly electrical conductivity at both Varanasi and Prayagraj stations in the Upper Ganga basin.https://doi.org/10.1007/s13201-023-02005-1Decision treeMultilayer perceptronRandom forestCo-adaptive neuro-fuzzy inference systemElectrical conductivity |
| spellingShingle | Deepak Kumar Vijay Kumar Singh Salwan Ali Abed Vinod Kumar Tripathi Shivam Gupta Nadhir Al-Ansari Dinesh Kumar Vishwakarma Ahmed Z. Dewidar Ahmed A. Al‑Othman Mohamed A. Mattar Multi-ahead electrical conductivity forecasting of surface water based on machine learning algorithms Applied Water Science Decision tree Multilayer perceptron Random forest Co-adaptive neuro-fuzzy inference system Electrical conductivity |
| title | Multi-ahead electrical conductivity forecasting of surface water based on machine learning algorithms |
| title_full | Multi-ahead electrical conductivity forecasting of surface water based on machine learning algorithms |
| title_fullStr | Multi-ahead electrical conductivity forecasting of surface water based on machine learning algorithms |
| title_full_unstemmed | Multi-ahead electrical conductivity forecasting of surface water based on machine learning algorithms |
| title_short | Multi-ahead electrical conductivity forecasting of surface water based on machine learning algorithms |
| title_sort | multi ahead electrical conductivity forecasting of surface water based on machine learning algorithms |
| topic | Decision tree Multilayer perceptron Random forest Co-adaptive neuro-fuzzy inference system Electrical conductivity |
| url | https://doi.org/10.1007/s13201-023-02005-1 |
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