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Econ. Environ. Geol. 2024; 57(3): 329-342
Published online June 30, 2024
https://doi.org/10.9719/EEG.2024.57.3.329
© THE KOREAN SOCIETY OF ECONOMIC AND ENVIRONMENTAL GEOLOGY
A Grey Wolf Optimized- Stacked Ensemble Approach for Nitrate Contamination Prediction in Cauvery Delta
Correspondence to : *vellingiri.j@vit.ac.in
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided original work is properly cited.
Abstract
The exponential increase in nitrate pollution of river water poses an immediate threat to public health and the environment. This contamination is primarily due to various human activities, which include the overuse of nitrogenous fertilizers in agriculture and the discharge of nitrate-rich industrial effluents into rivers. As a result, the accurate prediction and identification of contaminated areas has become a crucial and challenging task for researchers. To solve these problems, this work leads to the prediction of nitrate contamination using machine learning approaches. This paper presents a novel approach known as Grey Wolf Optimizer (GWO) based on the Stacked Ensemble approach for predicting nitrate pollution in the Cauvery Delta region of Tamilnadu, India. The proposed method is evaluated using a Cauvery River dataset from the Tamilnadu Pollution Control Board. The proposed method shows excellent performance, achieving an accuracy of 93.31%, a precision of 93%, a sensitivity of 97.53%, a specificity of 94.28%, an F1-score of 95.23%, and an ROC score of 95%. These impressive results underline the demonstration of the proposed method in accurately predicting nitrate pollution in river water and ultimately help to make informed decisions to tackle these critical environmental problems.
Keywords nitrate prediction, machine learning, stacked ensemble, decision tree, random forest
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Research Paper
Econ. Environ. Geol. 2024; 57(3): 329-342
Published online June 30, 2024 https://doi.org/10.9719/EEG.2024.57.3.329
Copyright © THE KOREAN SOCIETY OF ECONOMIC AND ENVIRONMENTAL GEOLOGY.
A Grey Wolf Optimized- Stacked Ensemble Approach for Nitrate Contamination Prediction in Cauvery Delta
Kalaivanan K, Vellingiri J*
School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore-632014, India
Correspondence to:*vellingiri.j@vit.ac.in
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided original work is properly cited.
Abstract
The exponential increase in nitrate pollution of river water poses an immediate threat to public health and the environment. This contamination is primarily due to various human activities, which include the overuse of nitrogenous fertilizers in agriculture and the discharge of nitrate-rich industrial effluents into rivers. As a result, the accurate prediction and identification of contaminated areas has become a crucial and challenging task for researchers. To solve these problems, this work leads to the prediction of nitrate contamination using machine learning approaches. This paper presents a novel approach known as Grey Wolf Optimizer (GWO) based on the Stacked Ensemble approach for predicting nitrate pollution in the Cauvery Delta region of Tamilnadu, India. The proposed method is evaluated using a Cauvery River dataset from the Tamilnadu Pollution Control Board. The proposed method shows excellent performance, achieving an accuracy of 93.31%, a precision of 93%, a sensitivity of 97.53%, a specificity of 94.28%, an F1-score of 95.23%, and an ROC score of 95%. These impressive results underline the demonstration of the proposed method in accurately predicting nitrate pollution in river water and ultimately help to make informed decisions to tackle these critical environmental problems.
Keywords nitrate prediction, machine learning, stacked ensemble, decision tree, random forest
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Table 1 . Literature Survey.
Paper Machine Learning Mode Performance Metrics Key Findings Limitation Wagh et al. (2017) ANN R2= 0.75 ANN model outperformed other methods in predicting nitrate concentrations in the Kadava River catchment Small dataset size Rodriguez-Galiano et al. (2018) CART, RF, SVM AUC = 0.92 RF-SSFS method outperformed others in nitrate-related groundwater contamination Limited to a specific area Benzer et al. (2018) ANN Accuracy = 96 ANN model effectively predicted nitrate concentrations in surface waters in a river basin in China. The application of the model has not tested in the other regions. Rahmati et al. (2019) KNN, RF, SVM R2 = 0.72, RMSE = 10.41 RF model outperformed traditional regression models in estimating nitrate concentration in streams in Iran. The model depends on the assessing seasonal and interannual fluctuations of the nitrate concentrations. Knoll et al. (2019) GBR, CART, MLR, RF R2= 0.75 GBR could more accurately estimate nitrate levels The System did not enhance accuracy Jafari et al. (2019) ANFIS, SVM, MLP, GEP RMSE = 58.93, R = 0.998 GEP model provided accurate TDS prediction in Tabriz plain aquifer Machine learning techniques did not reduce time complexity Band et al. (2020) BANN, Cubist, RF, SVM R2 = 0.89, RF model outperformed others in Marvdasht watershed, Iran Limited to a specific region Bedi et al. (2020) ANN, XGB, SVM RMSE = 3.91 XGB model excelled in predicting nitrate and pesticide contamination Scarcity of labeled data for training advanced models. Hà et al. (2020) RF R2 = 0.92 RF model performed better in estimating nitrate and phosphorus concentrations in Tri An reservoir Focused solely on the Tri An reservoir. Latif et al. (2020) ANN Accuracy score = 0.94 ANN model was superior in forecasting nitrate levels in Feitsui reservoir, Taiwan Limited to five input Parameter Stamenković et al. (2020) ANN, MLR MAE = 0.53 ANN models showed good predictive ability for nitrates in river water Limited significant deviations in parameters Alizamir et al. (2021) Hybrid Bat-ELM R2= 0.89 Hybrid model effectively predicted daily chlorophyll-a concentration in rivers. Key factors influencing chlorophyll-a concentration may vary by ecosystem Pham et al. (2021) ANN, ANFIS, GMDH MAE = 0.0120.0219, NSE = 0.96 ANFIS method excelled in estimating Water Quality Index in surface wetlands Deep neural networks lacked effective incorporation of prior knowledge Lu et al. (2022) GBRT, LSTM, RF RMSE = 0.11 LSTM model performed best in predicting total phosphorus and nitrogen concentrations in Taihu Lake Focused on monthly data, limited temporal scope Ottong et al. (2022) LR, SVM, RF, GBM Accuracy = 87%, Precision = 100%, Sensitivity = 95.2%, Specificity = 100% GBM model effectively forecasted arsenic contamination risk in the Red River Delta Limited data points for model training Hu et al. (2023) XGB R2= 0.91 XGB model effectively predicted nitrogen and phosphorus concentrations in Taihu lakes. - Sulaiman et al. (2023) KNN, SVM, DT, NB, RF, GB, XGB Accuracy: 92.8% RF-PCA hybrid method outperformed other models in predicting nitrate concentrations for hydroponic plants. Limited Input Size Liang et al. (2024) GB R2= 0.627, MAE: 0.529, RMSE: 0.705 Developed models to predict nitrogen levels in Chongqing city using various predictors Tested in a small area Mehdaoui et al. (2024) RBF-NN Accuracy = 0.957 Introduced MLR and RBF-NN models to forecast nitrate levels in the Cheliff basin This model specifically suitable for this location
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Table 2 . Attribute of water quality dataset.
Variable Description Bureau of Indian Standard NO3 Nitrate 10 Ph Potential of Hydrogen 6.5-8.5 Cl Chloride 250 BOD Biological oxygen demand Not mentioned DO Dissolved Oxygen Not mentioned FC Fecal coliforms 0.2 TC Total coliforms Not mentioned Tu Turbidity Not mentioned Pa Phenolphthalein Alkalinity Not mentioned Tal Total Alkalinity 200 EC Electrical conductivity Not mentioned N Nitrogen 4 COD Chemical Oxygen Demand Not mentioned NH3 Ammonia 50 Ca Calcium 75 Th Total hardness 300 K Potassium 0.4 Mg Magnesium 30 S04 Sulphate 200 Na Sodium 4 TDS Total Dissolved Solids 500 PO4 Phosphate Not mentioned TFS Total Fixed Solids 500 Br Boron 0.3 TSS Total Suspended Solids 500 F Fluoride 1
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Table 3 . Confusion matrix result for test data.
S.NO Classifier TP FP FN TN Accuracy 1 GWO-Stacked Ensemble (Proposed) 76 4 6 75 0.93 2 RF 73 6 7 76 0.90 3 DT 71 8 9 73 0.88 4 MLP 72 10 10 69 0.87 5 KNN 70 10 11 69 0.86
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Table 4 . Comparison of the proposed model with the base classifiers.
Classifier Accuracy Precision Sensitivity Specificity F1-Score MCC GWO-Stacked Ensemble (Proposed) 0.93 0.95 0.92 0.92 0.93 0.85 RF 0.90 0.92 0.91 0.90 0.91 0.83 DT 0.88 0.89 0.88 0.89 0.89 0.78 MLP 0.87 0.87 0.86 0.85 0.87 0.74 KNN 0.86 0.87 0.87 0.86 0.86 0.72
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Table 5 . Comparison of the performance of the proposed methods against the basic classifier using data splitting validation.
Metrics Data Split Ratio Classification Method GWO-Stacked (Proposed) RF DT MLP KNN Accuracy 60-40 91.5 88 86.5 85.3 83.55 70-30 93.21 89.78 88 87.65 85.52 80-20 92.98 89.16 87.05 87.23 84.89 90-10 91.94 88.94 86.83 85.32 82.94 Precision 60-40 91.56 85.09 84.78 84.35 81.36 70-30 93 87.26 86.72 86.72 83.88 80-20 92.13 86.58 85.09 85.39 81.36 90-10 91.04 85.98 84.21 83.96 80 Sensitivity 60-40 95.13 91.89 91.27 89.28 87.18 70-30 97.53 93.33 93 91.86 89.10 80-20 96.41 91.56 91.04 89.74 88.72 90-10 95.23 90.86 90.12 88.21 87.23 Specificity 60-40 86.94 82.92 81.89 81.11 78.27 70-30 88.56 84.23 83.98 83.15 80 80-20 87.88 83.55 82.45 82.18 78.89 90-10 86.52 82.10 81.89 81.23 77.25 F1-Score 60-40 92.41 88.72 86.94 85.36 85.10 70-30 94.28 90 89.56 88.78 86.23 80-20 93.88 88.72 87.90 87.45 85.63 90-10 92.58 86.28 85.92 85.11 84.23 ROC 60-40 93.88 92.12 90.25 89.41 88.23 70-30 95.23 94.15 92.10 91 90.05 80-20 94.73 93.78 91.65 89.41 88.14 90-10 93.12 92.89 92.10 88.11 87.23 MCC 60-40 81.56 77.89 76.23 75.96 72.18 70-30 83 79.52 78.36 75.12 74.89 80-20 82.16 78.23 77.65 73.96 73.98 90-10 81.23 77.23 76.63 72.13 71.08
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Table 6 . Shows a comparison of performance between the Proposed method and existing research.
Author Model Accuracy (%) Proposed Model GWO-Stacked Ensemble 93 Latif et al. (2020) ANN 93 Sulaiman et al. (2023) KNN, SVM, DT, NB, RF, GB, XGB 92.8 Bhattarai et al. (2021) KNN, NB, RF, GB, SVM 92.8 Alizamir et al. (2021) Hybrid Bat-ELM 89 Knoll et al. (2019) GBR, CART, MLR, RF 75
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