A Grey Wolf Optimized- Stacked Ensemble Approach for Nitrate Contamination Prediction in Cauvery Delta

• 영문명: A Grey Wolf Optimized- Stacked Ensemble Approach for Nitrate Contamination Prediction in Cauvery Delta
• 발행기관: 대한자원환경지질학회
• 저자명: Kalaivanan K Vellingiri J
• 간행물 정보: 『자원환경지질』57권 3호, 329~342쪽, 전체 14쪽
• 주제분류: 자연과학 > 지질학
• 파일형태: PDF
• 발행일자: 2024.06.30

국문 초록

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.

목차

1. Introduction

2. Literature Review

3. Methodology

4. Result and Discussion

5. Conclusion

Data Availability Statement

Disclosure Statement

Reference

키워드

nitrate prediction machine learning stacked ensemble decision tree random forest