Application of machine learning methods on predicting irrigation water quality
Journal
Taiwan Water Conservancy
Journal Volume
68
Journal Issue
1
Pages
1-14
Date Issued
2020
Author(s)
Abstract
The pollution of irrigation water leads to the pollution of farmlands directly or indirectly, which will further cast impacts on crop quality. Therefore, accurate predictions of future pollution events are essential for management of irrigation water. The aim of our study is to predict the potential occurrence of future abrupt pollution events by historical and real time monitoring water quality data. The 12 basic water quality monitoring stations and 2 heavy metal monitoring stations are selected in this study. We then use SVM and RF methods to predict whether the water quality might exceed normal standard in the near future. Our result shows that both of the methods received high credibility in predicting the standard-exceeding conditions of irrigation water. In addition, our study takes water level as well as precipitation factors into the models for a better precision in predicting of major standard-exceeding concentration of heavy metal, copper, in the irrigation water of study area. The result indicates that the prediction ability increased after water level factor was added, but not in the case of precipitation factor. Additionally, by making water quality data resemble the actual conditions, data segmentation should be conducted based on time series while analyzing the data instead of random selection. The accuracy of SVM model can be increased to 99.7% and 85.18% in the validation and test data set. By predicting potential occurring time of pollution events via historical as well as water monitoring data, it is possible to take necessary preventions to lower the risks of crops being polluted, which is a major issue in agricultural production nowadays. © 2020.
Other Subjects
Agricultural robots; Crops; Forecasting; Heavy metals; Irrigation; Machine learning; Monitoring; Pollution detection; Precipitation (chemical); Statistical tests; Water levels; Water quality; Accurate prediction; Agricultural productions; Heavy metal monitoring; Irrigation water quality; Machine learning methods; Real time monitoring; Water quality data; Water quality monitoring stations; Water pollution; concentration (composition); future prospect; heavy metal; irrigation; machine learning; monitoring; precipitation (chemistry); prediction; water level; water management; water pollution; water quality
Type
journal article