Multi-Environmental Parameters Dashboard for Susquehanna River Basin using Machine Learning techniques
| Autor: | Maria H. Rivero, Michael Meyer, Joshua L. Ramirez Paulino, Nikesh K. Pahuja, James Shallenberger, Roozbeh Sadeghian, Siamak Aram |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
geography
Geographic information system geography.geographical_feature_category business.industry Dashboard (business) Drainage basin Machine learning computer.software_genre Natural resource Random forest Environmental monitoring Environmental science Water quality Artificial intelligence Turbidity business computer |
| Zdroj: | 2020 International Conference on Computational Science and Computational Intelligence (CSCI). |
| Popis: | Data-driven decision making is essential for environmental monitoring and protection of natural resources. The Susquehanna River Basin Commission (SRBC) collects water quality data to monitor the condition of the Susquehanna River Basin. The practice of water quality management can be evolved introducing a Geographic Information Systems dashboard that incorporates Artificial Intelligence models to predict environmental parameters in real-time. This study presents an operational dashboard that integrates Machine Learning model using the data collected from the SRBC’s 62 different model sites from 2010 to 2019. Five daily time-series parameters were selected such as temperature, turbidity, pH, specific conductance, and dissolved oxygen. The results found the Random Forest as the best performance model to predict the specific conductance of water. These results outline an unprecedented tool for water quality management. |
| Databáze: | OpenAIRE |
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