ArticleEnvironmental monitoring and assessment2020
Estimation of nitrogen and phosphorus concentrations from water quality surrogates using machine learning in the Tri An Reservoir, Vietnam.
Article in Environmental monitoring and assessment, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
9 citing papers in PubMed.
- Article
- Article
- Determination of Optimal Predictors and Sampling Frequency to Develop Nutrient Soft Sensors Using Random Forest.Sensors (Basel, Switzerland) · 2023Article
- Value and limitations of machine learning in high-frequency nutrient data for gap-filling, forecasting, and transport process interpretation.Environmental monitoring and assessment · 2023Article
- Data Augmentation for a Virtual-Sensor-Based Nitrogen and Phosphorus Monitoring.Sensors (Basel, Switzerland) · 2023Article
- A Virtual Sensing Concept for Nitrogen and Phosphorus Monitoring Using Machine Learning Techniques.Sensors (Basel, Switzerland) · 2022Article
- From Fully Physical to Virtual Sensing for Water Quality Assessment: A Comprehensive Review of the Relevant State-of-the-Art.Sensors (Basel, Switzerland) · 2021Review
- Health risk assessment related to cyanotoxins exposure of a community living near Tri An Reservoir, Vietnam.Environmental science and pollution research international · 2021Article
- Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia.Environmental monitoring and assessment · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Surface water eutrophication due to excessive nutrients has become a major environmental problem around the world in the past few decades. Among these nutrients, nitrogen and phosphorus are two of the most important harmful cyanobacterial bloom (HCB) drivers. A reliable prediction of these parameters, therefore, is necessary for the management of rivers, lakes, and reservoirs. The aim of this study is to test the suitability of the powerful machine learning (ML) algorithm, random forest (RF), to provide information on water quality parameters for the Tri An Reservoir (TAR). Three species of nitrogen and phosphorus, including nitrite (N-NO
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.