Estimation of nitrogen and phosphorus concentrations from water quality surrogates using machine learning in the Tri An

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Estimation of nitrogen and phosphorus concentrations from water quality surrogates using machine learning in the Tri An Reservoir, Vietnam Nam-Thang Ha & Hao Quang Nguyen & Nguyen Cung Que Truong & Thi Luom Le & Van Nam Thai & Thanh Luu Pham

Received: 3 August 2020 / Accepted: 3 November 2020 # Springer Nature Switzerland AG 2020

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 (NNO2−), nitrate (N-NO3−), and phosphate (P-PO43−),

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