Autonomous calibration of EFDC for predicting chlorophyll-a using reinforcement learning and a real-time monitoring system-Reference-Cited by-同舟云学术

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Autonomous calibration of EFDC for predicting chlorophyll-a using reinforcement learning and a real-time monitoring system

Published:2023-10 Issue: Volume:168 Page:105805
ISSN:1364-8152
Container-title:Environmental Modelling & Software
language:en
Short-container-title:Environmental Modelling & Software

Author:

Hong Seok Min,Abbas Ather,Kim Soobin,Kwon Do Hyuck,Yoon Nakyung,Yun Daeun,Lee Sanguk,Pachepsky Yakov,Pyo JongCheol,Cho Kyung Hwa

Publisher

Elsevier BV

Subject

Ecological Modeling,Environmental Engineering,Software

Reference71 articles.

1. Predicting cyanobacterial harmful algal blooms (CyanoHABs) in a regulated river using a revised EFDC model;Ahn;Water,2021

2. Deep reinforcement learning: a brief survey;Arulkumaran;IEEE Signal Process. Mag.,2017

3. Detection of phycobilin pigments and their seasonal change in Lake Kasumigaura using a sensitive in situ fluorometric sensor;Asai;Anal. Lett.,2001

4. Eutrophication and algal blooms in channel type reservoirs: a novel enclosure experiment by changing light intensity;Cao;J. Environ. Sci.,2011

5. A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes;Cao;Remote Sens. Environ.,2020

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