Comparing the performance of machine learning algorithms for remote and in situ estimations of chlorophyll‐a content: A case study in the Tri An Reservoir, Vietnam-Reference-Cited by-同舟云学术

Comparing the performance of machine learning algorithms for remote and in situ estimations of chlorophyll‐a content: A case study in the Tri An Reservoir, Vietnam

Published:2021-10-04 Issue:12 Volume:93 Page:2941-2957
ISSN:1061-4303
Container-title:Water Environment Research
language:en
Short-container-title:Water Environment Research

Author:

Nguyen Hao Quang¹^ORCID,Ha Nam Thang²^ORCID,Nguyen‐Ngoc Lam³^ORCID,Pham Thanh Luu⁴⁵^ORCID

Affiliation:

1. Graduate School of Systems and Information Engineering University of Tsukuba Tsukuba Japan

2. Faculty of Fisheries University of Agriculture and Forestry, Hue University Hue Vietnam

3. Institute of Oceanography Vietnam Academy of Science and Technology (VAST) Nha Trang Viet Nam

4. Ho Chi Minh City University of Technology (HUTECH) Ho Chi Minh City Vietnam

5. Institute of Tropical Biology Vietnam Academy of Science and Technology (VAST) Ho Chi Minh City Vietnam

Funder

Vietnam Academy of Science and Technology

Publisher

Wiley

Subject

Water Science and Technology,Ecological Modeling,Waste Management and Disposal,Pollution,Environmental Chemistry

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/wer.1643

Reference62 articles.

1. Retrieval of Chlorophyll a from Sentinel-2 MSI Data for the European Union Water Framework Directive Reporting Purposes

2. Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model

3. Robust Estimation of Water Chlorophyll Concentrations With Gaussian Process Regression and IOWA Aggregation Operators

4. Random forest in remote sensing: A review of applications and future directions

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