Using Machine Learning Algorithms With In Situ Hyperspectral Reflectance Data to Assess Comprehensive Water Quality of Urban Rivers
Author:
Affiliation:
1. School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an, China
2. Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai, China
Funder
International Cooperation in Science and Technology Innovation among governments
National Natural Science Foundation of China
Instrument Developing Project of the Chinese Academy of Sciences
Science and Technology Program of Zhongshan
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/9633014/09696251.pdf?arnumber=9696251
Reference54 articles.
1. A review of water quality index models and their use for assessing surface water quality
2. A framework for assessing the adequacy of Water Quality Index – Quantifying parameter sensitivity and uncertainties in missing values distribution
3. Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks
4. Random Forest Ensemble for River Turbidity Measurement From Space Remote Sensing Data
5. Integrating Airborne Hyperspectral, Topographic, and Soil Data for Estimating Pasture Quality Using Recursive Feature Elimination with Random Forest Regression
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