Original Research

Published on 17 Jul 2023

Assessment of geothermal resource potential in Changbaishan utilizing high-precision gravity-based man-machine interactive inversion technology

in Data-driven Climate Sciences

Zhi-He Xu
Ji-Yi Jiang
Guan-Wen Gu
Zhen-Jun Sun
Xuan-Kai Jiao
Xing-Guo Niu
Qin Yu

Frontiers in Big Data

doi 10.3389/fdata.2023.1139918

468 views

Original Research

Published on 24 Feb 2023

Machine learning-based ozone and PM2.5 forecasting: Application to multiple AQS sites in the Pacific Northwest

in Data-driven Climate Sciences

Kai Fan
Ranil Dhammapala
Kyle Harrington
Brian Lamb
Yunha Lee

Frontiers in Big Data

doi 10.3389/fdata.2023.1124148

1,176 views
1 citations

Original Research

Published on 09 Jan 2023

Changing features of the Northern Hemisphere 500-hPa circumpolar vortex

in Data-driven Climate Sciences

Nazla Bushra
Robert V. Rohli
Chunyan Li
Paul W. Miller
Rubayet Bin Mostafiz

Frontiers in Big Data

doi 10.3389/fdata.2022.1009158

999 views

Original Research

Published on 09 Jan 2023

Flood risk assessment for residences at the neighborhood scale by owner/occupant type and first-floor height

in Data-driven Climate Sciences

Ayat Al Assi
Rubayet Bin Mostafiz
Carol J. Friedland
Md Adilur Rahim
Robert V. Rohli

Frontiers in Big Data

doi 10.3389/fdata.2022.997447

1,680 views
6 citations

Original Research

Published on 12 Dec 2022

A data-driven spatial approach to characterize the flood hazard

in Data-driven Climate Sciences

Rubayet Bin Mostafiz
Md Adilur Rahim
Carol J. Friedland
Robert V. Rohli
Nazla Bushra
Fatemeh Orooji

Frontiers in Big Data

doi 10.3389/fdata.2022.1022900

1,530 views
5 citations

Brief Research Report

Published on 09 Sep 2022

Observation-based assessment of secondary water effects on seasonal vegetation decay across Africa

in Data-driven Climate Sciences

Çağlar Küçük
Sujan Koirala
Nuno Carvalhais
Diego G. Miralles
Markus Reichstein
Martin Jung

Frontiers in Big Data

doi 10.3389/fdata.2022.967477

1,275 views

Original Research

Published on 20 May 2022

Application of a Machine Learning Algorithm in Generating an Evapotranspiration Data Product From Coupled Thermal Infrared and Microwave Satellite Observations

in Data-driven Climate Sciences

Li Fang
Xiwu Zhan
Satya Kalluri
Peng Yu
Chris Hain
Martha Anderson
Istvan Laszlo

Frontiers in Big Data

doi 10.3389/fdata.2022.768676

2,052 views

Editorial

Published on 05 May 2022

Editorial: Statistical Learning for Predicting Air Quality

in Data-driven Climate Sciences

Yves Philippe Rybarczyk
Rasa Zalakeviciute

Frontiers in Big Data

doi 10.3389/fdata.2022.898643

1,301 views

Original Research

Published on 04 Apr 2022

Deep Learning Approach for Assessing Air Quality During COVID-19 Lockdown in Quito

in Data-driven Climate Sciences

Phuong N. Chau
Rasa Zalakeviciute
Ilias Thomas
Yves Rybarczyk

Frontiers in Big Data

doi 10.3389/fdata.2022.842455

5,069 views
12 citations

Original Research

Published on 25 Mar 2022

Data-Driven Framework for Understanding and Predicting Air Quality in Urban Areas

in Data-driven Climate Sciences

Lakshmi Babu Saheer
Ajay Bhasy
Mahdi Maktabdar
Javad Zarrin

Frontiers in Big Data

doi 10.3389/fdata.2022.822573

3,553 views
6 citations

Brief Research Report

Published on 10 Mar 2022

Air Quality Forecast by Statistical Methods: Application to Portugal and Macao

in Data-driven Climate Sciences

Luísa Mendes
Joana Monjardino
Francisco Ferreira

Frontiers in Big Data

doi 10.3389/fdata.2022.826517

3,623 views
8 citations

Original Research

Published on 15 Feb 2022

Daily Spatial Complete Soil Moisture Mapping Over Southeast China Using CYGNSS and MODIS Data

in Data-driven Climate Sciences

Ting Yang
Zhigang Sun
Jundong Wang
Sen Li

Frontiers in Big Data

doi 10.3389/fdata.2021.777336

1,870 views
1 citations

Original Research

Published on 10 Feb 2022

Development of a Machine Learning Approach for Local-Scale Ozone Forecasting: Application to Kennewick, WA

in Data-driven Climate Sciences

Kai Fan
Ranil Dhammapala
Kyle Harrington
Ryan Lamastro
Brian Lamb
Yunha Lee

Frontiers in Big Data

doi 10.3389/fdata.2022.781309

2,053 views
4 citations

Original Research

Published on 04 Feb 2022

Runoff Forecasting Using Machine-Learning Methods: Case Study in the Middle Reaches of Xijiang River

in Data-driven Climate Sciences

Lu Xiao
Ming Zhong
Dawei Zha

Frontiers in Big Data

doi 10.3389/fdata.2021.752406

1,976 views
5 citations

Original Research

Published on 21 Dec 2021

DroughtCast: A Machine Learning Forecast of the United States Drought Monitor

in Data-driven Climate Sciences

Colin Brust
John S. Kimball
Marco P. Maneta
Kelsey Jencso
Rolf H. Reichle

Frontiers in Big Data

doi 10.3389/fdata.2021.773478

6,393 views
10 citations

Original Research

Published on 25 Oct 2021

Automated Analysis of the US Drought Monitor Maps With Machine Learning and Multiple Drought Indicators

in Data-driven Climate Sciences

Pouyan Hatami Bahman Beiglou
Lifeng Luo
Pang-Ning Tan
Lisi Pei

Frontiers in Big Data

doi 10.3389/fdata.2021.750536

2,160 views
1 citations

Navigation group

Articles

Assessment of geothermal resource potential in Changbaishan utilizing high-precision gravity-based man-machine interactive inversion technology

Machine learning-based ozone and PM2.5 forecasting: Application to multiple AQS sites in the Pacific Northwest

Changing features of the Northern Hemisphere 500-hPa circumpolar vortex

Flood risk assessment for residences at the neighborhood scale by owner/occupant type and first-floor height

A data-driven spatial approach to characterize the flood hazard

Observation-based assessment of secondary water effects on seasonal vegetation decay across Africa

Application of a Machine Learning Algorithm in Generating an Evapotranspiration Data Product From Coupled Thermal Infrared and Microwave Satellite Observations

Editorial: Statistical Learning for Predicting Air Quality

Deep Learning Approach for Assessing Air Quality During COVID-19 Lockdown in Quito

Data-Driven Framework for Understanding and Predicting Air Quality in Urban Areas

Air Quality Forecast by Statistical Methods: Application to Portugal and Macao

Daily Spatial Complete Soil Moisture Mapping Over Southeast China Using CYGNSS and MODIS Data

Development of a Machine Learning Approach for Local-Scale Ozone Forecasting: Application to Kennewick, WA

Runoff Forecasting Using Machine-Learning Methods: Case Study in the Middle Reaches of Xijiang River

DroughtCast: A Machine Learning Forecast of the United States Drought Monitor

Automated Analysis of the US Drought Monitor Maps With Machine Learning and Multiple Drought Indicators