Machine Learning for Peatland Ground Water Level (GWL) Prediction via IoT System-Reference-Cited by-同舟云学术

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Machine Learning for Peatland Ground Water Level (GWL) Prediction via IoT System

Published:2024 Issue: Volume:12 Page:89585-89598
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Li Lu¹^ORCID,Sali Aduwati²^ORCID,Liew Jiun Terng²,Saleh Nur Luqman²,Ali Azizi Mohd²

Affiliation:

1. School of Microelectronics and Data Science, Anhui University of Technology, Maanshan, China

2. Department of Computer and Communication Systems Engineering, Faculty of Engineering, Wireless and Photonics Networks Research Centre of Excellence (WiPNET), Universiti Putra Malaysia, Serdang, Selangor, Malaysia

Funder

IGNITE: Interference Modeling for 5G and Fixed Satellite Service (FSS) Coexistence at mmWave With Climate Change Considerations in the Tropical Region

Net-Peat: Networked Association of Southeast Asian Nations (ASEAN) Peatland Communities for Transboundary Haze Alert

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10571915.pdf?arnumber=10571915

Reference38 articles.

1. Investigation of fire risk zones using heat–humidity time series data and vegetation

2. Tropical Peat Swamp Forests

3. Biomass Burning Emissions in Indonesia and Policy Measures – An Overview

4. A 1980–2018 global fire danger re-analysis dataset for the Canadian Fire Weather Indices

5. Peat moisture and water level relationship in a tropical peat swamp forest;Nuruddin;J. Appl. Sci.,2006

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