Article Per Year (5 Year)
p-Index From 2021 - 2026
0.817
P-Index
This Author published in this journals
All Journal Emerging Science Journal
Jaroensutasinee, Krisanadej
Unknown Affiliation
Environmental Science

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : Emerging Science Journal

 1 
Government Policy Influence on Land Use and Land Cover Changes: A 30-Year Analysis Rattanarat, Jantira; Jaroensutasinee, Krisanadej; Jaroensutasinee, Mullica; Sparrow, Elena B.
Emerging Science Journal Vol 8, No 5 (2024): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-05-06

Abstract

This study investigated land use and land cover (LULC) patterns and changes in the Bandon Bay area of Thailand from 1991 to 2021 using satellite imagery, the first comprehensive effort to assess historical LULC trends over the past 30 years and forecast future LULC scenarios using the CA-Markov model for 2031, 2041, and 2051. Results showed the predominant LULC during 1991-2001 was the abandoned paddy fields, and during 2006-2021 was the oil palm plantations. During 1991-2001, the abandoned paddy fields changed significantly, with a net gain of 59.28 km2. From 2001-2011 and 2011-2021, the oil palm plantations experienced the most crucial change, with a net gain of 292.94 km2 and 70.06 km2. In 2031, 2041, and 2051, the LULC was predicted to be oil palms, shrimp farms, mangroves, and urban and built-up lands. The LULC changes were consistent with the government policies implemented and indicated government policy as a driving force in LULC dynamics on Bandon Bay area forestry, aquaculture, and agriculture, particularly on oil palm cultivation. Government management and regulation on land use is crucial for reducing the expansion of agricultural areas, especially oil palm plantations and aquaculture areas, to mitigate negative impacts on the Bandon Bay ecosystem. Doi: 10.28991/ESJ-2024-08-05-06 Full Text: PDF
PM2.5 IoT Sensor Calibration and Implementation Issues Including Machine Learning Srisang, Wacharapong; Jaroensutasinee, Krisanadej; Jaroensutasinee, Mullica; Khongthong, Chonthicha; Piamonte, John Rex P.; Sparrow, Elena B.
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-08

Abstract

Affordable IoT PM2.5 sensors, enabled by the Internet of Things, offer new ways to monitor air quality. However, concerns exist about their data accuracy. This study aimed (1) to investigate the low-cost PM sensor's performance under various outdoor ambient circumstances and (2) to evaluate seven calibration methods, which include decision trees, gradient-boosted trees, linear regression, nearest neighbors, neural networks, random forests, and the Gaussian Process. The Davis AirLink was used as a reference to compare the Plantower PMS3003 sensor's performance. The data from the Plantower PMS3003 sensor were then compared to the Davis AirLink values using calibration curves created by machine learning algorithms. Calibration curves were generated using machine learning algorithms trained on sensor measurements collected in two Thai cities (Nakhon Si Thammarat and Phuket). Our results show that all machine learning methods outperformed traditional linear regression, with decision trees and neural networks demonstrating the most significant improvement. This research highlights the need for sensor calibration and the limitations of current calibration methods and paves the way for advancements in cloud-based calibration and machine learning for improved data accuracy in IoT PM2.5sensor technology. Doi: 10.28991/ESJ-2024-08-06-08 Full Text: PDF
Driving Mangrove Recovery: Community Engagement and Socio-Economic Shifts in Aquaculture Areas Rattanarat, Jantira; Jaroensutasinee, Krisanadej; Jaroensutasinee, Mullica; Sparrow, Elena B.
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-09

Abstract

Land-use change and recovery patterns of mangroves in the Tha Sak subdistrict, Nakhon Si Thammarat, Thailand, were examined utilizing multi-temporal Landsat images and socio-economic data from 1988 to 2023. Land use was classified through visual interpretation, and potential changes were predicted using a Markov chain model. The results showed a significant expansion of mangrove forests (1.11 km² to 9.10 km²), indicating a clear recovery. At the same time, the aquaculture area decreased drastically (from 25.69 km² to 8.79 km²), indicating a significant change in land use. The recovery of mangroves is primarily attributed to the cessation of aquaculture and the active involvement of the Tha Sak subdistrict's Small-Scale Fishermen Group, highlighting the success of community-based restoration. This study provides evidence of the critical role local communities play in bringing about positive environmental change and enabling Sustainable Development Goals (SDGs) 15: Life on Land from ecosystem restoration, SDG 14: Life Below Water for conservation of coastal areas, and SDG 11: Sustainable Cities and Communities for increasing community resilience. Involving local communities in mangrove restoration and preservation is key to long-term sustainability.
Multi-Country GHG Emissions Forecasting by Sector Using a GCN-LSTM Model Tonny, Babey Dimla; Jaroensutasinee, Krisanadej; Jaroensutasinee, Mullica; Sparrow, Elena B.
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-03

Abstract

This study developed a novel hybrid Graph Convolutional Network–Long Short-Term Memory (GCN–LSTM) model to forecast greenhouse gas (GHG) emissions across multiple country sectors, aiming to enhance climate policy. We analyzed 52 years (1970–2022) of GHG emissions data (CO₂, CH₄, N₂O, F-Gases) from 163 countries and eight sectors (Agriculture, Buildings, Fuel Exploitation, Industrial Combustion, Power Industry, Processes, Transport, Waste), sourced from the EDGAR v8 database. The GCN adjacency matrix captures spatial relationships on a weighted sum of Haversine distance and cosine similarity, while the LSTM models temporal dynamics. Data preprocessing includes min-max scaling and outlier handling with Interquartile Range capping. The model was trained on 70% of the data, validated on 15%, and tested on 15%, using Mean Squared Error (MSE) loss and the Adam optimizer. The performance was evaluated with Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). The GCN–LSTM model outperformed baseline models (ARIMA, Simple LSTM, Stacked LSTM), achieving the lowest MAE (0.0207 in Waste) and highest R² (0.9756 in Waste). Model interpretability highlighted strong regional connections, such as Thailand–Cambodia in the Waste sector, suggesting that spatial and temporal dependencies offer superior forecasting accuracy, informing targeted climate action.
Co-Authors Jaroensutasinee, Mullica Khongthong, Chonthicha Piamonte, John Rex P. Rattanarat, Jantira Sparrow, Elena B. Srisang, Wacharapong Tonny, Babey Dimla