Comparative multi-algorithm AI framework for real-time carbon emission optimization in a medium-scale irrigation project in Thailand

Bibliographic Data

ID11367688
AuthorsChiratthawat Mueangphaen (Khon Kaen University), Wuttipong Kusonkhum (0000-0002-0456-5397, Khon Kaen University, corresponding author), Kittiwet Kuntiyawichai (0000-0002-4897-6630, Khon Kaen University), Tanyada Pannachet (0009-0000-8219-6585, Khon Kaen University), Ratamanee Nuntasarn (Khon Kaen University), Maetee Boonpichetvong (0009-0007-6192-8688, Khon Kaen University)
Year2026
Volume118
Pages108276
Publication date2026-04-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironmental Impact Assessment Review (JOURNAL)
Journal identifiersISSN: 0195-9255 • E-ISSN: 1873-6432
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.eiar.2025.108276
OpenAlexW4416658324
LanguageEN
References cited33

This study develops and validates a real-time carbon monitoring framework that integrates automated meteorological data with operational construction logs to enhance environmental impact assessment (EIA) methodology for tropical infrastructure projects. Leveraging 2332 h of synchronized climate–operational data from a reinforced-concrete sluice gate project in Thailand, the framework addresses critical gaps in carbon governance for medium-scale irrigation construction, a sector underrepresented in current EIA literature yet vital to climate adaptation in emerging economies. Three machine learning models (artificial neural networks, random forest, and extreme gradient boosting) were used to capture complex emission patterns. Under the tested monsoon scenarios, all models achieved high predictive fidelity (R 2 = 0.997–0.998) with low normalized errors (mean absolute scaled error < 0.05). The framework demonstrated 5 %–30 % potential emission reduction across operational–climatic scenarios, confirming its robustness for tropical infrastructure management and showing robust cross-algorithm consensus. Feature importance analysis highlighted embodied carbon in concrete (59.7 %) and steel reinforcement (24.8 %) as the primary emission sources, while environmental conditions emerged as influential factors. Furthermore, lower embodied‐carbon levels were observed during operational periods under ambient temperatures below 28 °C. The framework facilitates climate-responsive construction planning via real-time emission forecasting, scenario testing, and adaptive resource optimization. A three-tier implementation strategy encompassing climate-adaptive material scheduling, climate-responsive planning, and scenario-based emission testing demonstrates 5 %–30 % reduction potential while accommodating the technological constraints of tropical developing economies. This approach advances EIA methodology from retrospective assessment toward predictive, climate-responsive decision support, offering a scalable framework for integrating real-time carbon management into digital EIA platforms and aiding national net-zero infrastructure goals. • Comparative AI (XGBoost/ANN/RF) for site-level CO2e in tropical irrigation. • 2332 hourly records; R 2 ≈ 0.99 for real-time CO2e prediction from field logs. • Concrete 59.7 % + steel 24.8 % dominate; prioritize material actions. • Periods <28 °C associated with lower concrete/steel-related emissions. • Scenario tests indicate ∼14 % CO2e reduction via optimized practices

Adaptive Management · Carbon capture and storage (timeline · Climate change · Greenhouse gas · Robustness (evolution · Scalability · Testbed · Environmental and Social Impact Assessments · Environmental Impact and Sustainability · Urban Stormwater Management Solutions

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