Laokhongthavorn, Laemthong

Preferred name

Alternative Name

Laokhongthavorn, L.

Laokhongthavorn, Leamthong

Main Affiliation

School of Engineering

laemthong.la@kmitl.ac.th

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Sustainable Reduction of Soil Permeability through Microbial Bio-Clogging
(2026-01-01)
Kamchoom, Viroon
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Chaisarn, Sumetha
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Khattiwong, Thiti
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Laokhongthavorn, Laemthong
Microbially induced bio-clogging presents a promising, sustainable alternative to conventional soil improvmeent methods for mitigating seepage in geotechnical applications. Despite its potential, uncertainties remain regarding the influence of bacterial concentration, culture medium application, and associated setting times under field-like conditions—factors which are critical to the effective deployment of this technology in practice. This study investigates the impact of bacterial bio-clogging on the hydraulic behaviour of coarse-grained soils, with particular emphasis on the system's performance during and following the cessation of culture medium supply. Laboratory experiments were conducted to assess the mechanisms of permeability reduction resulting from microbial colonisation and extracellular polymeric substance (EPS) production. Results demonstrate that bacterial adhesion and subsequent EPS accumulation lead to the progressive clogging of soil pores, causing a marked decline in saturated permeability. The observed reductions in permeability are comparable to those produced by traditional methods such as cement and bentonite grouting. This highlights the durability of the biofilm matrix and its ability to maintain hydraulic resistance in the absence of continued nutrient input. These findings contribute valuable insight into the viability of bio-clogging as a ground improvement strategy. By elucidating the relationship between bacterial activity, EPS production, and soil pore occlusion, this research advances the practical understanding required to optimise bio-mediated techniques for field-scale applications in sustainable geotechnical engineering.
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Comprehensive Evaluation of Vertical Sub-Surface Flow Constructed Wetlands with Aquatic Plants on Water Quality of Raw and Phyto-Remediated Poultry-Aquaculture Wastewater: A Principal Component Analysis
(2026-06-01)
Akadiri, Shadrach A.
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Dada, Pius O.O.
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Badejo, Adekunle A.
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Adeosun, Olayemi J.
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Faloye, Oluwaseun T.
This study investigated the efficiency of macrophyte-based phytoremediation systems using Phragmites karka and Typha latifolia for the treatment of poultry–aquaculture wastewater and its suitability for irrigation reuse. Physicochemical parameters, heavy metals, and water quality indices were analysed using correlation analysis and Principal Component Analysis (PCA). Strong positive correlations were observed among turbidity, nutrients, biochemical oxygen demand (BOD<inf>5</inf>), and chemical oxygen demand (COD), while dissolved oxygen (DO) showed significant negative relationships, indicating organic pollution-driven oxygen depletion. Heavy metals exhibited strong intercorrelations, suggesting common anthropogenic sources and similar removal pathways. PCA results revealed that the first three principal components (PCs) explained over 95% of the total variance, with positive values recorded from the first PC highlighting organic load, nutrient enrichment, and metal interactions as dominant factors controlling wastewater quality. The negative values of factor loadings obtained in the second and third PCs confirmed the roles of sedimentation, adsorption, microbial activity, and plant uptake in pollutant removal. Water Quality Index (WQI) values decreased drastically from highly polluted levels (>3000) in raw wastewater to <1.0 after 21 days of treatment, indicating excellent water quality. Sodium Absorption Ratio (SAR) also declined significantly, confirming a low sodicity risk. Both macrophytes demonstrated high treatment efficiency, with Typha latifolia showing slightly improved sodium reduction. Overall, the study highlights macrophyte-based systems as sustainable, cost-effective solutions for wastewater treatment and safe agricultural reuse.
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Performance evaluation of machine learning algorithms for estimating reference evapotranspiration based on NASA POWER weather data: a case study in Nigeria
(2026-01-01)
Faloye, Oluwaseun Temitope
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Awotoye, Grace
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Eludire, Oluwadamilare Oluwasegun
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Olaleye, Oluwatobi Solomon
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Oluwadare, Ayoola Olamitomi
The Penman–Monteith (PM) method is recognized as the globally accepted approach for estimating reference evapotranspiration (ETo). However, its use is constrained in areas with limited or unavailable data. Predicting ETo using multiple support vector machine (SVM) kernels and decision tree (DT) ensembles with NASA POWER data is innovative, as previous SVM-based ETo prediction studies have relied primarily on linear kernels. This study aims to evaluate the performance of different machine learning (ML) models, specifically SVM and DT and their ensembles, using NASA Power data as input. For this purpose, ML models were trained using average values of the monthly climatic data (maximum and minimum air temperatures, relative humidity, and wind speed) from NASA POWER. ETo was used as the output variable and was calculated from ground-observed data using the PM method. The developed ML models underwent training and validation to determine ETo in areas with different weather conditions in Nigeria: Kano—dry weather, Onne—wet weather, and Ibadan—moderate weather. Thirty and 70 % of the data were used during training and validation, respectively. The SVMs used in this study include linear SVM, quadratic SVM, cubic SVM, fine Gaussian (FG) SVM, medium Gaussian (MG) SVM, and coarse Gaussian SVM. The decision trees include fine, medium, and coarse trees, along with their ensembles: bagged and boosted trees. The model performance was evaluated using various error metrics. The FG SVM model exhibited the most accurate and precise estimation of ETo, with root mean square error (RMSE) values of 0.38 and 0.599 mm during the training and testing phases, respectively. Additionally, the coefficient of determination (r<sup>2</sup>) was good, with values of 0.87 and 0.72 during training and validation. The FG SVM outperformed all other models across all study locations, demonstrating its robustness in predicting ETo despite the contrasting weather conditions. Overall, this study revealed that the integration of data from NASA POWER with FG SVM accurately estimated reference evapotranspiration, which is important for effective water resource management in areas where ground climatic data is unavailable.

Laokhongthavorn, Laemthong

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