Seeboonruang, Uma

Preferred name

Seeboonruang, Uma

Alternative Name

Seeboonruang, U.

Main Affiliation

School of Engineering

uma.se@kmitl.ac.th

Now showing 1 - 10 of 24

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Evaluation of the SEdiment Delivery Distributed (SEDD) model in the Shihmen Reservoir watershed
(2020-08-01)
Thomas, Kent
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Chen, Walter
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Lin, Bor Shiun
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Seeboonruang, Uma
The sediment delivery ratio (SDR) connects the weight of sediments eroded and trans-ported from slopes of a watershed to the weight that eventually enters streams and rivers ending at the watershed outlet. For watershed management agencies, the estimation of annual sediment yield (SY) and the sediment delivery has been a top priority due to the influence that sedimentation has on the holding capacity of reservoirs and the annual economic cost of sediment-related disasters. This study establishes the SEdiment Delivery Distributed (SEDD) model for the Shihmen Reservoir watershed using watershed-wide SDRw and determines the geospatial distribution of individual SDRi and SY in its sub-watersheds. Furthermore, this research considers the statistical and geospa-tial distribution of SDRi across the two discretizations of sub-watersheds in the study area. It shows the probability density function (PDF) of the SDRi. The watershed-specific coefficient (β) of SDRi is 0.00515 for the Shihmen Reservoir watershed using the recursive method. The SY mean of the entire watershed was determined to be 42.08 t/ha/year. Moreover, maps of the mean SY by 25 and 93 sub-watersheds were proposed for watershed prioritization for future research and remedial works. The outcomes of this study can ameliorate future watershed remediation planning and sediment control by the implementation of geospatial SDRw/SDRi and the inclusion of the sub-watershed prioritiza-tion in decision-making. Finally, it is essential to note that the sediment yield modeling can be im-proved by increased on-site validation and the use of aerial photogrammetry to deliver more up-dated data to better understand the field situations.
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Design and Implementation of a Hybrid Real-Time Salinity Intrusion Monitoring and Early Warning System for Bang Kachao, Thailand
(2025-07-01)
Seeboonruang, Uma
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Tanachaichoksirikun, Pinit
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Anuwongpinit, Thanavit
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Sirikaew, Uba
Salinity intrusion is a growing threat to freshwater resources, particularly in low-lying coastal and estuarine regions, necessitating the development of effective early warning systems (EWS) to support timely mitigation. Although various water quality monitoring technologies exist, many face challenges related to long-term sustainability, ongoing maintenance, and accessibility for local users. This study introduces a novel hybrid real-time salinity intrusion early warning system that uniquely integrates fixed and portable monitoring technologies with strong community participation—an approach not yet widely applied in comparable urban-adjacent delta regions. Unlike traditional systems, this model emphasizes local ownership, flexible data collection, and system scalability in resource-constrained environments. This study presents a real-time salinity intrusion early warning system for Bang Kachao, Thailand, combining eight fixed monitoring stations and 20 portable salinity measurement devices. The system was developed in response to community needs, with local input guiding both station placement and the design of mobile measurement tools. By integrating fixed stations for continuous, high-resolution data collection with portable devices for flexible, on-demand monitoring, the system achieves comprehensive spatial coverage and adaptability. A core innovation lies in its emphasis on community participation, enabling villagers to actively engage in monitoring and decision-making. The use of IoT-based sensors, Remote Telemetry Units (RTUs), and cloud-based data platforms further enhances system reliability, efficiency, and accessibility. Automated alerts are issued when salinity thresholds are exceeded, supporting timely interventions. Field deployment and testing over a seven-month period confirmed the system’s effectiveness, with fixed stations achieving 90.5% accuracy and portable devices 88.7% accuracy in detecting salinity intrusions. These results underscore the feasibility and value of a hybrid, community-driven monitoring approach for protecting freshwater resources and building local resilience in vulnerable regions.
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FLOOD AND DROUGHT PRELIMINARY ASSESSMENT IN THE BANG PAKONG RIVER BASIN USING THE WEIGHED FACTOR INDEX METHOD
(2025-01-01)
Tanachaichoksirikun, Pinit
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Jiao, Jinghan
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Sirikaew, Uba
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Seeboonruang, Uma
This research is dedicated to forecasting flood and drought assessment in the Bang Pakong River Basin through a weighted factor index method. The study employed geographic information systems to prioritize and create hazard maps. The maps integrated both natural elements, for example, average annual rainfall, temperature, terrain slope, and forest area, as well as human-made elements such as land use, water body, and irrigation area. The Gumbel distribution method was used to generate the future rainfall and temperature. The study predicts future flood and drought assessment areas based on maximum average precipitation and temperature. The findings reveal a distributed spectrum of risk levels, ranging from no risk to very high risk. Currently, moderate risks of floods and droughts exist in certain areas. However, projections indicate a significant increase in flood-prone regions over 5-year, 10-year, and 15-year return periods, attributed to escalating average rainfall. Conversely, while drought-prone areas encompass approximately 27.5% of the watershed, there is a notable rise in high-risk zones alongside a decrease in moderate-risk areas due to rising temperatures. These insights underscore the imperative of proactive interventions to address the escalating threats posed by floods and droughts in the Bang Pakong River Basin.
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Impact of climate change on soil erosion in the lam phra phloeng watershed
(2020-12-01)
Sirikaew, Uba
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Seeboonruang, Uma
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Tanachaichoksirikun, Pinit
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Wattanasetpong, Jatuwat
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Chulkaivalsucharit, Virun
Soil erosion plays a vital role in reducing reservoir capacity. The Lam Phra Phloeng (LPP) dams were built for flood protection and irrigation. However, they have experienced reservoir sedimentation, and the capacity of the reservoir has decreased. The surrounding soil surface was easily eroded and transported by heavy rainfall and surface runoff to streams and eventually into the reservoir. Understanding this soil erosion and sedimentation is necessary for preventing further decline of reservoir capacity and water management. This research aims to estimate long-term average annual soil erosion and predict sediment yield in the reservoir due to climate change. The methodology is determined soil loss parameters and sediment yield using the Universal Soil Loss Equation (USLE) with the Sediment Delivery Ratio (SDR). The USLE and SDR methods differed from field data, with an average absolute error of 4.0%. The Global Climatic Model, Institute Pierre Simon Laplace-Climate Model version 5A (IPSL-CM5A-MR), with Representative Concentration Pathways (RCP) 2.6, 4.5, and 8.5, was downscaled and analyzed to forecast future rainfall in the watershed. The high intensity of rainfall contributed to higher soil erosion, in RCP 8.5. Interestingly, the high and very high-risk areas increased, but the moderate risk area declined, indicating that the moderate risk area should be a priority in land management. However, the heavy rainfall and high slope gradient led to a slight increase in the soil erosion in some areas because the land covers were evergreen and deciduous forest. The prediction of sediment yield was positively correlated with the intensity of rainfall in the central part of the watershed, because the rainfall and runoff led the sediment to the river and streams, indicating that the land cover should be managed to prevent capacity decline.
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Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements
(2021-01-01)
Nguyen, Kieu Anh
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Chen, Walter
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Lin, Bor Shiun
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Seeboonruang, Uma
Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging, (b) boosting, and (c) stacking. The bagging method in this study refers to bagged multivariate adaptive regression splines (bagged MARS) and random forest (RF), and the boosting method includes Cubist and gradient boosting machine (GBM). Finally, the stacking method is an ensemble method that uses a meta-model to combine the predictions of base models. This study used RF and GBM as the meta-models, decision tree, linear regression, artificial neural network, and support vector machine as the base models. The dataset used in this study was sampled using stratified random sampling to achieve a 70/30 split for the training and test data, and the process was repeated three times. The performance of six ensemble methods in three categories was analyzed based on the average of three attempts. It was found that GBM performed the best among the ensemble models with the lowest root-mean-square error (RMSE = 1.72 mm/year), the highest Nash-Sutcliffe efficiency (NSE = 0.54), and the highest index of agreement (d = 0.81). This result was confirmed by the spatial comparison of the absolute differences (errors) between model predictions and observations using GBM and RF in the study area. In summary, the results show that as a group, the bagging method and the boosting method performed equally well, and the stacking method was third for the erosion pin dataset considered in this study.
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Identifying Water Source Locations on a Data-Limited Small Island Using GIS and the Factor Index Method
(2026-06-01)
Seeboonruang, Uma
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Horpeancharoen, Witthawin
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Sirikaew, Uba
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Tanachaichoksirikun, Pinit
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Chulkaivalsucharit, Virun
This study presents an integrated methodological framework for identifying potential freshwater source locations on data-limited small islands by combining Geographic Information Systems (GIS), the Soil and Water Assessment Tool (SWAT), and the Factor Index Method. The analytical workflow begins with processing spatial data (topography and land use) in a GIS environment to delineate initial watersheds. These inputs are then coupled with the SWAT model to simulate key hydrological output variables, such as surface runoff and stream network geometry, without requiring extensive historical streamflow data. The simulated outputs are subsequently reintegrated into the GIS environment to derive spatial attributes for each sub-basin. Subsequently, the Factor Index Method is applied to evaluate and rank the sub-basins using five key criteria: watershed area ratio, stream length ratio, slope, land use, and location accessibility. The analysis identified eight optimal sites, with field surveys confirming that four of these locations closely aligned with natural stream networks and existing spring-fed ponds. The results demonstrate that this approach can accurately delineate hydrological features and prioritize monitoring locations without extensive field data. By validating geospatial predictions through ground-truthing, this framework reduces the need for costly and time-consuming fieldwork. It enhances planning precision, supports efficient resource allocation, and can serve as a replicable model for sustainable water resource management on other small islands with similar data constraints.
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MANAGED AQUIFER RECHARGE FEASIBILITY USING WEIGHT FACTOR INDEX METHOD IN SUPHANBURI, THAILAND
(2025-01-01)
Tanachaichoksirikun, Pinit
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Xiao, Xi
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Sirikaew, Uba
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Seeboonruang, Uma
Managed Aquifer Recharge (MAR) is a technique used to intentionally enhance groundwater recharge by directing surface water or treated wastewater into underground aquifers. MAR offers several advantages for sustainable water resources management and ecosystems, namely, groundwater replenishment, drought resilience, and flood protection. Suphanburi Thailand faced challenges related to both floods and droughts because of variable rainfall patterns and hydrological conditions. Then, MAR is a good challenge for flood control and drought protection. This research aims to develop the model and find the optimal location and recharge type for set-managed aquifer recharge in Suphanburi, Thailand. Data, such as climate, land used, hydrology, and hydrogeology were carried out and provided the optional location to construct MAR. The model was developed using the Weight Factor Index Method. The results revealed that the suitable location for MAR was in the Tha Chin River area, due to the high rainfall, which allows for the storage of large quantities of water during the rainy season in the MAR in the event of groundwater shortages. Proper recharge of the river can dilute contaminants in groundwater and improve groundwater quality. The findings provided valuable guidelines for planners, decision-makers, and hydrogeologists in designing future artificial recharge projects within a similar area, ensuring a reliable water supply and the sustainable use of groundwater over the long term. In conclusion, the integration of WFI and GIS is recognized as an effective method for limited data, and location, and reducing errors.
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Estimation of Soil Erosion and Enhancing Sediment Retention in the Lam Phra Phloeng Watershed: Insights from RUSLE and InVEST Modelling
(2025-12-01)
Seeboonruang, Uma
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Mandadi, Ranadheer
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Thammaboribal, Prapas
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Gonzales, Arlene L.
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Bharadwaz, Ganni S.V.S.A.
The increasing rate of land use change, particularly deforestation and agricultural expansion, has intensified soil degradation, leading to reduced sediment retention and accelerated soil erosion. This study aims to analyze soil erosion and sediment retention in the Lam Phra Phloeng (LPP) watershed, Thailand, using a coupled modelling approach integrating the Revised Universal Soil Loss Equation (RUSLE) and the Sediment Delivery Ratio (SDR) model from the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) suite. Six land use classes (forest, cropland, rangeland, flooded vegetation, built-up areas, and water bodies) were identified using Sentinel-2 MSI satellite data, with a Random Forest (RF) classification algorithm achieving an overall accuracy of 91.3% (Kappa coefficient = 0.89). The results indicate that forested areas exhibit the highest sediment retention, whereas croplands and rangelands experience the most significant soil loss due to erosion. The RUSLE model estimated an average annual soil loss ranging between 50 and 90 tons/ha/year, with the highest erosion rates observed in agricultural lands with steep slopes and minimal vegetation cover. The InVEST SDR model further corroborates these findings, showing that sediment retention is predominantly concentrated in densely vegetated areas, reinforcing the crucial role of natural forests in preventing soil displacement. This complementary modelling approach identifies priority areas for soil conservation practices. This study is the first study to integrate the RUSLE and InVEST models for the Lam Phra Phloeng watershed, providing a coupled assessment of erosion risk and sediment retention capacity and offering a novel and transferable framework for watershed-scale conservation planning and soil management in tropical monsoonal environments.
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PERMEABILITY OF THE DIKE 1’S MATERIALS OF KAENG KRACHAN DAM, THAILAND
(2022-02-01)
Sirikaew, Uba
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Hopeancharoen, Witthawin
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Seeboonruang, Uma
Dike 1 of Kaeng Krachan Dam located in Phetchaburi province of Thailand constructed in 1966 with a reservoir capacity of 710 million m3. This large-scale project provides more than 55 years of irrigation and flood protection. Risk evaluation of dam is needed to be performed. The calibration of engineering properties of the Dike 1 is conducted because there is no database of those properties. The existing Dike 1 cross-section is a soil model, used for calculation. Piezometric level and flow rate obtained from the dam instruments were calibrated with the hydraulic head and the flow rate was determined by the SEEP/W model. The permeability coefficient of the Dike 1 materials can be analyzed by the calibration technique. The data of the dam instruments are helpful information, and the computer program is friendly to use. The coefficient of permeability of the soil of the Dike 1 of Kaeng Krachan Dam is determined and applied to risk analysis.
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Distributions of groundwater age under climate change of thailand’s lower chao phraya basin
(2020-12-01)
Tanachaichoksirikun, Pinit
;
Seeboonruang, Uma
Groundwater is important for daily life, because it is the largest freshwater source for domestic use and industrial consumption. Sustainable groundwater depends on many parameters: climate change is one factor, which leads to floods and droughts. Distribution of groundwater age indicates groundwater velocity, recharge rate and risk assessment. We developed transient 3D mathematical models, i.e., MODFLOW and MODPATH, to measure the distributions of groundwater age, impacted by climate change (IPSL-CM5A-MR), based on representative concentration pathways, defined in terms of atmospheric CO<inf>2</inf> concentration, e.g., 2.6 to 8.5, for the periods 2020 to 2099. The distributions of groundwater age varied from 100 to 100,000 years, with the mean groundwater age ~11,000 years, generated by climate led change in recharge to and pumping from the groundwater. Interestingly, under increasing recharge scenarios, the mean age, in the groundwater age distribution, decreased slightly in the shallow aquifers, but increased in deep aquifers, indicating that the new water was in shallow aquifers. On the other hand, under decreasing recharge scenarios, groundwater age increased significantly, both shallow and deep aquifers, because the decrease in recharge caused longer residence times and lower velocity flows. However, the overall mean groundwater age gradually increased, because the groundwater mixed in both shallow and deep aquifers. Decreased recharge, in simulation, led to increased groundwater age; thus groundwater may become a nonrenewable groundwater. Nonrenewable groundwater should be carefully managed, because, if old groundwater is pumped, it cannot be restored, with a detriment to human life.

Seeboonruang, Uma

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