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 42

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The relationship between the climatic indices and the rainfall fluctuation in the lower central plain of Thailand
(2019-02-01)
Taweesin, Korrakoch
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Seeboonruang, Uma
Global climate changes are revealing the interconnections between natural conditions, natural resources, and regional climate variability that may affect the rain fluctuation. Rainfall plays an important role in the process of hydrology. This research presents an analysis of rainfall in the lower central plain of Thailand and the climate variability/oceanographic events in the wider geographical region, including the El Niño/Southern Oscillation (ENSO), Asian Summer Monsoon (ASM), and Indian Ocean Dipole (IOD). Data from 1980-2010 and 2011-2014 were collected for calibration and verification. Next, the frequency domains, spectra, and wavelet transforms were analyzed, together with the climate index and rainfall. The results revealed that rainfall occurs in seasons, yearly cycles, and off-seasons. The behavior of ASMs, for example, Indian Summer Monsoon Index (IMI) and Western North Pacific Monsoon Index (WNPMI), is the most similar to that of rainfall events, while the similarity of the other indices to rainfall events is not so strong. Cross-correlation analysis showed that there were delays between the climate indices and rainfall, so that multiple linear regression with lag time is required for further analysis. The results illustrate that the cross-correlation coefficients of IMI and WNPMI with rainfall are both approximately 0.6. The multiple regression with lag time shows that the average multiple coefficient correlation (R) is 0.64. The indicator of the summer monsoon index value is WNPMI, which is the most influential factor for rainfall. Finally, the proposed equations, based on the cross-correlation and multiple-linear regression with lag time techniques, can be used to predict precipitation and be applied to the development of rainfall forecasting in the future.
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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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Predicting sheet and rill erosion of Shihmen reservoir watershed in Taiwan using machine learning
(2019-07-01)
Nguyen, Kieu Anh
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Chen, Walter
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Lin, Bor Shiun
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Seeboonruang, Uma
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Thomas, Kent
Shihmen Reservoir watershed is vital to the water supply in Northern Taiwan but the reservoir has been heavily impacted by sedimentation and soil erosion since 1964. The purpose of this study was to explore the capability of machine learning algorithms, such as decision tree and random forest, to predict soil erosion (sheet and rill erosion) depths in the Shihmen reservoir watershed. The accuracy of the models was evaluated using the RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), and R<sup>2</sup>. Moreover, the models were verified against the multiple regression analysis, which is commonly used in statistical analysis. The predictors of these models were 14 environmental factors which influence soil erosion, whereas the target was 550 erosion pins installed at 55 locations (on 55 slopes) and monitored over a period of approximately three years. The data sets for the models were separated into 70% for the training data and 30% for the testing data, using the simple random sampling and stratified random sampling methods. The results show that the random forest algorithm performed the best of the three methods. Moreover, the stratified random sampling method had better results among the two sampling methods, as anticipated. The average error (RMSE relative to 1:1 line) of the stratified random sampling method of the random forest algorithm is 0.93 mm/yr in the training data and 1.75 mm/yr in the testing data, respectively. Finally, the random forest algorithm predicted that type of slope, slope direction, and sub-watershed are the three most important factors of the 14 environmental factors collected and used in this study for splits in the trees and thus they are the three most important factors affecting the depth of sheet and rill erosion in the Shihmen Reservoir watershed. The results of this study can be employed by decision-makers to improve soil conservation planning and watershed remediation.
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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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Wavelet relationship between climate variability and deep groundwater fluctuation in Thailand’s Central Plains
(2018-02-01)
Seeboonruang, Uma
Groundwater is constantly under direct and indirect pressures from the anthropogenic effects, long-term climate change, and climate variability. This research investigates the association, in the time-frequency domain, between the groundwater fluctuations in Thailand’s Lower Chao Phraya Basin and specific climate variability forces: the El Nino/Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the Asian Summer Monsoons (ASM). The analysis was carried out using the wavelet method and the findings presented in the form of the complete, global, and local wavelet spectrums. In addition, the Pearson correlation was utilized to establish the linkages between the groundwater and the climate variability forces. The results indicated that the deep groundwater signals of the Lower Chao Phraya Basin were linked to the ENSO, IOD and ASM with the absolute correlation coefficients in excess of 0.5. Moreover, the recent climatic indices exerted greater influence on the groundwater than in the past, given the former’s correlation coefficients of 0.9 on average. By comparison, the deep groundwater was strongly associated with the recent ENSO and ASM but weakly linked to the IOD, with the absolute coefficients of around 0.5. The findings revealed the resilience of the deep groundwater under such high frequency signal conditions as the seasonal and tidal oscillations. Moreover, the results showed that the groundwater could be an alternative source of water supply during periods of droughts in the region.
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An empirical decomposition of deep groundwater time series and possible link to climate variability
(2014-01-01)
Seeboonruang, U.
Deep groundwater data reflects hydrological processes, climate change and variability, as well as any anthropogenic influence. Decomposition of deep groundwater signal examines the history of the groundwater region. Detrending is a vital step in decomposition of groundwater time series because it is expected to remove anthropogenic effects and long-term cyclic patterns. Eight detrending methods were applied to long-term groundwater records monitored in the Lower Chao Phraya basin in Thailand. Detrended residuals and subsequently periodograms of the residuals were computed by applying the Fourier series analysis. The result from this study indicates that the 5<sup>th</sup> order polynomial interpolation provides the trendlines that significantly relate to the groundwater withdrawal background. The detrended residual function is imbedded with two major cyclic patterns, which can be the result from global climate variability, e.g. Indian Ocean Dipole and the El Niño Southern Oscillation. The magnitude of deep groundwater dynamics as the result from the anthropogenic effect, is much greater than that of the climate variability in this region. In addition, this study demonstrates that caution must be exercised when fitting groundwater time series with different detrending techniques can yield mistaken cyclic patterns and may infer to different climate variability phenomenon. © 2014 Global NEST Printed in Greece. All rights reserved.
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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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Relationship between groundwater properties and soil salinity at the Lower Nam Kam River Basin in Thailand
(2013-07-01)
Seeboonruang, Uma
The Lower Nam Kam River Basin lies in the vicinity of the Mekong River and is located in the eastern section of the Nakhon Panom Province in northeastern Thailand. Drought, particularly in the winter and summer seasons, is the main environmental challenge in this area. In addition, soil becomes saline and groundwater is brackish in some locations. This problem worsens the drought crisis in the area. Groundwater is known to closely relate to the soil salinity distribution. To successfully manage highly saline areas, saline groundwater and soil properties must be evaluated together. Therefore, the main objective was to study the shallow groundwater physical and chemical properties in conjunction with surface soil salinity. Soil samples were collected and measured for physical and chemical properties. Shallow groundwater was measured for depth from ground surface and sampled from the sites in the study area. The water samples were measured for pH, total dissolved solids, electrical conductivity, and salinity. Results were interpolated and displayed via a geographic information system and further analyzed by simple linear regressions between surface soil salinity and the other factors. The results show that the topsoil contaminated with salinity is typically situated in relatively low areas with shallow groundwater levels and low head gradient of groundwater. This is due to the characteristics of the soil profile and groundwater depth. © 2012 Springer-Verlag Berlin Heidelberg.
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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.

Seeboonruang, Uma

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