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Access type: UnknownAuthor: search.filters.author.Vanishkorn, Buddhaporn

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    A One-dimensional Salinity Measurement Model in the Chao Phraya River with the Chao Phraya Barrage Dam Using a Shooting Method
    (2023-03-01)
    Vanishkorn, Buddhaporn
    ;
    Pochai, Nopparat
    Sea water receding from the Gulf of Thailand causes salinity diffusion in the Chao Phraya River, Thailand, and the amount of northern water is reduced in the dry season. It has an impact on people, particularly the generation of tap water. The Samlae raw water pumping station is the major pumping station. The Ban Krachaeng subdistrict is located in Mueang district, Pathum Thani province, and is affected by saltwater intrusion, which causes the salinity level to exceed the recommended threshold. In order for the Metropolitan Waterworks Authority (MWA)'s water supply system to achieve the standard, the salinity index at the Samlae raw water pumping station is controlled to not exceed the surveillance threshold of 0.25 g/l in this research. A barrage dam consists of a number of large gates that can be opened or closed to control the amount of fresh water passing through. There is a Chao Phraya barrage dam which is across the Chao Phraya River at Chai Nat, the northern part of the focused area. The objective of this research is to demonstrate a onedimensional steady-state salinity measurement model in a river with a barrage dam. Irrigation is done in rivers with dams using the shooting method to estimate the solution. The results obtained from simulating simulated salinity measurements from Phra-Nakhon Tai Power Plant Station to Samlae Station demonstrated that the shooting method can be used to accurately estimate the solution. Freshwater flow velocity and salinity dissolving efficiency were discovered to be the most important elements controlling salinity levels. The suggested salinity control approach may help to regulate the salinity level until it reaches a normal level.
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    The Analysis of Mobility Patterns during the COVID-19 Pandemic in Thailand Using Time Series Clustering
    (2023-01-01)
    Supanich, Weeriya
    ;
    Kulkarineetham, Suwanee
    ;
    Vanishkorn, Buddhaporn
    The COVID-19 pandemic has affected the lives, health, economics, and travel of all nations, including Thailand. The purpose of this study is to investigate human mobility patterns during the pandemic. We opted to use the public transportation data from January 1st, 2020 until September 28th, 2022 collected from the Ministry of Transport, Thailand as a data source. We conducted a time series study on trend and seasonality patterns, as well as clustering analysis. It can be concluded that public buses and Bangkok electric trains, nationwide state trains and domestic air travel are the two pairs of public transportation with the most similar usage patterns. Moreover, the majority of personal car travel patterns are quite similar to public buses and Bangkok electric trains during some periods.
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    Crash Severity Classification Prediction and Factors Affecting Analysis of Highway Accidents
    (2022-01-01)
    Vanishkorn, Buddhaporn
    ;
    Supanich, Weeriya
    Every day 3,700 people died in road crashes and many more suffer serious injuries. Road traffic collisions are not accidents; they are things that can be avoided. This paper's objective was to develop a crash severity classifier based on previous road accident open data from the Ministry of Transport, Thailand. The confusion matrix was used as performance evaluation. The results found that the Gradient Boosting classifier outperforms other models. In addition, the identification of factors affecting crash severity is analyzed using the Shapley additive explanations (SHAP). The output revealed that features that contribute to a positive impact on more fatal accident severity are the number of trailers involved, tollway collisions, and overturn crashes. Whereas, the number of motorcycles associated, night-time collisions, and rear-end crashes gave a negative impact on the severity which led to lower injuries.