Pochai, Nopparat

Preferred name

Pochai, Nopparat

Alternative Name

Pochai, N.

Main Affiliation

School of Science

nopparat.po@kmitl.ac.th

Now showing 1 - 4 of 4

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A Simulation of Shoreline Evolution with a Groin Structure Using an Alternative Machine Learning Algorithm
(2025-07-01)
Manilam, Surasak
;
Pochai, Nopparat
Uneven sediment transport is a major cause of coastal erosion. Using groin structures is one method to help slow the outflow of sediment from the shoreline. Studying coastal behavior and forecasting future shoreline changes are crucial for managing and assessing the viability of remediation strategies. This research presents simulations of shoreline evolution with a single groin structure using two different methods, such as mathematical modeling and an alternative machine learning. A mathematical model is a representation of a real-world shoreline evolution phenomenon using partial differential equations. A machine learning algorithm is designed to learn patterns and relationships directly from real data. In this research, an alternative machine learning algorithm is designed to learn patterns and relationships directly from mathematical simulation data and let the machine make a decision in a situation that it has never learned before. For mathematical modeling, we introduced a one-dimensional model to predict the shoreline evolution. The initial and the boundary conditions with related parameter settings are introduced. The Saulyev finite difference method is used to obtain the approximated solution. An alternative machine learning algorithm for unexpected shoreline evolution prediction is also proposed. For alternative machine learning simulations, we identified six suitable features for the training dataset and developed an alternative K-nearest neighbor algorithm. It provides a way of predicting the evolution of the shoreline with a single groin structure. Additionally, an exact solution in an ideal scenario is used to test the precision of the simulation as well. The results show that the Saulyev technique outperforms an alternative K-nearest neighbor algorithm due to the lower root mean square error value. Both results of them are closed together. According to the research, mathematical modeling outperforms the KNN regression technique in terms of computational effectiveness during time periods of 0.5, 1, 5, 10, 15, and 20 years. Based on the modeling configuration and parameter simplicity, the KNN algorithm is still a good option for non-expert users.
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A Non-Dimensional Mathematical Model of Shoreline Evolution with a Groin Structure Using an Unconditionally Stable Explicit Finite Difference Technique
(2022-01-01)
Manilam, Surasak
;
Pochai, Nopparat
Abstract—Coastal erosion is a natural phenomenon that occurs when sediment transport away from the coast is not countered by the formation of new material on the shoreline. This is indeed a problem that is driving the erosion of coastal areas. A sea wall and a groin were created to prevent coastal erosion and floods. The future topography of the beach is being investigated using shoreline evolution analysis. Erosion, accretion, and sea level changes are basic stages that have a significant impact on the coastal structure. A qualitative analysis of the model coastal behavior in relation to the controlling process is required to research beach erosion and beach deposition. When stated in terms of non-dimensional variables, all are mathematically equivalent. In general, the models do not have to be dimensionally different. Those might just be modifications of the same problems. One can solve a wide range of models with a single solution to the related nondimensional equation. In this research, we provide a governing equation when a groin is introduced to a one-dimensional shoreline growth model. A non-dimensional shoreline evolution model with a groin structure model is provided. The model now has the ability to manipulate physical parameters. When groin structural effects are present, the initial condition setting method and boundary condition approaches are also given. To approximate the incremental model in each year, the forward time-centered space technique and the unconditionally stable Saulyev finite difference methods are used. The Saulyev finite difference approach can handle numerical solutions in almost any scenario since the stability requirements are not restricted. The Saulyev finite difference technique can be very useful for computing a practical conceptual design of shoreline evolution since the number of grids has increased. The numerical models offered provide a viable simulation for evaluating long-term coastal development. The proposed modeling may be used to forecast the effectiveness of constructing a groin system on a local beach.
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An Unconditionally Stable Explicit Finite Difference Method for a Non-Dimensional Mathematical Model of Shoreline Evolution with a Twin Groins Structure
(2024-02-01)
Manilam, Surasak
;
Pochai, Nopparat
The development of a more effective model and the prediction of trends in shorelines were the two goals of this study. For simulations of shoreline evolution utilizing the straight twin groin structure, we used two mathematical models. A one-dimensional evolution model makes up the initial model. The first model is transformed into a non-dimensional evolution model in the second model. We propose a method for transforming one-dimensional models into non-dimensional models, that involves creating initial and boundary conditions for each model. The forward time centered space (FTCS) technique and the Saulyev finite difference technique were applied to approximately represent shoreline evolution each year. Their simulation results demonstrate that when the engineering structure was built on the nearby shorelines, shoreline evolution accelerated annually. As the Saulyev finite difference technique is not restricted by the stability conditions, it produces better simulations.
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A Biomathematical Clustering Framework for Classifying Neuromechanical Performance Profiles in Amateur Boxers
(2026-01-01)
Punthipayanon, Sirichet
;
Chottidao, Monchai
;
Manilam, Surasak
;
Thongtha, Kaboon
;
Pochai, Nopparat
Boxing effectiveness is strongly influenced by neuromuscular power generation and the efficient transmission of force through the lower extremities; however, traditional evaluation methods tend to assess these variables in isolation across athletes. A biomathematical clustering strategy provides a structured approach to extracting coherent performance patterns from complex, multidimensional biomechanical datasets. The present investigation proposes a biomathematical clustering model to categorise amateur boxers and quantify between-cluster variation, thereby facilitating tailored training interventions. A cohort of 30 amateur competitors underwent a series of standardised biomechanical tests, including Muscle Power (MP), reaction time (RT), rear-leg ground reaction force (GRF) relative to body mass, and maximal cross-punch (MCP) force output. Before analysis, all variables were rescaled through min–max normalisation. Unsupervised classification was executed via K-means clustering. The quality of clustering was assessed using indices of compactness and separation, specifically the Dunn Index and Davies–Bouldin Index. In contrast, the appropriate cluster count was determined using within-cluster sum of squares (WCSS) interpreted via the elbow method. Statistical procedures, including post hoc testing and effect size computation, were applied to evaluate intergroup differences. Additionally, principal component analysis (PCA) was utilised to project the data into a reduced-dimensional space for clearer visual interpretation of cluster distinctiveness. All computational procedures were implemented in Python. The analysis supported a three-cluster configuration. Cluster 3 (40%) exhibited superior performance characteristics, including elevated MP (7,700 ± 3,500 W), reduced RT (0.18 ± 0.03 s), and greater rear-leg GRF (1.55 ± 0.18 BW) relative to Cluster 2 (p ≤ 0.002; d = 1.35–2.45). In contrast, Cluster 2 (46.7%) was characterised by diminished MP (4,000 ± 1,400 W) and prolonged RT (0.26 ± 0.07 s), whereas Cluster 1 (13.3%) showed moderate values across all measured variables. The biomathematical clustering framework successfully distinguishes discrete neuromechanical profiles among amateur boxers, thereby enabling cluster-specific training strategies and enhancing individual performance optimisation.

Pochai, Nopparat

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