Article Per Year (5 Year)
p-Index From 2020 - 2025
0.817
P-Index
This Author published in this journals
All Journal Emerging Science Journal Civil Engineering Journal
Pannarat Guayjarernpanishk
Faculty of Interdisciplinary Studies, Nong Khai Campus, Khon Kaen University, Nong Khai 43000,
Civil Engineering, Building, Construction & Architecture Environmental Science

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Epidemic Peaks Forecasting on Re-emerging Diseases in Elderly People using the Grey Disaster Model Nipaporn Chutiman; Pannarat Guayjarernpanishk; Butsakorn Kong-ied; Piyapatr Busababodhin; Monchaya Chiangpradit
Emerging Science Journal Vol 5, No 6 (2021): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/esj-2021-01325

Abstract

Climate change causes the spread of non-vector diseases due to the influence of climate uncertainty. The elderly group, which is vulnerable, is affected by such disasters. Therefore, the objectives of this study were to forecast epidemic peaks of food poisoning, which was found as one of the re-emerging diseases in elderly people in Khon Kaen Province, Maha Sarakham Province, and Roi Et Province, which are in the Northeastern region of Thailand by using 2 types of Grey Model: GM(1,1) and Discrete Grey Model (DGM). The monthly rate of food poisoning incidence per 100,000 elderly people from January 2017 to December 2020 i.e., 48 months in total were used in the study. The study result revealed that the DGM had higher forecasting effectiveness than that of the GM(1,1) in all three provinces. The food poisoning incidences in elderly people were forecasted to re-emerge from August to September 2021 in Khon Kaen Province, from August to September 2022 in Maha Sarakham Province, and from May to June 2022 in Roi Et Province. The results of this study are useful and helpful for the government, the Ministry of Public Health and related cooperatives to effectively help services planning resource preparation and prevention measures. Doi: 10.28991/esj-2021-01325 Full Text: PDF
Daily Maximum Rainfall Forecast Affected by Tropical Cyclones using Grey Theory Nipaporn Chutiman; Monchaya Chiangpradit; Butsakorn Kong-ied; Piyapatr Busababodhin; Chatchai Chaiyasaen; Pannarat Guayjarernpanishk
Civil Engineering Journal Vol 8, No 8 (2022): August
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2022-08-08-02

Abstract

This research aims to develop a model for forecasting daily maximum rainfall caused by tropical cyclones over Northeastern Thailand during August and September 2022 and 2023. In the past, the ARIMA or ARIMAX method to forecast rainfall was used in research. It is a short-term rainfall prediction. In this research, the Grey Theory was applied as it is an approach that manages limited and discrete data for long-term forecasting. The Grey Theory has never been used to forecast rainfall that is affected by tropical cyclones in Northeastern Thailand. The Grey model GM(1,1) was analyzed with the highest daily cumulative rainfall data during the August and September tropical cyclones of the years 2018–2021, from the weather stations in Northeastern Thailand in 17 provinces. The results showed that in August 2022 and 2023, only Nong Bua Lamphu province had a highest daily rainfall forecast of over 100 mm, while the other provinces had values of less than 70 mm. For September 2022 and 2023, there were five provinces with the highest daily rainfall forecast of over 100 mm. The average of mean absolute percentage error (MAPE) of the maximum rainfall forecast model in August and September is approximately 20 percent; therefore, the model can be applied in real scenarios. Doi: 10.28991/CEJ-2022-08-08-02 Full Text: PDF
The Partial L-Moment of the Four Kappa Distribution Pannarat Guayjarernpanishk; Piyapatr Bussababodhin; Monchaya Chiangpradit
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-06

Abstract

Statistical analysis of extreme events such as flood events is often carried out to predict large return period events. The behaviour of extreme events not only involves heavy-tailed distributions but also skewed distributions, similar to the four-parameter Kappa distribution (K4D). In general, this covers many extreme distributions such as the generalized logistic distribution (GLD), the generalized extreme value distribution (GEV), the generalized Pareto distribution (GPD), and so on. To utilize these distributions, we have to estimate parameters accurately. There are many parameter estimation methods, for example, Method of Moments, Maximum Likelihood Estimator, L-Moments, or partial L-Moments. Nowadays, no researchers have applied the partial L-Moments method to estimate the parameters of K4D. Therefore, the objective of this paper is to derive the partial L-Moments (PL-Moments) for K4D, namely the PL-Moments of the K4D in order to estimate hydrological extremes from censored data. The findings of this paper are formulas of parameter estimation for K4D based on the PL-Moments approach. We have derived the Partial Probability-Weighted Moments (PPWMs) of the K4D (β'r) and derive the estimation of parameters when separated by shape parameters (k,h) conditions i.e., case k>-1 and h>0, case k>-1 and h=0 and case -1<k<-1/h and h<0. Finally, we expect that the parameter estimate for K4D from this formula will help to make accurate forecasts. Doi: 10.28991/ESJ-2023-07-04-06 Full Text: PDF
Climate Forecasting Models for Precise Management Using Extreme Value Theory Pannarat Guayjarernpanishk; Monchaya Chiangpradit; Butsakorn Kong-ied; Nipaporn Chutiman
Civil Engineering Journal Vol 9, No 7 (2023): July
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2023-09-07-014

Abstract

The objective of this research was to develop a mathematical and statistical model for long-term prediction. The Extreme Value Theory (EVT) was applied to analyze the appropriate distribution model by using the peak-over-threshold approach with Generalized Pareto Distribution (GPD) to predict daily extreme precipitation and extreme temperatures in eight provinces located in the upper northeastern region of Thailand. Generally, each province has only 1–2 meteorological stations, so spatial analysis cannot be performed comprehensively. Therefore, the reanalysis data were obtained from the NOAA Physical Sciences Laboratory. The precipitation data were used for spatial analysis at the level of 25 square kilometers, which comprises 71 grid points, whereas the temperature data were used for spatial analysis at the level of 50 square kilometers, which includes 19 grid points. According to the analysis results, GPD was appropriate for the goodness of fit test with Kolmogorov-Smirnov Statistics (KS Test) according to the estimation for the return level in the annual return periods of 2 years, 5 years, 10 years, 25 years, 50 years, and 100 years, indicating the areas with daily extreme precipitation and extreme temperatures. The analysis results would be useful for supplementing decision-making in planning to cope with risk areas as well as in effective planning for resources and prevention. Doi: 10.28991/CEJ-2023-09-07-014 Full Text: PDF
Co-Authors Butsakorn Kong-ied Butsakorn Kong-ied Chatchai Chaiyasaen Monchaya Chiangpradit Monchaya Chiangpradit Monchaya Chiangpradit Nipaporn Chutiman Nipaporn Chutiman Piyapatr Busababodhin Piyapatr Busababodhin Piyapatr Bussababodhin