Jamjareegulgarn, Punyawi

Preferred name

Alternative Name

Jamjareegulgarn, P.

Main Affiliation

KMITL Prince of Chumphon Campus

punyawi.ja@kmitl.ac.th

Now showing 1 - 10 of 27

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Deep Machine Learning Based Possible Atmospheric and Ionospheric Precursors of the 2021 Mw 7.1 Japan Earthquake
(2023-04-01)
Draz, Muhammad Umar
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Shah, Munawar
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Jamjareegulgarn, Punyawi
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Shahzad, Rasim
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Hasan, Ahmad M.
Global Navigation Satellite System (GNSS)- and Remote Sensing (RS)-based Earth observations have a significant approach on the monitoring of natural disasters. Since the evolution and appearance of earthquake precursors exhibit complex behavior, the need for different methods on multiple satellite data for earthquake precursors is vital for prior and after the impending main shock. This study provided a new approach of deep machine learning (ML)-based detection of ionosphere and atmosphere precursors. In this study, we investigate multi-parameter precursors of different physical nature defining the states of ionosphere and atmosphere associated with the event in Japan on 13 February 2021 (M<inf>w</inf> 7.1). We analyzed possible precursors from surface to ionosphere, including Sea Surface Temperature (SST), Air Temperature (AT), Relative Humidity (RH), Outgoing Longwave Radiation (OLR), and Total Electron Content (TEC). Furthermore, the aim is to find a possible pre-and post-seismic anomaly by implementing standard deviation (STDEV), wavelet transformation, the Nonlinear Autoregressive Network with Exogenous Inputs (NARX) model, and the Long Short-Term Memory Inputs (LSTM) network. Interestingly, every method shows anomalous variations in both atmospheric and ionospheric precursors before and after the earthquake. Moreover, the geomagnetic irregularities are also observed seven days after the main shock during active storm days (Kp > 3.7; Dst < −30 nT). This study demonstrates the significance of ML techniques for detecting earthquake anomalies to support the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) mechanism for future studies.
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Urban rainfall in the Capitals of Brazil: Variability, trend, and wavelet analysis
(2022-04-01)
Oliveira-Júnior, José Francisco de
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Correia Filho, Washington Luiz Félix
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Monteiro, Lua da Silva
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Shah, Munawar
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Hafeez, Amna
The patterns of urban rainfall in Brazil's capitals are critical, due to population growth and extreme weather. Therefore, the objectives are: i) to identify homogeneous rainfall groups and meteorological systems, ii) to evaluate the trend of the monthly rainfall time series and iii) to apply wavelet analysis to estimate the variance at different frequencies in the rainfall series in the capitals of the Brazil. Monthly rainfall data during 1960–2020 for 27 stations located in the capitals of Brazil were used. The data were flawed, and data imputation (mtsdi package) was applied via Fully Conditional Specification (FCS). Rainfall data were submitted to descriptive, exploratory statistics (boxplot), multivariate analysis (Cluster Analysis - CA) and the Mann-Kendall (MK) test. Seven CA methods (Ward, Single, Complete, Average, McQuity, Median and Centroid) were tested using the cophenetic correlation coefficient (CCC) with a significance level of 5%, the Average method obtained CCC > 0.81 (S). The CA identified three homogeneous regions (G1, G2 and G3) in the capitals of Brazil. The G1 group is formed by the capitals of the Northeast of Brazil (NEB), except for Boa Vista, (North of Brazil - NB). The G2 group is the largest group formed by the capitals of the Midwest (MWB), Southeast (SEB) and South (SB) of Brazil. The G3 group is the smallest group, with the capitals of the NB and some of the NEB. The capitals with the category of significant growth trend were only Porto Alegre and Florianópolis (SB), Vitória (SEB) and Belém (NB). The category of non-significant increase trend prevailed in most capitals of Brazil, with emphasis on the corridor formed between the NB and the Center-South, except for Natal (NEB). The without trend category prevailed in the North, Northeast and Midwest regions of Brazil. Monthly precipitation analyzes for trend detection purposes via Wavelet Analysis showed that ENSO phases are significant in rainfall variability in Brazilian capitals.
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Possible Thermal Anomalies Associated with Global Terrestrial Earthquakes during 2000-2019 Based on MODIS-LST
(2022-01-01)
Shah, Munawar
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Ehsan, Muhsan
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Abbas, Ayesha
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Ahmed, Arslan
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Jamjareegulgarn, Punyawi
The recent advances in satellite-based earthquakes (EQs) precursors provide an opportunity to correlate the seismic variation on lithosphere with atmosphere during the EQ preparation period through a rigorous atmospheric monitoring system. In the present study, seismic-induced thermal anomalies from cloud-free satellite thermal images of Moderate Resolution Imaging Spectroradiometer-Land Surface Temperature (MODIS-LST) are analyzed within a time interval of three months (precedent two months and succeeding one month to each EQ day) of 13 $\text{M}_{w} \ge6.0$ terrestrial EQs during 2000-2019. All these EQs occur in low vegetation and no snow cover regions except $\text{M}_{w}~6.7$ , Siberia Russia event. Remote sensing data show evidence of significant perturbation with reference to confidence bounds in LST within 5-20 time window upon the antecedent and the descendant of EQ day. The studied thermal anomalies are obtained from LST values over the epicenter region. This work endorses the performance of MODIS-LST for detecting EQ-induced thermal anomalies in terrestrial regions with no vegetation and snow cover and also assisting to the development of lithosphere-atmosphere hypothesis over the epicenter region.
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Assessment of improvement of the IRI model for foF2 variability over three latitudes in different hemispheres during low and high solar activities
(2021-03-01)
Timoçin, Erdinç
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Temuçin, Hüseyin
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Inyurt, Samed
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Shah, Munawar
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Jamjareegulgarn, Punyawi
This paper discusses the diurnal and seasonal variations of the F2 layer critical frequency (foF2) and the improvement of performance of the IRI-2016 model in predicting foF2 over three latitudes in different hemispheres during low and high solar activities. We extracted the foF2 data from six ionosonde stations which are Manila (14.7oN, 121.1oE), Yamagawa (31.2oN, 130.6oE), Yakutsk (62.0oN,129.6oE), Townsville (19.6oS, 146.8oE), Hobart (42.9oS, 147.3oE) and Terre Adelie (66.6oS, 140.0oE). The data of both low solar activity (LSA) period and high solar activity (HSA) periods were divided into three seasons as Northern Summer (May, June, July and August), Equinoxes (March, April, September and October) and Northern Winter (November, December, January and February). The present study showed that the IRI-2016 performance is strongly dependent on the solar activity, latitude, season, local time and hemisphere. For both hemispheres, the foF2 values at low latitude station are larger than those at middle latitude station, whereas the foF2 values at middle latitude station are larger than those at high latitude station. The agreement between IRI2016-modelled foF2 and foF2 measurements on all stations selected in the northern hemisphere is best for North Summer and worst for North Winter. For northern hemisphere, the values of relative deviations during both solar activities are largest in high latitudes and smallest in middle latitudes. As for southern hemisphere, the values of relative deviations during LSA are largest in middle latitudes and smallest in high latitudes, whereas the values of relative deviations during HSA are largest in low latitudes and smallest in high latitudes. It is thought that the relative deviations in the observed foF2 values are caused by solar activity that strongly alter chemical and electromagnetic processes in the ionosphere. These results are important for future improvements depending on solar activity and seasons in the IRI model for foF2 values over three latitudes in different hemispheres.
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Machine-Learning-Based Lithosphere-Atmosphere-Ionosphere Coupling Associated with Mw > 6 Earthquakes in America
(2023-08-01)
Shah, Munawar
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Shahzad, Rasim
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Jamjareegulgarn, Punyawi
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Ghaffar, Bushra
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Oliveira-Júnior, José Francisco de
The identification of atmospheric and ionospheric variations through multiple remote sensing and global navigation satellite systems (GNSSs) has contributed substantially to the development of the lithosphere-atmosphere-ionosphere coupling (LAIC) phenomenon over earthquake (EQ) epicenters. This study presents an approach for investigating the Petrolia EQ (Mw 6.2; dated 20 December 2021) and the Monte Cristo Range EQ (Mw 6.5; dated 15 May 2020) through several parameters to observe the precursory signals of various natures. These parameters include Land Surface Temperature (LST), Air Temperature (AT), Relative Humidity (RH), Air Pressure (AP), Outgoing Longwave Radiations (OLRs), and vertical Total Electron Content (TEC), and these are used to contribute to the development of LAIC in the temporal window of 30 days before and 15 days after the main shock. We observed a sharp increase in the LST in both the daytime and nighttime of the Petrolia EQ, but only an enhancement in the daytime LST for the Monte Cristo Range EQ within 3–7 days before the main shock. Similarly, a negative peak was observed in RH along with an increment in the OLR 5–7 days prior to both impending EQs. Furthermore, the Monte Cristo Range EQ also exhibited synchronized ionospheric variation with other atmospheric parameters, but no such co-located and synchronized anomalies were observed for the Petrolia EQ. We also applied machine learning (ML) methods to confirm these abrupt variations as anomalies to further aid certain efforts in the development of the LAIC in order to forecast EQs in the future. The ML methods also make prominent the variation in the different data.
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Wet and dry periods in the state of Alagoas (Northeast Brazil) via Standardized Precipitation Index
(2021-11-01)
Oliveira-Júnior, José Francisco de
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de Gois, Givanildo
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Silva, Iago José de Lima
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de Oliveira Souza, Edson
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Jardim, Alexandre Maniçoba da Rosa Ferraz
The state of Alagoas has its economic base directed to agriculture, and the knowledge of wet and dry periods is fundamental for the success of agricultural enterprises. The objective was to define the wet and dry periods in the state of Alagoas via multivariate analysis applied to the Standardized Precipitation Index (SPI). Monthly rainfall data for 20 weather stations from 1960 to 2016 were used, with fault filling via imputation. Moreover, Cluster analysis (CA) was applied to the time series (definition of homogeneous regions) and SPI-12 (annual) - (determination of dry and wet spell periods). The annual accumulated rainfall showed two distinct periods: 1960 to 1990 (P1) and 1990 to 2016 (P2), according to Pettitt test. Both periods alternate according to the phases of La Niña and Neutral (rainy years) and El Niño (dry years). In the monthly rainfall, only the Eastern part of Alagoas increased the rainy months during long time series. In tested connection methods, the average connection, also known as cophenetic correlation coefficient (CCC = 0.8760), represented homogeneous groups of rainfall, featuring two regions: one on the coast (G<inf>1</inf>) and another hinterland and arid (G<inf>2</inf>); and two non-homogeneous groups (NA) in the state. The annual SPI helped identify dry and wet periods, regardless of El Niño-Southern Oscillation (ENSO) categorization. The SPI-12 categories show high annual and decadal variability in the groups, except for the extremely dry and wet categories. There is greater variability in dry and wet periods near the coast than hinterland of the state.
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Remotely sensed atmospheric anomalies of the 2022 Mw 7.0 Bantay, Philippines earthquake
(2025-02-15)
Khan, Sohrab
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Shah, Munawar
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Jamjareegulgarn, Punyawi
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El-Sherbeeny, Ahmed M.
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Abukhadra, Mostafa R.
Remote sensing satellites have emerged as invaluable tools for surveilling natural disasters with more inevitable insights at various altitudes in atmosphere for various precursors. Moreover, the methods and satellite data before and after any event need more understanding for predicting the main shock due to the complexity of precursors. This study involves data from multiple sensors to assess how atmospheric parameters change in space and time over the Mw 7.0 Bantay, Philippines epicenter. The methods of statistical analysis, Nonlinear Autoregressive Network with Exogenous Inputs (NARX), and Multilayer Perceptron (MLP) are applied to various atmospheric parameters, including Land Surface Temperature (LST), Air Temperature (AT), Relative Humidity (RH), and Outgoing Longwave Radiation (OLR) to identify abnormal atmospheric patterns associated with earthquakes (EQ). These analyses focus on 3–5 days before the earthquake day. For this purpose, we trained daily average indices of atmospheric parameters for the month leading up to and the 15 days following the main shock. Since variations are irregular, detection can be challenging with classical statistics; therefore, we leveraged supervised machine learning to detect anomalies promptly and minimize the chances of missed detection. Thus, these findings support the lithosphere-atmosphere–ionosphere coupling (LAIC) hypothesis and suggest the need for further investigation in future research.
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Exploring vegetation health in Southern Thailand under climate stress from temperature and water impacts between 2000 and 2023
(2025-12-01)
Mehmood, Kaleem
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Anees, Shoaib Ahmad
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Shahzad, Fahad
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Muhammad, Sultan
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Liu, Qijing
This study provides a detailed spatiotemporal analysis of vegetation health in Southern Thailand from 2000 to 2023, focusing on the impacts of temperature and water stress on vegetation degradation. Using high-resolution Landsat-derived kernel Normalized Difference Vegetation Index (kNDVI) and Land Surface Temperature (LST), alongside precipitation (PPT), soil moisture (SM), vapor pressure deficit (VPD), and solar radiation (SR), several key indices were derived such as Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI). The study offers a robust framework to monitor vegetation health under climate stress by integrating satellite-based indices with detailed climate datasets. Our findings reveal significant temperature-induced stress during critical years like 2005 and 2016, with over 60% of the region experiencing vegetation degradation. Long-term trend analysis indicates that while 22.5% of forested areas show signs of recovery, 3.6% continue to degrade, primarily due to persistent temperature extremes and water stress. Soil moisture emerged as a critical driver during the dry season, positively influencing 11.16% of the region, while solar radiation exhibited mixed effects depending on moisture availability. These insights highlight the complex interplay of climatic drivers on vegetation dynamics, particularly in tropical ecosystems. The study underscores the need for adaptive management strategies to enhance resilience against climate extremes, providing valuable guidance for sustainable land management in Southern Thailand.
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Exploring Electromagnetic Wave Propagation Through the Ionosphere Over Seismic Active Zones
(2025-03-01)
Eshkuvatov, Husan
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Ahmedov, Bobomurat
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Shah, Munawar
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Begmatova, Dilfuza
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Jamjareegulgarn, Punyawi
This study presents an analytical solution for the electric current formation in the lower ionosphere as a result of charged aerosols being ejected from the ground before the earthquakes. The impact of ionosphere-related processes on radio wave propagation through the atmosphere is explored by investigating the resulting energy losses of electromagnetic waves traversing this ionospheric layer. Theoretical considerations suggest that these processes may generate detectable electromagnetic signals, offering insights into seismic precursors. The effects of electron density inhomogeneities in the upper ionospheric layers on electromagnetic wave properties such as group delay, Faraday rotation, and Doppler frequency shift are examined. Understanding these effects aims to improve ionospheric monitoring techniques to detect pre-earthquake disturbances. To validate the theoretical findings, a comparison is made with the empirical data from various sources, including VLF transmitters and GPS-TEC measurements. This comparative analysis underscores the potential of electromagnetic phenomena as credible indicators of impending seismic events.
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Spatiotemporal analysis of surface Urban Heat Island intensity and the role of vegetation in six major Pakistani cities
(2025-03-01)
Anees, Shoaib Ahmad
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Mehmood, Kaleem
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Raza, Syed Imran Haider
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Pfautsch, Sebastian
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Shah, Munawar
The Urban Heat Island (UHI) phenomenon exacerbates thermal discomfort in urban areas and significantly contributes to urban overheating when combined with climate change. This study investigates the spatiotemporal patterns of Surface Urban Heat Island Intensity (SUHII) in six major cities of Pakistan, focusing on the interplay between urban expansion, vegetation cover, and SUHII. To quantify SUHII dynamics, the impact of urban sprawl and vegetation changes was analyzed. The study offers critical insights into the implications for urban planning and policymaking in Pakistan. Using remote sensing data from Landsat satellites, analyzed with Geographic Information Systems (GIS) techniques, estimates of SUHII, urban expansion, and vegetation cover were derived. Specifically, imagery from Landsat-5 (2010−2013) and Landsat-8 (2014–2022), obtained from the US Geological Survey (USGS), was employed. Statistical analyses, including Pearson's correlation and linear regression, were conducted to assess relationships between these variables from 2010 to 2022. SUHII was found to increase annually by 0.18 °C in Islamabad and 0.19 °C in Peshawar, with corresponding urban expansion rates of 8.07 km2 (8967.75 pixels) and 1.67 km2 (1860.42 pixels) per year, respectively. Vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and Fractional Vegetation Cover (FVC) were inversely correlated with SUHII, explaining up to 50 % of the variance in Peshawar. However, weaker correlations in Lahore suggest the presence of additional factors influencing SUHII. A distinct spatial relationship between increased vegetation and cooler areas was observed. For instance, Islamabad has greater vegetation cover and cool zones over 41.5 km2. In contrast, Lahore's hot spots spanned 127.1 km2, compared to Abbottabad's 10.4 km2, underscoring the thermal impact of reduced vegetation. The findings emphasize the effectiveness of urban greening, particularly in Islamabad's neutral thermal regions, in mitigating SUHII. This study offers important insights for urban planners in developing sustainable, climate-resilient cities within similar urban contexts. While the results are specific to Pakistani cities, the role of vegetation in mitigating SUHII may hold broader relevance for urban planning strategies in comparable settings.

Jamjareegulgarn, Punyawi

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