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Drought vulnerability assessment using morphometric features and extreme precipitation indicators to prioritize sub-basins: AI-based Fuzzy Logic approach
(2026-03-01)
Nigam, Utkarsh
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Patel, Vinodkumar M.
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Patel, Dhruvesh P.
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Jodhani, Keval H.
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Gupta, Nitesh
The identification of watersheds and extraction of drainage networks are essential for effective hydrological and geomorphological modelling. This study investigates the influence of morphometric factors and extreme precipitation events on the hydrological responses of the Sabarmati River Basin (SRB), India, to identify the drought-vulnerable sub-basins. Watershed prioritization was carried out using satellite remote sensing, GIS, and secondary data, including topographic sheets and ASTER DEM with a spatial resolution of 90 m. The SRB was divided into nine sub-watersheds, and 28 morphometric parameters were evaluated, comprising 07 linear, 15 areal, and 06 relief parameters. A compound factor (CF) was derived using multi-criteria decision-making techniques such as Weighted Sum Analysis (WSA), Principal Component Analysis (PCA), Analytic Hierarchy Process (AHP), Fuzzy-AHP (FAHP), and TOPSIS. Sub-watersheds were ranked based on CF value, where a lower CF indicated higher priority for runoff management strategies. Additionally, 42 years of precipitation data were analysed using the Standardized Precipitation Index (SPI) at timescales ranging from 3 to 24 months to assess trends in drought and extreme precipitation events. The analysis indicate decline in runoff potential in several sub-basins, however others (e.g., SB2, SB3, SB4, SB5) exhibit positive precipitation trends, making them suitable for runoff enhancement. This integrated methodology offers a comprehensive framework for managing sub-basins, optimizing runoff potential, and supporting sustainable water conservation. The results provide actionable insights for policymakers and planners to better utilize the SRB water resources based on its geomorphological and climatic characteristics.
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GC-MS Metabolite Profiling and Chemometric Analysis of Robusta Green Beans (Bantjah Coffee)
(2025-01-01)
Permatasari, Fitria Indah
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Nazir, Novizar
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Anggraini, Tuty
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Hellyward, James
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Techavuthiporn, Chairat
In this study, three types of Robusta green bean coffee developed in the Bantjah area were characterized. This study characterized three types of Robusta green coffee beans with potential for development in the Bantjah area. The three coffee beans were (A) Bantjah area and the two (B, C) selected from different altitudes of the West Sumatra area. The purpose of this study was to analyze metabolite profiling of Robusta green coffee green beans. The coffee beans were fermented using the natural method and then the extract was derivatized with N-Methyl-N-(trimethylsilyl) trifluoroacetamide before being analyzed by Gas chromatography-mass spectrometry (GCMS). The chromatogram obtained was then analyzed statistically using principle component analysis (PCA). GCMS analysis produced more than 60 chemical components in green beans of Robusta coffee. Principle component analysis determined the metabolite distribution of coffee samples as influenced by their geographical origin. Coffee originating from the highlands had different marker compounds than coffee grown in lower plains. A metabolomics approach provides a comprehensive explanation of this relationship.
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Source apportionment of PM2.5 in Thailand’s deep south by principal component analysis and impact of transboundary haze
(2023-08-01)
Chaisongkaew, Phatsarakorn
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Dejchanchaiwong, Racha
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Inerb, Muanfun
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Mahasakpan, Napawan
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Nim, Nobchonnee
Atmospheric particulate matter smaller than 2.5 micron (PM<inf>2.5</inf>) was evaluated at four sites in the lower southern part of Thailand during 2019–2020 to understand the impact of PM<inf>2.5</inf> transport from peatland fires in Indonesia on air quality during the southwest monsoon season. Mass concentration and chemical bound-PM, including carbon composition, e.g., organic carbon (OC) and elemental carbon (EC), polycyclic aromatic hydrocarbons (PAHs), and inorganic elements, were analyzed. The PM<inf>2.5</inf> emission sources were identified by principal components analysis (PCA). The average mass concentrations of PM<inf>2.5</inf> in the normal period, which represents clean background air, from four sites was 3.5–5.1 µg/m3, whereas during the haze period, it rose to 5.4–13.5 µg/m3. During the haze period, both OC and EC were 3.5 times as high as in the normal period. The average total PAHs and BaP-TEQ of PM<inf>2.5</inf> during the haze period were ~ 1.3–1.7 and ~ 1.2–1.9 times higher than those in the normal period. The K concentrations significantly increased during haze periods. SO<inf>4</inf>2− dominated throughout the year. The effects of external sources, especially the transboundary haze from peatland fires, were significantly enhanced, because the background air in the study locations was generally clean. PCA indicated that vehicle emission, local biomass burning, and secondary particles played a key role during normal period, whereas open biomass burning dominated during the haze phenomena. This was consistent with the OC/EC and PAH diagnostic ratios. Backward trajectories confirmed that the sources of PM during the haze period were predominantly peatland fires in Sumatra, Indonesia, due to southwest wind.
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Growth and Yield of Watermelon (Citrullus lanatus) in Plastic House in Response to White LED Supplementary Lighting
(2023-01-01)
Chamchum, Wasinee
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Glahan, Somchai
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Kramchote, Somsak
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Maniwara, Phonkrit
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Suwor, Patcharaporn
Watermelon plants cultivar ‘Kinaree 457’ were grown in plastic house under natural daylight only (control) or with nightly LED supplementary lighting for 6 h (6:00 pm-12:00 pm) or 12 h (6:00 pm-6:00 am) starting from transplanting up to fruit harvest. Plant height, leaf chlorophyll content and fruit yield significantly increased in response to 6 h supplementary LED lighting. Fruit mass, size (length x width) and flesh thickness at 6 h LED treatment were about 2.3 kg, 19.3 ×15.7 cm, and 15.7 cm, respectively, while the fruit of control had 1.7 kg, 16.0 × 14.3 cm, and 13.8 cm, respectively. No significant treatment effect was obtained on peel thickness, flesh color L* and b* values, juice pH and total soluble solids. However, 6 h LED treatment resulted in lower reddening flesh (lower a* values), firmness and higher titratable acidity relative to the control, suggesting the need for improvement in cultural management. Furthermore, multivariate statistics of principal component analysis (PCA) performed on physico-chemical quality revealed the variations among watermelons from lighting and control treatments regardless of lighting hour.
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Heart Rate Estimation by PCA with LSTM from Video-based Plethysmography Under Periodic Noise
(2022-01-01)
Traivinidsreesuk, Chetsadaporn
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Yodrabum, Nutcha
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Chaikangwan, Irin
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Titijaroonroj, Taravichet
A remote photoplethysmography (rPPG) analysis can extract vital signs from the source video, including heart rate estimation. One of the problems of heart rate estimation is periodic noise embedded in the source video. It is difficult for an rPPG analysis to discriminate between vital signal information and noise, increasing prediction error. To alleviate this problem, this paper used principal component analysis (PCA) to extract rPPG signals from the input video before forwarding the signal to Long Short Term Memory (LSTM) to estimate heart rate. The experimental results show that, among discrete Fourier Transform method, neural networks, and neural network with LSTM, the proposed method accomplished a much lower MAEP at 15.05, 13.90, and 17.90 in the cases of overall, with no periodic noise, and with periodic noise, respectively.

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