Chili pepper productivity in Indonesia is highly sensitive to climate variability, particularly solar radiation. This study aims to predict solar radiation to optimize chili pepper productivity across four distinct topographic zones in the Special Region of Yogyakarta. We employed the Extreme Gradient Boosting (XGBoost) algorithm to predict solar radiation 18 months ahead. Models were trained using monthly ERA5 data, with the solar radiation parameter specifically validated against ground-based measurements, resulting in an average Pearson correlation (r) of 0.79. Furthermore, we analyzed the impact of the El Niño-Southern Oscillation (ENSO) on local solar radiation anomalies, and utilized time-lag correlation analysis to evaluate the delayed effects of prior solar radiation on chili productivity. The XGBoost model demonstrated moderate predictive accuracy across all zones, yielding a Mean Absolute Error (MAE) of 10.87-12.38 W/m², Root Mean Square Error (RMSE) of 13.61-14.92 W/m², Coefficient of Determination (R²) of 0.45-0.59, and Symmetric Mean Absolute Percentage Error (SMAPE) of 5.17-5.69%. Spatiotemporal analysis revealed that El Niño phases increased radiation intensity, whereas La Niña decreased it (average r=0.34 across topographic variations). A two-month lag effect was identified, with solar radiation from the preceding two months showing the strongest association with chili productivity among the evaluated lag periods (average r=0.32). These findings suggest that solar radiation prediction may provide useful information for agricultural planning and climate adaptation strategies in regions influenced by climate variability.
Forest fires are a major environmental concern that affect ecosystems and human activities, particularly during Thailand’s main fire season. Understanding seasonal burned area dynamics is important for supporting fire monitoring and management. This study analyzed monthly burned area data from January to April for the period 1999-2024 to examine seasonal patterns and compare the forecasting performance of three time series methods: moving average, single exponential smoothing, and a regression based seasonal model. Forecasting performance was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results showed seasonal patterns and interannual variability in burned areas. Among the evaluated methods, the moving average model with a four-month window (MA(4)) achieved the lowest MAE and RMSE values. The regression-based seasonal model produced comparable forecasting errors, whereas exponential smoothing with α=0.3 showed higher error values. Residual analysis indicated no clear systematic pattern or persistent temporal trend, suggesting that the main temporal variation during the fire season was adequately represented by the forecasting models. These findings suggest that simple smoothing based forecasting methods may be suitable for short term forecasting of seasonal burned area data while providing a useful baseline for future studies.
Climate change has emerged as a global challenge, influenced by human activities, industrial operations and energy systems that depend on non-renewable energy such as carbon, gas, and petroleum. These activities have contributed to long-term shifts in temperature and broader climatic conditions. Land Surface Temperature (LST) has become an important standard for informing weather-related strategies at regional scales. This study aims to examine the seasonal pattern and variation of LST, including forecasting. Thailand is organized into six super-regions, each comprising nine sub-regions, for a total of 54 units. The sub-regions were delineated according to cardinal and intercardinal directions, with the central sub-region following the island’s shape. The separation between sub-regions is defined by a 150-pixel grid in latitude and longitude, approximately 95 km, covering the entire country. This study utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) data from NASA’s Terra Satellite, spanning the years 2000-2025. Cubic spline models incorporating eight, four, and three knots were applied to examine seasonal cycles, long-term variability, and LST projections for 2021-2031. The estimated mean increase in LST is 0.117oC per decade (1.17oC per century), statistically significant at the 5% level, [0.082, 0.272]oC of the confidence interval. The mean projected LST change designates a further significant increase of 0.219oC per decade. No statistically significant acceleration in LST is detected across Thailand’s super-regions, as the estimated acceleration of 0.103oC per decade2 does not meet the 0.05 significance threshold, and the associated p-value is 0.177.
The use of biomass has the potential to improve the mechanical characteristics of cement-based composites. Activated carbon is considered competitive in absorbing hazardous pollutants in the air, is able to reduce cracking and increase the mechanical strength of cement-based composites. The addition of gamal wood chips also has the potential to increase flexibility in mortar composites. The purpose of this study was to analyze the characteristics of the mechanical properties and pollutant absorption capacity of mortar composites with the addition of activated carbon and gamal wood chips. In this study, activated carbon is used as a fine aggregate (filler) with an addition of 1-5% to the weight of cement. Gamal wood chips are used as much as 1.5% of the total weight. Composite brick testing includes density, compressive strength, modulus of elasticity (MOE), modulus of rupture (MOR), adsorption capacity for ammonia, benzene, chloroform, and formaldehyde and thermal analysis of Field Emission Electron Microscopes (FE-SEM) and Fourier-transform infrared spectroscopy (FTIR). The density of composite mortar with the addition of activated carbon and gamal wood chips ranges from 1950.36±19.54-2137.24±45.09 kg/m3, compressive strength 8.48±0.76-9.71±1.68 MPa, MOE 421.74±69.59 -645.58±57.51 MPa, MOR 2.14±0.27-2.75±0.20 MPa. The composite mortar exhibited a total pollutant absorption rate of less than 3% for all tested parameters. The addition of activated carbon up to 4% can increase the mechanical strength and pollutant absorption capacity and can produce more CaCO3 in composite bricks. Morphological observations as analyzed by FE-SEM, were found to correlate with FTIR analysis, namely the formation of CaCO3. The following method produces an environmentally friendly mortar composite that performs exceptionally well and has a lot of potential for use in sustainable building.
The upper Lam Nam Chi and Lam Saphung subwatershed in Thailand is an area at risk of drought and water scarcity, with climate change having the potential to increase the severity of these problems. Therefore, this study evaluated streamflow and investigated the impact of climate change on streamflow in the research area, applying the Soil and Water Assessment Tool model under climate change scenarios RCP4.5 and RCP8.5. Streamflow data were used from two stations of the Royal Irrigation Department during 2012-2022 for model calibration, with model accuracy being analyzed using the Nash-Sutcliffe efficiency and PBIAS. The findings showed that the average annual streamflow of the upper Lam Nam Chi and Lam Saphung Subwatershed during 2012-2022 was 856.40 million cubic meters. The highest discharge occurred in September and the lowest in January. There was a considerable increase in the predicted annual streamflow under the climate change scenarios RCP4.5 and RCP8.5 in the near-term (2041-2060), medium-term (2061-2080), and long-term (2081-2100). The highest streamflow occurred in July and August and the lowest in April and May. In particular, the estimated annual streamflow under the long-term scenario without adopting any measures or policies for GHG emission control (RCP8.5) was notably different, being more than 4 times higher than the current streamflow. In conclusion, climate change influenced the streamflow in this watershed, which could lead to severe flooding and water scarcity in this watershed in the future.
