This study leverages the efficacy of Voronoi diagram theory within a mixed-methods approach to thoroughly examine the spatial distribution, service coverage, and optimal locations for expanding electric vehicle (EV) charging infrastructure in Bangkok. Drawing on data from field surveys and public data providers, our analysis unfolds in four key stages. Firstly, we delve into the spatial distribution of charging stations, scrutinizing density, proximity to various road types, and land use through the lens of Voronoi diagrams. Secondly, the application of Voronoi diagrams informs the evaluation of service boundaries. Thirdly, utilizing this effective tool, we assess population density per parking slot or charging head to anticipate future EV adoption. Finally, the study introduces an approach to identify suitable locations for new charging stations through the application of overlapping Voronoi circles. Findings reveal a clustered distribution of charging stations along roads, particularly in the central business district, showcasing the efficiency of Voronoi diagrams in spatial analysis. Residential areas and urban commercial zones also host significant charging station concentrations. Notably, service coverage in inner Bangkok surpasses that of middle and outer areas, highlighting underserved regions. Prospective areas for new charging stations, identified through Voronoi analysis, include Bang Khae, Phra Khanong, Min Buri, and Huai Khwang. This research, rooted in the application of Voronoi diagram theory, offers vital insights for various stakeholders involved in urban infrastructure planning. By employing Voronoi diagrams within Geographic Information Systems (GIS), the study contributes to strategically placing charging stations, optimizing spatial understanding, and enhancing resource allocation. This GIS-based approach not only supports the rise of electric vehicles but also promotes sustainable urban development practices through the efficient utilization of spatial data and analysis techniques.

This study investigated the spatiotemporal dynamics of tropical deciduous forest including dry dipterocarp forest (DDF) and mixed deciduous forest (MDF) and its phenological changes in responses to El Niño and La Niña during 2001–2016. Based on time series of Normalized Difference Vegetation Index (NDVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS), the start of growing season (SOS), the end of growing season (EOS), and length of growing season (LOS) were derived. In absence of climatic fluctuation, the SOS of DDF commonly started on 106 ± 7 DOY, delayed to 132 DOY in El Niño year (2010) and advanced to 87 DOY in La Niña year (2011). Thus, there was a delay of about 19 to 33 days in El Niño and an earlier onset of about 13 to 27 days in La Niña year. The SOS of MDF started almost same time as of DDF on the 107 ± 7 DOY during the neutral years and delayed to 127 DOY during El Niño, advanced to 92 DOY in La Niña year. The SOS of MDF was delayed by about 12 to 28 days in El Niño and was earlier about 8 to 22 days in La Niña. Corresponding to these shifts in SOS and LOS of both DDF and MDF were also induced by the El Niño–Southern Oscillation (ENSO).

Land use land cover (LULC) change is one of the main drivers contributing to global climate change. It alters surface hydrology and energy balance between the land surface and atmosphere. However, its impacts on surface air temperature have not been well understood in a dynamic region of LULC changes like Southeast Asia (SEA). This study quantitatively examined the contribution of LULC changes to temperature trends in Myanmar and Thailand as the typical parts of SEA during 1990–2019 using the “observation minus reanalysis” (OMR) method. Overall, the average maximum, mean, and minimum temperatures obtained from OMR trends indicate significant warming trends of 0.17 °C/10a, 0.20 °C/10a, and 0.42 °C/10a, respectively. The rates of minimum temperature increase were larger than maximum and mean temperatures. The decreases of forest land and cropland, and the expansions of settlements land fractions were strongly correlated with the observed warming trends. It was found that the effects of forest land converted to settlement land on warming were higher than forest conversion to cropland. A comprehensive discussion on this study could provide scientific information for the future development of more sustainable land use planning to mitigate and adapt to climate change at the local and national levels.

The Himalayas, especially the Everest region, are highly sensitive to climate change. Although there are research works on this region related to cryospheric work, the ecological understandings of the alpine zone and climate impacts are limited. This study aimed to assess the changes in surface water including glacier lake and streamflow and the spatial and temporal changes in alpine vegetation and examine their relationships with climatic factors (temperature and precipitation) during 1995–2019 in the Everest region and the Dudh Koshi river basin. In this study, Landsat time-series data, European Commission’s Joint Research Center (JRC) surface water data, ECMWF Reanalysis 5th Generation (ERA5) reanalysis temperature data, and meteorological station data were used. It was found that the glacial lake area and volume are expanding at the rates of 0.0676 and 0.0198 km³/year, respectively; the average annual streamflow is decreasing at the rate of 2.73 m³/s/year. Similarly, the alpine vegetation greening as indicated by normalized difference vegetation index (NDVI) is increasing at the rate of 0.00352 units/year. On the other hand, the annual mean temperature shows an increasing trend of 0.0329 °C/year, and the annual precipitation also shows a significant negative monotonic trend. It was also found that annual NDVI is significantly correlated with annual temperature. Likewise, the glacial lake area expansion is strongly correlated with annual minimum temperature and annual precipitation. Overall, we found a significant alteration in the alpine ecosystem of the Everest region that could impact on the water–energy–food nexus of the Dudh Koshi river basin.

River segmentation is important in delivering essential information for environmental analytics such as water management, flood/disaster management, observations of climate change, or human activities. Advances in remote-sensing technology have provided more complex features that limit the traditional approaches’ effectiveness. This work uses deep-learning-based models to enhance river extractions from satellite imagery. With Resnet-50 as the backbone network, CNN U-Net and DeepLabv3+ were utilized to perform the river segmentation of the Sentinel-1 C-Band synthetic aperture radar (SAR) imagery. The SAR data was selected due to its capability to capture surface details regardless of weather conditions, with VV+VH band polarizations being employed to improve water surface reflectivity. A total of 1080 images were utilized to train and test the models. The models’ performance was measured using the Dice coefficient. The CNN U-Net architecture achieved an accuracy of 0.94, while DeepLabv3+ attained an accuracy of 0.92. Although DeepLabv3+ showed more stability during the training and performed better on wider rivers, CNN U-Net excelled at identifying narrow rivers. In conclusion, a river-segmentation model was conducted using Sentinel-1 C-Band SAR data, with CNN U-Net outperforming DeepLabv3+; this enabled detailed river mapping for irrigationand flood-monitoring applications – particularly in cloud-prone tropical regions.

A precise rainfall-runoff prediction is crucial for hydrology and the management of water resources. Rainfall-runoff prediction is a nonlinear method influenced by simulation model inputs. Previously employed methods have some limitations in predicting rainfall-runoff, such as low learning speed, overfitting issues, stopping criteria, and back-propagation issues. Therefore, this study uses distinctive soft computing approaches to overcome these issues for modeling rainfall-runoff for the Mangla watershed in Pakistan. Rainfall-runoff data for 29 years from 1978–2007 is used in the study to estimate runoff. The soft computing approaches used in the study are Tree Boost (TB), decision tree forests (DTFs), and single decision trees (SDTs). Using various combinations of past rainfall datasets, these soft computing techniques are validated and tested for the security of efficient results. The evaluation criteria for the models are some statistical measures consisting of root means square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), and Nash–Sutcliffe efficiency (NSE). The outcomes of these computing techniques were evaluated with the multilayer perceptron (MLP). DTF was found to be a more accurate soft computing approach with the average evaluation parameters R², NSE, RMSE, and MAE being 0.9, 0.8, 1000, and 7000 cumecs. Regarding R² and RMSE, there are about 57% and 17% of improvement in the results of DTF compared to other techniques. Flow duration curves (FDCs) were employed and revealed that DTF performed better than other techniques. This assessment revealed that DTF has potential; researchers may consider it an alternative approach for rainfall-runoff estimations in the Mangla watershed.