Energy-efficiency is one of the critical issues in smart cities. It is an essential basis for optimizing smart cities planning. This study proposed four new artificial intelligence (AI) techniques for forecasting the heating load of buildings’ energy efficiency based on the potential of artificial neural network (ANN) and meta-heuristics algorithms, including artificial bee colony (ABC) optimization, particle swarm optimization (PSO), imperialist competitive algorithm (ICA), and genetic algorithm (GA). They were abbreviated as ABC-ANN, PSO-ANN, ICA-ANN, and GA-ANN models; 837 buildings were considered and analyzed based on the influential parameters, such as glazing area distribution (GLAD), glazing area (GLA), orientation (O), overall height (OH), roof area (RA), wall area (WA), surface area (SA), relative compactness (RC), for estimating heating load (HL). Three statistical criteria, such as root-mean-squared error (RMSE), coefficient determination (R²), and mean absolute error (MAE), were used to assess the potential of the aforementioned models. The results indicated that the GA-ANN model provided the highest performance in estimating the heating load of buildings’ energy efficiency, with an RMSE of 1.625, R² of 0.980, and MAE of 0.798. The remaining models (i.e., PSO-ANN, ICA-ANN, ABC-ANN) yielded lower performance with RMSE of 1.932, 1.982, 1.878; R² of 0.972, 0.970, 0.973; MAE of 1.027, 0.980, 0.957, respectively.

In this study, a novel technique to support smart city planning in estimating and controlling the heating load (HL) of buildings, was proposed, namely PSO-XGBoost. Accordingly, the extreme gradient boosting machine (XGBoost) was developed to estimate HL first; then, the particle swarm optimization (PSO) algorithm was applied to optimize the performance of the XGBoost model. The classical XGBoost model, support vector machine (SVM), random forest (RF), Gaussian process (GP), and classification and regression trees (CART) models were also investigated and developed to predict the HL of building systems, and compared with the proposed PSO-XGBoost model; 837 investigations of buildings were considered and analyzed with many influential factors, such as glazing area distribution (GAD), glazing area (GA), orientation (O), overall height (OH), roof area (RA), wall area (WA), surface area (SA), and relative compactness (RC). Mean absolute percentage error (MAPE), root-mean-squared error (RMSE), variance account for (VAF), mean absolute error (MAE), and determination coefficient (R²), were used as the statistical criteria for evaluating the performance of the above models. The color intensity, as well as the ranking method, were also used to compare and evaluate the models. The results showed that the proposed PSO-XGBoost model was the most robust technique for estimating the HL of building systems. The remaining models (i.e., XGBoost, SVM, RF, GP, and CART) yielded more mediocre performance through RMSE, MAE, R², VAF, and MAPE metrics. Another finding of this study also indicated that OH, RA, WA, and SA were the most critical parameters for the accuracy of the proposed PSO-XGBoost model. They should be particularly interested in smart city planning as well as the optimization of smart cities.

Vietnam is an Asian country with hot and humid tropical climate throughout the year. Forests account for more than 40% of the total land area and have a very rich and diverse vegetation. Monitoring the changes in the vegetation cover is obviously important yet challenging, considering such large varying areas and climatic conditions. A traditional remote sensing technique to monitor the vegetation cover involves the use of optical satellite images. However, in presence of the cloud cover, the analyses done using optical satellite image are not reliable. In such a scenario, radar images are a useful alternative due to the ability of radar pulses in penetrating through the clouds, regardless of day or night. In this study, we have used multi temporal C band satellite images to monitor vegetation cover changes for an area in Dau Tieng and Ben Cat districts of Binh Duong province, Mekong Delta, Vietnam. With a collection of 46 images between March 2015 and February 2017, the changes of five land cover types including vegetation loss and replanting in 2017 were analyzed by selecting two cases, using 9 images in the dry season of 3 years 2015, 2016 and 2017 and using all of 46 images to conduct Random Forest classifier with 100, 200, 300 and 500 trees respectively. The result in which the model with nine images and 300 trees gave the best accuracy with an overall accuracy of 98.4% and a Kappa of 0.97. The results demonstrated that using VH polarization, Sentinel - 1 gives quite a good accuracy for vegetation cover change. Therefore, Sentinel - 1 can also be used to generate reliable land cover maps suitable for different applications.

This paper presents the results of an assessment of change in urban green spaces in Thanh Hoa city (Vietnam). Sentinel 2 MSI data in 2015 and 2021 are used to calculate 3 parameters: percentage of green, weight of green types, and weight of proximity to green. These parameters are used to calculate the Weighted Urban Green Space Index (WUGSI). The final result shows the distribution of green space in the study area consisted of very high-quality green, high-quality green, moderate quality green, and low quality green. The obtained results show that the quality of urban green space in Thanh Hoa city has changed significantly in the period 2015-2021, in which the area with category “low quality green space” increased from 7.17% up to 9.48%; areas with category “very high-quality green space” reduced from 65.02% to 47.39%.

Tidal flat plays a crucial role in socio - economic development and ecological environment. Tidal flats in Ha Long - Cam Pha in Vietnam are impacted by human activities, especially coal mining activities. Using remote sensing data is able to detect, extract, and monitor the changes of tidal flats and exploited coal mine area with multi - temporal, in various scales, and for a large coverage. This study aims to investigate the impact of coal mining activities on the changes of tidal flats using remote sensing in Cam Pha, Ha Long, one of the biggest coal basins in Vietnam. Digital Elevation Models (DEMs) of tidal flats constructed by Landsat satellite images acquired in years 1989, 2001, and 2014 are compared to determine the volume changes. Besides, coal mining activities including coal production, waste rock dump area, and the expansion of open coal mine during the period 1989 - 2014 are investigated using correspondent Landsat images and the reports from the coal mine companies in the study area. Sediment samples in tidal flats are analyzed to determine the origin of the sediments. As the results, organic matter in the tidal flats is dominant with the concentration of 459 g/kg to 607 g/kg, which is evidence for the impact of coal exploitation on the coastal environment. In addition, the relationship between coal mine activities and tidal flat variation is well observed in this study.

Gastronomy has been recognized as a source of place-based advantage for destination differentiation and territorial development. Culinary inventories are commonly used tools for documentation and planning purposes but often remain descriptive in nature and do not readily support spatial tourism analysis. This research created a Food Heritage Spatial Framework for Thanh Hoa Province, Vietnam, by converting a previously compiled inventory of 122 recorded culinary specialties into an analytically usable spatial database. Culinary specialties were coded based on seven traits, including ingredient base, cultural derivation, processing style, food type, cultural significance, peak season, and ecological region, then mapped using Geographic Information System (GIS) technology and clustered using mixed-attribute cluster analysis. Cluster analysis was performed using Gower dissimilarity to accommodate mixed data types, followed by Partitioning Around Medoids (PAM) and hierarchical clustering. Validity of clusters was determined using silhouette analysis. Spatial differentiation of cuisine revealed three ecological-zone patterns distributed across mountainous–midland (56 specialties; 45.9%), river delta–lowland (40; 32.8%), and coastal–estuarine (26; 21.3%) ecological regions. Cluster analysis revealed four key culinary strategy groups: Royal Heritage, Ethno-Ecological, Urban Gastronomy, and Marine Gastronomy, with an overall average silhouette width of 0.48. Findings illustrated how Thanh Hoa’s existing culinary geography represents differences in ecology as well as historical associations, local knowledge systems, processing styles and urban functions. These spatial patterns and identified culinary groups were mapped as potential gastronomic tourism itineraries and described within an integrated value-chain framework. This spatially derived gastronomy framework provides a planning basis that may support spatially informed gastronomic tourism development by linking culinary heritage with local communities, producers, destination characteristics, and regional development priorities. The study demonstrates how a descriptive culinary inventory can be transformed into a spatially organized and analytically usable planning framework that may support future sustainable gastronomic tourism planning.