One of the most important variables that leads to effective individual and group tours is the tourism route planning approach, which enables tourists to engage with tourism with ease, speed, and safety. However, current methods of designing tourist routes have some glitches, such as relying only on external objectives to find the best route. In this paper, a novel urban tourism path planning method based on a multiobjective genetic algorithm is proposed. The main goal of this paper is to enhance the accuracy of the genetic algorithm (GA) by adopting new parameters and selecting the optimal tourism path by combining external and internal tourist site potentials. Moreover, the GA and analytical hierarchy process (AHP) were used in our proposed approach to evaluate urban tourism route planning under multiple conflicting objectives. To visualize and execute the proposed approach, the geographic information system (GIS) environment was used. Our suggested approach has been applied to develop the tourist road network of Chengdu City in China. Compared with existing tourism path planning approaches, our proposed approach is more accurate and straightforward than other approaches used to choose routes.

Social media platforms play a vital role in determining valuable tourist objectives, which greatly aids in optimizing tourist path planning. As data classification and analysis methods have advanced, machine learning (ML) algorithms such as the k-means algorithm have emerged as powerful tools for sorting through data collected from social media platforms. However, traditional k-means algorithms have drawbacks, including challenges in determining initial seed values. This paper presents a novel approach to enhance the k-means algorithm based on survey and social media tourism data for tourism path recommendations. The main contribution of this paper is enhancing the traditional k-means algorithm by employing the genetic algorithm (GA) to determine the number of clusters (k), select the initial seeds, and recommend the best tourism path based on social media tourism data. The GA enhances the k-means algorithm by using a binary string to represent initial centers and to apply GA operators. To assess its effectiveness, we applied this approach to recommend the optimal tourism path in the Red Sea State, Sudan. The results clearly indicate the superiority of our approach, with an algorithm optimization time of 0.01 s. In contrast, traditional k-means and hierarchical cluster algorithms required 0.27 and 0.7 s, respectively.

Air pollution is an escalating concern for both environmental sustainability and public health, exacerbated by urbanization and industrial growth. In Saudi Arabia, pollutants primarily from industrial activities and vehicle emissions present significant health hazards. This study utilizes data from the Sentinel-5P satellite to analyze the variations in Carbon Monoxide (CO), Nitrogen Dioxide (NO₂), Sulfur Dioxide (SO₂), and Particulate Matter (PM2.5) over a five-year period, from January 2019 to December 2023. The data was processed using Google Earth Engine (GEE) to produce monthly and seasonal averages, while ArcGIS Pro was used to map trends and spatial distribution. The results reveal distinct seasonal fluctuations in pollution levels, with CO peaking between March-May and July-September but showing an overall decline. NO₂ and SO₂ exhibit seasonal highs with slight upward trends, likely linked to industrial output and traffic emissions. PM2.5, the most harmful pollutant to human health, consistently surpasses World Health Organization (WHO) limits, especially during high-emission periods. These findings underscore the urgency of adopting targeted measures to mitigate pollution during critical times and safeguard public health. The seasonal spikes, particularly in industrial and densely populated regions, highlight the need for improved policies and technologies to effectively monitor and manage air quality

The agricultural landscape of Al Jazirah State in Sudan, has undergone significant changes influenced by environmental and socio-political factors. This research used the remote sensing technology of the Normalized Difference Vegetation Index (NDVI), to analyze these transformations. Utilizing Sentinel-2 imagery and Google Earth Engine (GEE), tools used to conducted a robust data preparation process to calculate NDVI and perform GIS-based analyses to quantify vegetated areas and monitor temporal shifts from 2016 to 2024. Initial findings indicate a substantial expansion in vegetated area, peaking in 2020, followed by a sharp decline post-2019 due to socio-political unrest, including the December 2019 revolution and the conflict in December 2023. Despite stable climatic variables temperature, rainfall, and humidity political dynamics emerged as the primary driver of observed changes. The total vegetated area peaked at 8044.128 $${Km}^{2}$$ Km 2 in 2020, decreasing to 3384.544 $${Km}^{2}$$ Km 2 by 2024, marking a 42% reduction. The most important element affecting vegetation development, according to correlation analysis, was precipitation (r = 0.89), but temperature had little effect (r = 0.04). In addition to highlighting the importance of rainfall in vegetation dynamics, negative correlations between temperature and rainfall (r = -0.57) and between humidity and rainfall (r = -0.58) indicate the role of rainfall in vegetation dynamics. This study underscores the profound impact of political instability on agricultural productivity, emphasizing the urgent need for stable governance and effective land management policies to ensure sustainable development in regions facing similar challenges.

This study assesses air pollution in Sichuan Province, China, from January 2019 to December 2023 using Copernicus Sentinel-5P satellite data. The analysis reveals distinct seasonal variations in levels of Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), and Ozone (O3), with urban areas experiencing higher concentrations due to industrial and vehicular emissions. CO and O3 frequently exceeded WHO guidelines, particularly in winter and summer, indicating significant health risks. While most pollutant levels remained within safety limits, the findings highlight the importance of continuous monitoring and targeted mitigation strategies to improve air quality and safeguard public health, especially in urban environments.