## 3 Dataset Analysis
### Analysis of Literature
#### 3.4.2 Top 10 Most Cited Papers--Review
The results showed that the integrated ANN and CA-MC model significantly outperformed the original CA-MC model in accuracy. The predicted maps for 2021 and 2027 are expected to help local authorities in managing urban expansion and protecting agricultural regions, thereby supporting Jordan's food security. The study by [PERSON] et al. (2017) focuses on predicting flood-prone zones in the Teesta River basin, Bangladesh, using state-of-the-art ensemble machine learning algorithms. By integrating bagging with REPTree, RF, M5P, and Random Tree (RT), the study aims to provide reliable and accurate flood susceptibility models. Twelve conditioning factors and 413 current and former flooding points were used to develop the models. The models were validated using the ROC curve, and several statistical tests were applied to compare their performance. The findings revealed that more than 800 km\({}^{2}\) of the area was predicted as having high flood susceptibility by all models, with the bagging with the M5P algorithm performing the best. The results offer valuable insights for regional and local authorities to mitigate flood risks and implement effective measures to prevent potential damage. The study by [PERSON] et al. (2013) explores strategies for companies, public administrators, and educational organizations to address digital transformation within the Tyrol-Veneto macroregion. The research employs qualitative methods, including interviews with 60 stakeholders and analyses using IRaMuTeQ and NVivo software. The findings reveal three strategic pillars: culture and skills, infrastructures and technologies, and ecosystems. These pillars encompass various fields of action, emphasizing the importance of a systemic perspective and collaboration among stakeholders. The study's insights are adaptable to other regional innovation systems, providing valuable references for enhancing business, market, and local development. The next study, by [PERSON] et al. (2014), investigates the shift towards decentralized decision-making in urban land-use policies, focusing on the collaborative roles of local authorities and the private sector in urban development. Through a comparative study of nine public-private partnerships in the Netherlands and the application of rough set analysis, the research identifies key factors such as institutional structure, financial viability, and spatial externalities that influence the success of urban revitalization projects. The study provides insights into the critical policy variables that can guide future urban development strategies. In the research by [PERSON] et al. (2015), the authors address the major environmental challenge of air pollution in smart city environments through real-time data monitoring and advanced machine learning techniques. By deploying IoT-based sensors, the study compares four regression models for pollution prediction using multiple datasets processed using Apache Spark. The evaluation criteria include Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for accuracy, alongside processing time. The comparative analysis aims to identify the best model for accurate and timely air quality prediction, assisting local authorities in making informed decisions to manage traffic and reduce pollution levels. In the last study analyzed, [PERSON] et al. (2016) address the limitations of existing air quality indices by developing a novel fuzzy-based air quality index (FAQII). Utilizing fuzzy logic, which is a key computational method in artificial intelligence, the index incorporates two criteria, air pollutants and volatile organic compounds, due to their health impacts. Different weighting factors are assigned to each pollutant, and trapezoidal membership functions are employed for classifications. The final index is based on 72 inference rules. A case study using air quality data from Tehran was conducted, comparing FAQII with the USEPA AQI, showing that FAQII is a comprehensive and accurate tool for air quality classification.