## 3 Dataset Analysis
### Analysis of Literature
#### 3.4.2 Top 10 Most Cited Papers--Review
It utilizes distributed artificial intelligence techniques and principles of MASs and HMSs in the context of manufacturing. The study by [PERSON] et al. [99] highlights the application of advanced machine learning models to predict flood-prone areas in the Teesta River basin, in northern Bangladesh. The research employs two hybrid ensemble models, Dagging and Random Subspace, combined with an artificial neural network (ANN), random forest (RF), and support vector machine (SVM). These models are validated using multiple statistical measures, showing high accuracy, with ROC AUC values above 0.80. The Dagging model outperformed others, indicating its potential for superior flood susceptibility modeling. The findings aim to support state and local authorities in mitigating flood risks and developing effective strategies for future flood management. [PERSON] et al. [100], in their study, present Adaptive Intelligent Dynamic Water Resource Planning (AIDWRP) as a solution to the challenges of sustainable water management in urban areas. The approach leverages AI, specifically the Markov Decision Process (MDP), to manage dynamic water resources efficiently. By combining AI tools and human expertise, AIDWRP aims to optimize environmental planning policies, improving water use efficiency and local economic performance. Numerical simulations demonstrate substantial improvements in balancing water supply and demand, indicating the potential of AIDWRP to address water management issues exacerbated by climate change. [PERSON] et al. [101], in their study, aim to map landslide susceptibility in the Ziarat watershed, in Golestan Province, Iran, using the maximum entropy (ME) machine learning model with two sampling strategies: Mahalanobis distance (MEMD) and random sampling \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Paper (First Author, Year, Journal, Reference)** & **Title** & **Context and Problem Statement** & **Data** & **Purpose** & **Hybrid Approach/ Theories** \\ \hline \multirow{8}{*}{9} & & & & & Provide local authorities with a better understanding of machine learning & \\  & & Comparative Analysis of Machine & in smart cities by comparing different machine learning & for pollution estimation using Apache Spark. & & \\  & & Quality in Smart Cities & techniques for real-time pollution prediction. & & & \\  & & & & & & \\  & & & & & & \\ \hline \multirow{8}{*}{10} & & & The main purpose of this study is to & & & Create a more accurate and comprehensive air quality index using the Tehran, Iran & \\  & & A novel, fuzzy-based air quality index & develop a novel fuzzy-based air quality index & Air quality data from five sampling stations & quality index using the Tehran, Iran & \\  & & 2011, _Atmospheric Environment_, [106] & quality assessment & the limitations of existing air quality indices, such as high levels of subjectivity. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._(MERS). A total of 92 landslides were recorded using GPS, and 12 controlling factors were selected through principal component analysis. The results showed that both MEMD and MERS performed well, with AUSRC values of 0.884 and 0.878, respectively. MEMD was found to have superior predictive power, with an AUPRC of 0.906. Lithological formations, proximity to roads, and precipitation were identified as the most significant factors. The study highlights that about 13.8% of the area is highly susceptible to landslides, providing critical information for land-use planning and risk management. In the study by [PERSON] et al. (2012), the purpose is to improve the simulation capability of the Cellular Automata Markov Chain (CA-MC) model by integrating it with an ANN to better predict land-use changes. The model uses socio-economic, spatial, and environmental variables to generate potential transition maps, which are then fed into the CA-MC model. The study validated the enhanced model using Kappa indices by comparing simulated maps with actual maps of Irbid City, Jordan, from 2015.