## 3 Overview of Results
### Most Cited Documents
According to the results, the neural network algorithm was the most suitable solution for the prediction of visitor arrivals, especially if the data had no trend or pattern. Only one author contributed to the paper, receiving 193 citations, with a TCY value of 8.39 and an NTC value of 1. A long short-term memory recurrent neural network (LSTM-RNN) and AI were implemented by [PERSON] et al. for a gauging station located in the Mahanadi River basin, India, containing daily information. A comparison was performed between LSTM-RNN, RNN, and naive methods. Multiple metrics, such as the Nash-Sutcliffe coefficient, correlation coefficient, and root mean squared error, were applied in order to test the accuracy of the algorithms. The outcome confirmed that LSTM-RNN was the model with the best metrics, representing a reliable AI solution for low-flow forecasting. The article had 171 citations,and four authors contributed to the research. The TCY value was 24.43, while the NTC value was 4.73. The importance of accurately predicting photovoltaic power generation represents a crucial step for integrating, operating, and scheduling smart grid systems, as [PERSON] et al. pointed out in their research. The authors focused on predicting the day-ahead hourly photovoltaic power supply, using historical data extracted from the DKASC website and taking into account existing constraints like volatility and intermittence of solar energy. A short-term temporal neural network model (ALSM), LSTM, and other algorithms were tested. The outcome confirmed that ALSM predicted the photovoltaic power with the highest accuracy. Three authors contributed to the article, resulting in a total of 154 citations, with a TCY value of 30.80 and an NTC value of 7.13. Using time series and AI, [PERSON] and [PERSON] [57] predicted the energy consumption of buildings by taking into account the real-time data collected from an experimental construction project. The objective of the research was to improve sustainable development and energy efficiency, reducing the costs and environmental impact. A hybrid model that combines optimization and forecasting methods was defined, affording better accuracy than ensemble models and representing a significant support for planning energy efficiency and management. Two authors investigated the topic, and the article was cited 146 times, with a TCY value of 18.25 and an NTC value of 2.40. A time series analysis, together with AI, was explored by [PERSON] [58] in order to predict daily rainfall runoff related to the hydrological domain. An emotional artificial neural network (EANN) was defined, trained, and evaluated, demonstrating the capability of the algorithm, compared with ANN, in understanding and differentiating rainless days and rainy days using artificial emotional system elements, such as hormonal criteria. [PERSON] was the only author that worked on the paper, obtaining 137 citations, with a TCY value of 15.22 and an NTC value of 1.96. Published in 1997, the paper in nineth position based on the number of citations, authored by [PERSON] [59], demonstrates an interest in the domain before the 21 st century. Using simulated data, the author explored urban traffic flow based on neural networks. The initial approach focused on modeling a local neural network, then including the traffic flow. The objective of the analysis was to demonstrate the possibility of modeling traffic flow using AI. The outcome showed that the model predicted the queue lengths one minute ahead, representing a good starting point for further analysis. A total of 108 citations have been obtained by the paper published by [PERSON], with a TCY value of 3.72 and an NTC value of 1. Self-organizing maps (SOMs) and least square support vector machine (LSSVM) have been combined, resulting in the SOM-LSSVM model, which was been tested by [PERSON] et al. [60] on two datasets, the Wolf yearly sunspot and monthly unemployed young women datasets.