Smart City Gnosys
Smart city article details
| Title | Smart City Optimization Through Machine Learning For Enhancing Urban Efficiency And Sustainability |
|---|---|
| ID_Doc | 50394 |
| Authors | Papitha Christobel T.; Meenakshi S.; Rajeswari P.; Mohanambal K.; Giri R.K. |
| Year | 2024 |
| Published | Nanotechnology Perceptions, 20, S5 |
| DOI | http://dx.doi.org/10.62441/nano-ntp.v20iS5.22 |
| Abstract | This research is based on the use of machine learning algorithms to improve the operations of smart cities for greater urban efficiency and sustainability. With the challenges associated with resource allocation, environmental sustainability, and service delivery that defines the increasing trend of urbanization, cities are bound to enhance their approaches and abilities to deliver sustainable urban environments. Through the enormous data generated in urban environments, machine learning techniques have been developed that offer cities and city administrations unique opportunities for decision making on resource allocations and waste management. This paper has reviewed the analysis of existing information and research from other scholars and works in the field of ML and smart cities. The reviewed works cover the area associated with the use of ML algorithms in various areas of smart cities, such as transportation, energy management, waste management, public safety, and urban planning. In most studies, machine learning algorithms show positive results of optimization. In particular, the reward function values varied from 0.85 to 0.92, while policy gradient scores varied between 0.78 and 0.86, and Q-values reached 0.97. At the same time, the NLP techniques produced rather impressive accuracy, precision, recall, and F1 score results, varying from 0.89 to 0.95, 0.86 to 0.92, 0.90 to 0.96, and 0.88 to 0.94, respectively. The time series forecasting conducted via the employment of the ARIMA model provided accurate predictions. Particularly, the MAE scores ranged from 2.0 to 2.7, MSE was between 4.2 and 5.5, RMSE varied from 2.0 to 2.6, and MAPE was between 3.2% and 4.7%. © 2024, Collegium Basilea. All rights reserved. |
| Author Keywords | Battery management systems; Convolutional Neural Networks; Lithium-ion batteries; Machine learning; State-of-health prediction |
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