# PRISMA on Machine Learning Techniques in Smart City Development
## 3 Results
### Green Innovation in Smart Cities (12 Papers)  
Green innovation is a cornerstone of sustainable urban development. Smart cities aim to minimize their environmental impact through the adoption of energy-efficient technologies and sustainable practices. This section delves into how machine learning is being utilized to drive green innovation in energy management, focusing on areas such as smart street lighting, energy waste management, and the monitoring of air and water quality. [58]. The studies reviewed provide insights into the transformative potential of these technologies in creating more sustainable urban environments. This theme highlights the transformative impact of Machine Learning (ML) and deep learning on the implementation of green innovations, particularly in energy management and sustainability within smart cities. Challenges in implementing deep learning in smart cities are acknowledged, and the feasibility of results in implementation and computation is considered. The information derived from the PRISMA analysis, for instance, underscores an advanced early warning system known as BARCS (Bacterial Risk Controlling System), which is engineered for the surveillance and management of hazards related to bacteria and disinfection byproducts within a drinking water distribution network. BARCS utilizes an artificial intelligence (AI) approach, specifically employing Support Vector Regression (SVR) for data-driven prediction. The system centers on the concentration of total chlorine (TCI) as a key factor for identifying and managing risks. The research involved a case study in a highly urbanized city in Southern China utilizing data from regular manual sampling and laboratory analysis provided by the local water supply company. The proposed framework involves a closed-loop monitoring-prediction-warning-advising system aiming to control the dual risk of bacteria and disinfection byproducts in urban drinking water distribution systems. The SVR model demonstrated effective prediction of TCI concentration throughout the distribution system, and BARCS showed comparable performance to other machine learning methods. The system's robustness under data error conditions was also evaluated. BARCS is presented as a flexible and expandable framework with potential applications in smart cities, emphasizing real-time monitoring and online prediction for improved drinking water safety. Overall, BARCS is deemed a promising solution for the safe management of drinking water in future smart cities, with potential for further enhancements and applications [57]. The issue of particulate pollution causing health problems in Southern China is also brought up, particularly in Hong Kong. The need for a practical urban air pollution [52] simulation model is emphasized, and the challenges of plume dispersion in the urban canopy layer (UCL) are discussed. The article highlights the use of machine learning (ML), specifically an Artificial Neural Network (ANN) model, to predict neighborhood-scale PM10 concentration levels in a densely populated urban environment. The performance of the ML model is compared with a Computational Fluid Dynamics (CFD) model [28; 58]. Studies in Hong Kong feature a roadside air quality monitoring station, major roads, sparse vegetation, and varied building heights. The ANN model, which comprises interconnected neurons, is used for pollution transport modelling. The ENVI-met CFD model simulates PM10 dispersion in the UCL using inputs such as wind speed, wind direction, air temperature, and relative humidity. Results indicate that the ANN model captures diurnal cycles and trends well, demonstrating good agreement with measurements for NO2 but less for particulate levels. The CFD model, while underestimating measurements, performs similarly to the ANN model for PM10 predictions. However, the ANN model requires significantly less computational time.