Smart cities are aiming to develop a management system for growing urban cities, improve the economy, energy consumption, and living standards of their citizens. Information and communication technology (ICT) has a much more important place in decision making, policy design, and implementation of modern techniques to develop smart cities. This review aims primarily to investigate the role of artificial intelligence (AI) and machine learning (ML) in the development of smart cities. This survey leads to the systematic interpretation of current patterns in ICT-related information flow publications as well as to the identification of the usual technologies used to facilitate this communication. In this paper, we represent the detailed presentation of AI & ML in the intelligent transport system and the prediction of mix design and mechanical properties of concrete.

Concept of water quality index need to be propagated in India under the present circumstances leading to spread out colonies / residential areas .in the present paper Canadian standard has been used. water quality indices where worked out for urban fringe area and rural area around Nagpur. Canadian concept of water quality index was adopted. Uses of water for drinking, Agriculture, recreation etc. where considered because of the prevailing water uses in the surveyed area.

The vision for sewage treatment plants is being revised and they are no longer considered as pollutant removing facilities but rather as water resources recovery facilities (WRRFs). However, the newly adopted bioprocesses in WRRFs are not fully understood from the microbiological and kinetic perspectives. Thus, large variations in the outputs of the kinetics-based numerical models are evident. In this research, data driven models (DDM) are proposed as a robust alternative towards modelling emerging bioprocesses. Methanotrophs are multi-use bacterium that can play key role in revalorizing the biogas in WRRFs, and thus, a Multi-Layer Perceptron Artificial Neural Network (ANN) model was developed and optimized to simulate the cultivation of mixed methanotrophic culture considering multiple environmental conditions. The influence of the input variables on the outputs was assessed through developing and analyzing several different ANN model configurations. The constructed ANN models demonstrate that the indirect and complex relationships between the inputs and outputs can be accurately considered prior to the full understanding of the physical or mathematical processes. Furthermore, it was found that ANN models can be used to better understand and rank the influence of different input variables (i.e., the physical parameters that influence methanotrophs) on the microbial activity. Methanotrophic-based bioprocesses are complex due to the interactions between the gaseous, liquid and solid phases. Yet, for the first time, this study successfully utilized DDM to model methanotrophic-based bioprocesses. The findings of this research suggest that DDM are a powerful, alternative modeling tool that can be used to model emerging bioprocesses towards their implementation in WRRFs.

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