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Population and urban growth are challenging traditional approaches to solving city-related problems. To meet these challenges, the concept of smart city/community (SC) has been introduced as a strategic solution. This research seeks to identify the key smartness dimensions of a city, build a corresponding novel smartness concept, and develop a full assessment model. The contribution of this research includes identifying three key dimensions for SCs: connectivity (C), sustainability (S), and resiliency (R); and developing a corresponding maturity model (MM) for SC assessment referred to as CSR-MM. The model's applicability is validated by examining its conformance to MM design principles, available in the literature, and practically demonstrated via a case study (Fredericton Public Transit, New Brunswick). The assessment outcomes were compared against an international SC assessment tool (ISO37120 2018). Municipalities will benefit from this CSR-MM in identifying maturity gaps, setting prioritized goals, and focusing on continuously improving citizens' well-being. [ABSTRACT FROM AUTHOR]

The deterioration of surface water quality occurs due to the presence of various types of pollutants generated from human, agricultural, and industrial activities. Thus, mapping concentrations of different surface water quality parameters (SWQPs), such as turbidity, total suspended solids (TSS), chemical oxygen demand (COD), biological oxygen demand (BOD), and dissolved oxygen (DO), is indeed critical for providing the appropriate treatment to the affected waterbodies. Traditionally, concentrations of SWQPs have been measured through intensive field work. Additionally, quite a lot of studies have attempted to retrieve concentrations of SWQPs from satellite images using regression-based methods. However, the relationship between SWQPs and satellite data is complex to be modelled accurately by using regression-based methods. Therefore, our study attempts to develop an artificial intelligence modelling method for mapping concentrations of both optical and non-optical SWQPs. In this context, a remote-sensing framework based on the back-propagation neural network (BPNN) is developed for the first time to quantify concentrations of different SWQPs from the Landsat8 satellite imagery. Compared to other methods, such as Support Vector Machine, significant coefficients of determination (R2) between the Landsat8 surface reflectance and concentrations of SWQPs were obtained using the developed Landsat8-based-BPNN models. The resulting R2 values were 0.991, 0.933, 0.937, 0.930, and 0.934 for turbidity, TSS, COD, BOD, and DO, respectively. Indeed, these findings indicate that the developed Landsat8-based-BPNN framework is capable of developing highly accurate models for retrieving concentrations of different SWQPs from the Landsat8 imagery. [ABSTRACT FROM AUTHOR]