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Commission VI, WG VI/4 KEY WORDS: Uncertainty, Spatial Data Quality, Modelling, Urban simulation models, CA, Hydrological models, SWAT, Error propagation ABSTRACT:Data quality for GIS processing and analysis is becoming an increased concern due to the accelerated application of GIS technology for problem solving and decision making roles. Uncertainty in the geographic representation of the real world arises as these representations are incomplete. Identification of the sources of these uncertainties and the ways in which they operate in GIS based representations become crucial in any spatial data representation and geospatial analysis applied to any field of application. This paper reviews the articles on the various components of spatial data quality and various uncertainties inherent in them and special focus is paid to two fields of application such as Urban Simulation and Hydrological Modelling. Urban growth is a complicated process involving the spatio-temporal changes of all socio-economic and physical components at different scales. Cellular Automata (CA) model is one of the simulation models, which randomly selects potential cells for urbanisation and the transition rules evaluate the properties of the cell and its neighbour. Uncertainty arising from CA modelling is assessed mainly using sensitivity analysis including Monte Carlo simulation method. Likewise, the importance of hydrological uncertainty analysis has been emphasized in recent years and there is an urgent need to incorporate uncertainty estimation into water resources assessment procedures. The Soil and Water Assessment Tool (SWAT) is a continuous time watershed model to evaluate various impacts of land use management and climate on hydrology and water quality. Hydrological model uncertainties using SWAT model are dealt primarily by Generalized Likelihood Uncertainty Estimation (GLUE) method.

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Urban Growth Models (UGMs) are very essential for a sustainable development of a city as they predict the future urbanization based on the present scenario. Neural Network based Cellular Automata models have proved to predict the urban growth more close to reality. Recently, deep learning based techniques are being used for the prediction of urban growth. In this current study, urban growth of Chennai Metropolitan Area (CMA) of 2017 was predicted using Neural Network based Cellular Automata (NN-CA) model and Deep belief based Cellular Automata (DB-CA) model using 2010 and 2013 urban maps. Since the study area experienced congested type of urban growth, "Existing Built-Up" of 2013 alone was used as the agent of urbanization to predict urban growth in 2017. Upon validating, DB-CA model proved to be the better model, as it predicted 524.14 km 2 of the study area as urban with higher accuracy (kappa coefficient: 0.73) when compared to NN-CA model which predicted only 502.42 km 2 as urban (kappa coefficient: 0.71), while the observed urban cover of CMA in 2017 was 572.11 km 2. This study also aimed at analyzing the effects of different types of neighbourhood configurations (Rectangular: 3 × 3, 5 × 5, 7 × 7 and Circular: 3 × 3) on the prediction output based on DB-CA model. To understand the direction and type of the urban growth, the study area was divided into five distance based zones with the State Secretariat as the center and entropy values were calculated for the zones. Results reveal that Chennai Corporation and its periphery experience congested urbanization whereas areas away from the Corporation boundary follow dispersed type of urban growth in 2017. How to cite this paper: Aarthi, A.D. and Gnanappazham, L.

Municipal solid waste (MSW) management has emerged as one of the major environmental challenges globally. The consequences of inappropriate waste management are manifold and the trend would continue if immediate interventions are not taken for its reversion amid rapid urbanization and current consumption patterns of individuals. The concept of circular economy (CE) can contribute to a paradigm shift in the transformation of the traditional linear approach that does not favour reuse, recycle, recovery concept. Modern and proven waste management practices with collection systems, recycling facilities, sanitary landfills, and waste-to-energy (WtE) and nutrient recovery offer opportunities to improve urban environment through the valorization of waste and by-products in a CE. This study scrutinizes the existing literature on the assessment of circularity and helps to develop a unified circularity framework in the management of MSW in cities. Key aspects such as tools for measuring circularity, nexus and trade-offs, and conditions in promoting CE are discussed. Finally, this paper elucidates the need for circularity, including enablers and inhibitors for promoting circularity in the management of MSW with a case study in the city of Curitiba, Brazil.