In this study, a new methodology has been developed for a sustainable mass appraisal system. A mathematical model was created with the combination of the Cobb-Douglas and the linear regression model. With the Analytic Hierarchy Process (AHP) method, real estate value criteria were grouped and weighted in a hierarchical structure. The weights obtained with AHP were integrated into the coefficients regarding the criteria weights and densities in the Cobb-Douglas hybrid model. The new hybrid model was confirmed with the features and price equivalents of 435 parcels for sale from the market. Besides, the model analysis results were compared with the Multiple Regression Analysis (MRA) modelling using market prices. While creating the methodology, Geographic Information Systems (GIS) was used to organize the geographic and regional data of the region. After developing the new hybrid model, criteria groups that developed the model and relevant sub-criteria were evaluated using Pearson's correlation analysis.

Considering water quality is an essential requirement in terms of environmental planning and management. To protect and manage water resources effectively, it is necessary to develop an analytical decision-support system. In this study, a systematic approach was suggested to evaluate the lake water quality. The methodology includes the prediction of the values in different locations of the lakes from experimental data through inverse distance weighting (IDW) method, creation of maps by using Geographic Information System (GIS) integrated with analytic hierarchy process (AHP) from multi-criteria decision analysis (MCDA), reclassification into five class, combining the time-related spatial data into a single map to predict the whole lake water quality from the data of sampling points, and finally overlapping the final maps with topography/geology and land use. The proposed approach was verified and presented as case study for Meke and Acigol Lakes in Konya/Turkey which were affected by human and natural factors although they have ecological, hydromorphological, and socio-economic importance. In the proposed approach, categorizing water quality parameters as “hardness and minerals,” “substrates and nutrients,” “solids content,” “metals,” and “oil-grease” groups was helpful for AHP with the determined group weights of 0.484, 0.310, 0.029, and 0.046, respectively. Assigning weights within each group and then assigning weights between groups resulted in creating accurate final map. The proposed approach is flexible and applicable to any lake water quality data; even with a limited number of data, the whole lake water quality maps could be created for assessment.

Demand for parking areas has increased with the growing population and increasing number of vehicles. Large cities are suffering from a lack of parking areas, which are one of the most significant parts of the modern urban transportation system and traffic management. Locating parking areas has become a major challenge for the urban transport planners, especially in the downtown of metropolises. Geographic Information Systems (GIS) with geographic analysis tools can provide a scientific approach to determine optimum locations for parking areas. In this paper, the essential factors affecting parking site selection were considered and data sets concerning these factors were created by GIS analysis techniques. The Analytical Hierarchy Process (AHP) as a Multiple-Criteria-Decision-Analysis (MCDA) method was applied to derive weights of the selected parameters. To conduct parking demand analysis, the parking suitability map was produced by integrating the GIS with AHP. Then, suitable parking areas were determined in a zoning plan that was based on the highest suitability on the map. Other MCDA techniques including TOPSIS and VIKOR were examined and compared to determine the order of preferences among suitable parking areas. Similar to the traditional AHP method, the same results were obtained in the ranking of parking areas with the other methods. Using GIS with these MCDA techniques appears to be a usable approach for better resource allocation as well as parking site selection.

The calculation, management and maintenance of energy performance of buildings (EPBs) are significant in increasing energy efficiency in buildings and reducing greenhouse gas emissions since it is estimated that approximately one third of energy consumption is associated with buildings, and furthermore, three-quarters of the existing building stock is characterized by energy inefficiency. However, in many cases, EPBs are either not calculated or not integrated in a register within national spatial data infrastructure (NSDI). This complicates policy development and planning for both local and national governments, which may result in numerous complications. The objective of this paper is twofold: firstly, to design a building energy data model as an extension of NSDI in Türkiye and then implementing and populating it with real data taken from energy performance certificates from the Tuzla District in Istanbul; and secondly, to develop energy performance prediction models with Machine Learning (ML) algorithms (i.e., Random Forest (RF), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost) in order to estimate the overall performance scores of the buildings. The model’s findings demonstrated robust predictive accuracy, achieving an R² of 0.818 (XGBoost) and performance metrics of RMSE = 5.153, MAE = 2.886, and MAPE = 3.369. These results substantiate the model’s reliability in estimating targeted building energy performance scores. These predictions can be used to provide a comprehensive overview of districts in terms of EPB and inform the development of road maps at the district, city, or national level. Furthermore, the predictions can support the development of EPB-related legislation, facilitate the design of incentive and sanction mechanisms, and promote broader sustainability and climate mitigation goals in a practical manner. Nevertheless, as a limitation of this study, the model has only been tested in a single district, which restricts its generalizability; it should therefore be evaluated in other areas to confirm its applicability.