Optimal site selection for construction and demolition waste disposal using GIS-FAHP: a case study from Kafr El Sheikh City, Egypt.

The increasing number of building and demolition projects results in huge amounts of construction and demolition wastes (CDWs) that are illegally dumped. However, these wastes must be disposed of in appropriate legal sites to protect the environment and human health. After reviewing the literature, no prior research examined optimal site selection for dumping or recycling CDW in an Egyptian city. Furthermore, the absence of field surveys did not offer a holistic understanding of the specific criteria used in the model for this region, nor did it permit an assessment of the suitability of existing dumpsites, thereby revealing certain limitations in the final results. In this regard, this research aims to apply a multi-criteria geographic information system (GIS)-based framework to identify an optimal site for CDW disposal in Kafr El Sheikh City. The criteria affecting the site selection are identified and categorized from prior literature, which are further refined using field surveys and focus group to evaluate their applicability in the context of an Egyptian city. After conducting questionnaire surveys, the trapezoidal interval type II fuzzy analytic hierarchy process is applied to compute the weights of the identified criteria from the perspective of each group of experts. The entropy-based aggregation approach is employed to identify the compromise weights taking into account the preferences of different groups. GIS is a powerful tool for geoprocessing and analyzing spatial big data. The result is a scenario map for the optimal site locations with varying suitability scales (i.e., excellent, very good, good, average, poor, and very poor). The proposed methodology provides what-if scenarios based on a selected set of criteria. According to the results of the multi-criteria decision analysis models, the suitability varies based on the weights of the criteria. For the equal-weighted criteria model, the excellent category covers 5.96% of the study area, increasing to 6.48% for the weighted criteria model. These areas primarily lie in the northeast direction. Conversely, the majority of the study area, 41.80% under equal-weighted criteria and 32.39% under weighted criteria, falls within the average and poor suitability categories, respectively. In general, the most suitable areas are located on the outskirts of the city, and the suitability decreases near the central business district. To bridge the gap between research findings and practical applications, a land use analysis employing satellite imagery is conducted to pinpoint suitable locations for CDW disposal. Existing CDW dumpsites predominantly fall within the range of poor to very good for the equal-weighted criteria model, while the weighted criteria model categorizes them into the poor (16.66%) and average (83.33%) categories. The findings demonstrated the applicability of the proposed framework for CDW disposal management and planning.

Modeling construction and demolition waste quantities in Tanta City, Egypt: a synergistic approach of remote sensing, geographic information system, and hybrid fuzzy neural networks.

A waste management strategy needs accurate data on the generation rates of construction and demolition waste (CDW). The objective of this study is to provide a robust methodology for predicting CDW generation in Tanta City, one of the largest and most civilized cities in Egypt, based on socioeconomic and waste generation statistics from 1965 to 2021. The main contribution of this research involves the fusion of remote sensing and geographic information systems to construct a geographical database, which is employed using machine learning for modeling and predicting the quantities of generated waste. The land use/land cover map is determined by integrating topographic maps and remotely sensed data to extract the built-up, vacant, and agricultural areas. The application of a self-organizing fuzzy neural network (SOFNN) based on an adaptive quantum particle swarm optimization algorithm and a hierarchical pruning scheme is introduced to predict the waste quantities. The performance of the proposed models is compared against that of the FNN with error backpropagation and the group method of data handling using five evaluation measures. The results of the proposed models are satisfactory, with mean absolute percentage error (MAPE), normalized root mean square error (NRMSE), determination coefficient, Kling-Gupta efficiency, and index of agreement ranging between 0.70 and 1.56%, 0.01 and 0.03, 0.99 and 1.00, 0.99, and 1.00. Compared to other models, the proposed models reduce the MAPE and NRMSE by more than 92.90% and 90.64% based on fivefold cross-validation. The research findings are beneficial for utilizing limited data in developing effective strategies for quantifying waste generation. The simulation outcomes can be applied to monitor the urban metabolism, measure carbon emissions from the generated waste, develop waste management facilities, and build a circular economy in the study area.