Selection of the central composting facility for organic wastes through the Bayesian best worst method and Geographic information system incorporation: The case of Istanbul

This research tackles the big problem of municipal solid‑waste management in fast‑growing cities by creating a step‑by‑step method for locating central organic‑waste composting plants. The study focuses on Istanbul’s complex urban setting and combines two tools: advanced Multi‑Criteria Decision‑Making (MCDM) and Geographic Information System (GIS) spatial analysis. Because stakeholder judgments can be uncertain and biased, the Bayesian Best‑Worst Method (BWM) is used to assign weights to twenty‑eight different criteria—environmental, economic, social, and physical. These weights are then loaded into a GIS program, where a weighted linear‑combination overlay produces detailed suitability maps. The model highlights the best areas, such as the Çatalca‑Karacabey region, that cause the least ecological damage while offering

the most efficient logistics. The project gives environmental‑engineering students a clear guide for building spatial decision‑support systems. It shows a rigorous way to plan sustainable waste infrastructure, improve resource recovery, and reduce urban environmental impacts. By following this approach, researchers can see how spatial limits and multi‑criteria weights together affect the sustainability of waste‑management systems.

Message: Hi FE, I need help with “Selection of the central composting facility for organic wastes through the Bayesian best worst method and Geographic information system incorporation: The case of Istanbul” in “Environmental Engineering”