Enhancing GIS models for sustainable development in human settlements using intelligent IoT infrastructure and human-machine interaction

• Integration of Internet of Things (iot) and GIS: This paper introduces an innovative framework that breaks through the limitations of traditional GIS static models, combining iot devices with GIS models to achieve real-time data collection and adaptive decision-making for sustainable urban planning in human settlements. • Enhance human-computer interaction: In geographic information systems, the information of a specific building is associated with its precise location on a city or regional map. It presents an intuitive interface to enhance the engagement of stakeholders and GIS models, promoting data-driven decision-making and effective resource management for sustainable development in urban environments. • Enhance urban sustainability: This framework optimizes energy usage, waste management, and infrastructure development, and combines reinforcement learning to dynamically adjust the model. Through particle swarm optimization (PSO) and genetic algorithm (GA), energy allocation and garbage collection routes are optimized in real time to maximize resource utilization in densely populated areas. This demonstrates how GIS models supported by the Internet of Things can help establish more resilient, adaptable and sustainable urban settlements in dynamic environments. Sustainable development (SD) in human settlements (HS) can be achieved by integrating intelligent IoT infrastructure and human-machine interaction (HMI) into geographic information system (GIS) models. This study proposes a method combining real-time (RT) data and automation to address gaps in technology and urban planning. Current GIS models are ineffective due to limited RT data integration, poor urban system interaction, and lack of adaptability to dynamic changes. These shortcomings hinder sustainable urban growth, leading to suboptimal planning and resource management (RM). The proposed IoT-based framework for SD in HS (IoT-SD-HS) utilizes IoT devices and sensors to collect RT social, economic, and environmental data. This data enables adaptive decision-making (DM), smarter city planning, improved RM, and enhanced urban resilience. Intuitive HMI interfaces allow stakeholders to work effectively with the framework, facilitating data-driven decisions for urban planning and sustainability. Outcomes demonstrate significant improvements in energy consumption (EC), waste management (WM), and infrastructure development. The IoT-SD-HS model achieves high urban resilience (96.12%), WM efficiency (97.34%), and decision accuracy (98.12%), while reducing EC by 10.12%. By continuously evaluating power distribution during peak hours, smart cities reduce carbon footprints and save costs, demonstrating superior performance compared to existing methods. © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Global Science and Technology Forum Pte Ltd