Smart assessment and forecasting framework for healthy development index in urban cities

With the sustainable development being a consensus in human society, assessment of healthy development index in urban cities (HDI-UC) has been a hot concern in academia. Existing research works had proposed some assessment models from the perspective of sociology. However, these approaches can just assess HDI-UC values of current year and past years, as they relied on complete index systems. They failed to possess the ability to directly assess HDI-UC values in future years. To bridge such gap, this paper proposes a smart assessment and forecasting framework for HDI-UC. On the one hand, an assessment model proposed in a previous study is introduced as the basic model. On the other hand, the Gaussian process regression is utilized to model the evolving HDI-UC sequence, so that HDI-UC values in future years can be directly forecasted according to historical ones. A case study is conducted on real-world statistical data collected from some regions of China to illustrate assessment process of the framework. Besides, another group of experiments are also carried out to evaluate forecasting performance. The simulation results show that prediction error of SAF-HDI is around 5 %, which is within an acceptable range.

Automated summary

This paper proposes a smart assessment and forecasting framework for the healthy development index in urban cities (HDI-UC). It combines a previous assessment model with Gaussian process regression to directly forecast future HDI-UC values. The framework is validated using real-world statistical data from China, showing acceptable prediction error.