Likelihood of Transformation to Green Infrastructure Using Ensemble Machine Learning Techniques in Jinan, China

Khansa Gulshad,Yicheng Wang,Qian Yu,Na Li,Jing Wang

doi:10.3390/land11030317

Khansa Gulshad, Yicheng Wang + Show 3 more

Open Access

https://doi.org/10.3390/land11030317

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Abstract

Rapid urbanization influences green infrastructure (GI) development in cities. The government plans to optimize GI in urban areas, which requires understanding GI spatiotemporal trends in urban areas and driving forces influencing their pattern. Traditional GIS-based methods, used to determine the greening potential of vacant land in urban areas, are incapable of predicting future scenarios based on the past trend. Therefore, we propose a heterogeneous ensemble technique to determine the spatial pattern of GI development in Jinan, China, based on driving biophysical and socioeconomic factors. Data-driven artificial neural networks (ANN) and random forests (RF) are selected as base learners, while support vector machine (SVM) is used as a meta classifier. Results showed that the stacking model ANN-RF-SVM achieved the best test accuracy (AUC 0.941) compared to the individual ANN, RF, and SVM algorithms. Land surface temperature, distance to water bodies, population density, and rainfall are found to be the most influencing factors regarding vacant land conversion to GI in Jinan.

Highlights

Urbanization brings both opportunities and challenges, such as cities enhancing the quality of life while urbanization poses a threat to the environment, e.g., excessive pollution, changes to local hydrology, and biodiversity loss
Three significant regions were identified for all maps, which will likely be converted into green infrastructure (GI), and these were grouped as A, B, and C (Figures 8–11)
These regions depict high land surface temperature (LST), are near water bodies, have high population density, and high rainfall.Region A is along the Yellow River, which comprises a large area of vacant land to be transformed into riparian vegetation

Summary

Introduction

Urbanization brings both opportunities and challenges, such as cities enhancing the quality of life while urbanization poses a threat to the environment, e.g., excessive pollution, changes to local hydrology, and biodiversity loss. The demand for gray infrastructure (GY) is expected to increase, which is associated with enormous costs. Reliance on GY will be insufficient to meet the growing demand from urbanization, compounded by the effects of climate change and energy scarcity [2]. While cities are challenged to balance urban development and its impact on the environment, green infrastructure (GI) provides opportunities to enhance the resilience of socioecological systems in urban areas. Protecting, improving, re-establishing, and increasing urban and peri-urban green infrastructure is an important measure for sustainable development of urban areas [3]

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