Self-Organizing Feature Zoning and Multiple Hotspots Identification of Ecosystem Services: How to Promote Ecological Refined Management of Chengdu-Chongqing Urban Agglomeration

Li, Zhigang; Yang, Fei; Zhong, Jialong; Zhao, Jingjing

doi:10.1061/(asce)up.1943-5444.0000900

Cited by 3 publications

(2 citation statements)

References 63 publications

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“…The Chengdu-Chongqing double-city economic circle has become a major strategy for national regional development, taking Chengdu and Chongqing as the core cities to drive the coordinated development of Chengdu-Chongqing urban agglomeration (CY) [20,21]. However, in the process of economic and urbanization development, CY has also accumulated some contradictions and problems, such as urban sprawl [22] and serious environmental pollution [23], which have greatly impacted the original ecological and spatial structure of the city and caused the decline of ecological functions.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning in Ecological Red Line Identification: A Case Study of Chengdu–Chongqing Urban Agglomeration

Deng,

Xie,

Wei

et al. 2024

Diversity

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China’s Ecological Protection Red Lines (ERLs) policy has proven effective in constructing regional ecological security patterns and protecting ecological space. However, the existing methods for the identification of high conservation value areas (HCVAs) usually use physical models, whose parameters and processes are complex and only for a single service, affecting the ERL delineation. In this study, the data-driven machine learning (ML) models were innovatively applied to construct a framework for ERL identification. First, the One-Class Support Vector Machine (OC-SVM) was used to generate negative samples from natural reserves and ecological factors. Second, the supervised ML models were applied to predict the HCVAs by using samples. Third, by applying the same ecological factors, the traditional physical models were used to assess the ecological services of the study area for reference and comparison. Take Chengdu–Chongqing Urban Agglomeration (CY) as a case study, wherein data from 11 factors and 1822 nature reserve samples were prepared for feasibility verification of the proposed framework. The results showed that the area under the receiver operating characteristic curve (AUC) of all ML models was more than 97%, and random forest (RF) achieved the best performance at 99.57%. Furthermore, the land cover had great contributions to the HCVAs prediction, which is consistent with the land use pattern of CY. High-value areas are distributed in the surrounding mountains of CY, with lush vegetation. All of the above results indicated that the proposed framework can accurately identify HCVAs, and that it is more suitable and simpler than the traditional physical model. It can help improve the effectiveness of ERL delimitation and promote the implementation of ERL policies.

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Section: Introductionmentioning

confidence: 99%

Application of Machine Learning in Ecological Red Line Identification: A Case Study of Chengdu–Chongqing Urban Agglomeration

Deng,

Xie,

Wei

et al. 2024

Diversity

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“…This is achieved without explicit external commands or intervention, instead relying on inherent feedback mechanisms. In the context of aerospace, this suggests a spacecraft control system that can adapt in real-time, revising its control strategies based on immediate environmental feedback and system states [8][9][10]. When fused with the robustness of structurally stable mappings, the outcome is a control system that promises superior adaptability and resilience.…”

Section: Introductionmentioning

confidence: 99%

Self-Organizing Control Systems for Nonlinear Spacecraft in the Class of Structurally Stable Mappings

Abdiramanov,

Taiman,

Beisenbi

et al. 2023

IJACSA

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In recent developments within the domain of aerospace engineering, there is a burgeoning interest in the autonomous control of nonlinear spacecraft using advanced methodologies. The present research delves deep into the realm of self-organizing control systems tailored for such nonlinear spacecraft, emphasizing its application within the framework of structurally stable mappings. By harnessing the inherent characteristics of structurally stable mappingsoften renowned for their resilience to minor perturbations and local modificationsthis research endeavors to design a control mechanism that mitigates the challenges presented by the intrinsic nonlinearity of spacecraft dynamics. Initial findings suggest a commendable enhancement in spacecraft maneuverability and robustness against unforeseen disturbances. Furthermore, the employment of self-organization principles leads to an adaptive and resilient system that can reconfigure its control strategies in real-time, basing decisions on immediate environmental feedback. This adaptability, in essence, mimics biological systems that evolve and adapt in the face of challenges. Such a breakthrough in nonlinear spacecraft control not only widens the horizons for space exploration by making missions safer and more efficient but also contributes foundational knowledge to the broader field of nonlinear dynamic system controls. Researchers and practitioners are encouraged to explore this synergistic combination of self-organization and structurally stable mappings to further harness its potential in diverse arenas beyond aerospace.

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Fine Management of Drug Regulatory Informatization Projects

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2024

MSE

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Self-Organizing Feature Zoning and Multiple Hotspots Identification of Ecosystem Services: How to Promote Ecological Refined Management of Chengdu-Chongqing Urban Agglomeration

Cited by 3 publications

References 63 publications

Application of Machine Learning in Ecological Red Line Identification: A Case Study of Chengdu–Chongqing Urban Agglomeration

Application of Machine Learning in Ecological Red Line Identification: A Case Study of Chengdu–Chongqing Urban Agglomeration

Self-Organizing Control Systems for Nonlinear Spacecraft in the Class of Structurally Stable Mappings

Fine Management of Drug Regulatory Informatization Projects

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