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38. Synergistic Application of Multiple Machine Learning Algorithms and Hyperparameter Optimization Strategies for Net Ecosystem Productivity Prediction in Southeast Asia

Author

Huang, Chaoqing, primary, Chen, Bin, additional, Sun, Chuanzhun, additional, Wang, Yuan, additional, Zhang, Junye, additional, Yang, Huan, additional, Wu, Shengbiao, additional, Tu, Peiyue, additional, Nguyen, MinhThu, additional, Hong, Song, additional, and He, Chao, additional

Published
2023
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43. Synergistic Application of Multiple Machine Learning Algorithms and Hyperparameter Optimization Strategies for Net Ecosystem Productivity Prediction in Southeast Asia.

Author

Huang, Chaoqing, Chen, Bin, Sun, Chuanzhun, Wang, Yuan, Zhang, Junye, Yang, Huan, Wu, Shengbiao, Tu, Peiyue, Nguyen, MinhThu, Hong, Song, and He, Chao

Subjects
MACHINE learning, ECOLOGICAL models, CARBON sequestration, ECOSYSTEMS, RESTORATION ecology, CLIMATE change
Abstract

The spatiotemporal patterns and shifts of net ecosystem productivity (NEP) play a pivotal role in ecological conservation and addressing climate change. For example, by quantifying the NEP information within ecosystems, we can achieve the protection and restoration of natural ecological balance. Monitoring the changes in NEP enables a more profound understanding and prediction of ecosystem alterations caused by global warming, thereby providing a scientific basis for formulating policies aimed at mitigating and adapting to climate change. The accurate prediction of NEP sheds light on the ecosystem's response to climatic variations and aids in formulating targeted carbon sequestration policies. While traditional ecological process models provide a comprehensive approach to predicting NEP, they often require extensive experimental and empirical data, increasing research costs. In contrast, machine-learning models offer a cost-effective alternative for NEP prediction; however, the delicate balance in algorithm selection and hyperparameter tuning is frequently overlooked. In our quest for the optimal prediction model, we examined a combination of four mainstream machine-learning algorithms with four hyperparameter-optimization techniques. Our analysis identified that the backpropagation neural network combined with Bayesian optimization yielded the best performance, with an R2 of 0.68 and an MSE of 1.43. Additionally, deep-learning models showcased promising potential in NEP prediction. Selecting appropriate algorithms and executing precise hyperparameter-optimization strategies are crucial for enhancing the accuracy of NEP predictions. This approach not only improves model performance but also provides us with new tools for a deeper understanding of and response to ecosystem changes induced by climate change. [ABSTRACT FROM AUTHOR]

Published
2024
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45. Digital Twin Driven Service Self-Healing with Graph Neural Networks in 6G Edge Networks

Author

Yu, Peng, Zhang, Junye, Fang, Honglin, Li, Wenjing, Feng, Lei, Zhou, Fangqin, Xiao, Pei, Guo, Song, Yu, Peng, Zhang, Junye, Fang, Honglin, Li, Wenjing, Feng, Lei, Zhou, Fangqin, Xiao, Pei, and Guo, Song

Abstract

6G edge networks strive to offer ubiquitous intelligent services, requiring a greater emphasis on network stability and reliability. However, current networks present a low automation degree of the operation, administration and maintenance process. Consequently, active service migration away from abnormal network nodes and links, as well as automatic and transparent service recovery from sudden anomalies, become challenging tasks. These conditions underscore the urgency for an innovative service self-healing mechanism for 6G edge networks. Digital twin (DT) technology uses modeling to represent physical entities, thereby facilitating lifecycle management. However, the application of DT technology in networks is still a burgeoning field of study. In this paper, we explore the DT-driven service self-healing mechanism in 6G edge networks. Initially, we design a DT-based architecture for service self-healing. Subsequently, we construct a performance prediction mechanism leveraging graph neural networks (GNNs) to devise an efficient prediction model, which aims to accurately infer network performance and promptly detect abnormal network conditions. To maintain fine-grained service stability amidst potential network anomalies, we propose a DT-driven service redeployment mechanism enhanced by GNNs. Comprehensive experimental results reveal that our proposed mechanism can accurately predict flow-level delays and identify abnormal links and nodes. Furthermore, the DT-driven service redeployment mechanism effectively reduces service delay and enhances network load balance.

Published
2023

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