Deep Learning-Based Risk Analysis and Prediction During the Implementation of Carbon Neutrality Goals
Year: 2024
Type: article
Abstract: Risk prediction has become increasingly crucial in today's complex and dynamic environments. However, existing forecasting methods still face challenges in terms of accuracy and reliability. Therefore, it is imperative to explore new approaches to better address risks. In response to this need, our study introduces an innovative risk prediction model known as WOA-FPALSTM. What sets this model apart is its seamless integration of deep learning and heuristic algorithms, designed to overcome the limitations of existing approaches. The core component of deep learning, LSTM, excels in sequence modeling by capturing long-term and short-term dependencies in time series data, thereby enhancing the model's ability to model temporal data. Meanwhile, the heuristic algorithm, WOA (Whale Optimization Algorithm), equips our model with global search capabilities, facilitating the discovery of optimal model configurations and significantly improving predictive performance.
DOI: 10.4018/joeuc.364100
Authors: Huizhen Long, Meng Li, Zhen Dong, Yuan Meng, Fengrui Zhang
Language: English
Field: Decision Sciences
Domain: Social Sciences
SDG: Climate action
Open Access status: diamond
OpenAlex ID: W4405396131