With the growing severity of global climate change, forecasting and managing carbon dioxide (CO₂) emissions has become one of the critical tasks in addressing climate change. To improve the accuracy of CO₂ emission forecasting, an innovative framework based on variational mode decomposition (VMD), improved black-winged kite algorithm (IBKA), and BiLSTM networks is proposed. This framework aims to address the challenges associated with predicting non-stationary data and optimizing model hyperparameters. Initially, experiments were conducted on 29 benchmark functions using the IBKA algorithm, demonstrating its superior performance in highly nonlinear and complex environments. Subsequently, the BiLSTM model optimized by IBKA was employed to predict CO₂ emission trends across four major industries in China, confirming its enhanced prediction accuracy. Finally, a comparative analysis with other mainstream machine learning and deep learning models revealed that the BiLSTM model consistently achieved the best predictive performance across all industries. This research proposes an efficient and practical technical pathway for intelligent carbon emission prediction under the “dual-carbon” strategic goals, offering scientific support for policy formulation and the low-carbon transition.

The highly stochastic nature of rainfall presents significant challenges for the accurate prediction of its time series. To enhance the prediction performance of non-stationary rainfall time series, this study proposes a hybrid deep learning forecasting framework—VMD-IWMA-BiLSTM—that integrates Variational Mode Decomposition (VMD), Improved Whale Migration Algorithm (IWMA), and Bidirectional Long Short-Term Memory network (BiLSTM). Firstly, VMD is employed to decompose the original rainfall series into multiple modes, extracting Intrinsic Mode Functions (IMFs) with more stable frequency characteristics. Secondly, IWMA is utilized to globally optimize multiple hyperparameters of the BiLSTM model, enhancing its ability to capture complex nonlinear relationships and long-term dependencies. Finally, experimental validation is conducted using daily rainfall data from 2020 to 2024 at the Xinzheng National Meteorological Observatory. The results demonstrate that the proposed framework outperforms traditional models such as LSTM, ARIMA, SVM, and LSSVM in terms of prediction accuracy. This research provides new insights and effective technical pathways for improving rainfall time series prediction accuracy and addressing the challenges posed by high randomness.

The increasing frequency of wildfires has made large-scale collective emergency evacuation increasingly critical. However, existing studies provide limited understanding of how information structures shape the interaction between individual risk perception and collective evacuation behavior. This study develops a collective evolutionary game-based evacuation framework under ambiguous and clear information conditions. Under ambiguous information, individual heterogeneity in risk sensitivity, mobility, and resource endowment is incorporated into social interaction payoffs. Under clear information, observable evacuation consequences, including travel time, risk exposure, and congestion effects derived from route-choice interactions, are incorporated into evacuation utility. Numerical simulations examine the evolutionary characteristics of collective evacuation behavior under different information conditions and population scales. The results show that social interactions play an important role in shaping evacuation decisions under ambiguous information, while congestion effects and route-choice interactions influence evacuation utility under large-scale demand. Sensitivity analyses further demonstrate that congestion representation affects evacuation utility across different population scales. These findings highlight the importance of considering information structure, individual heterogeneity, and collective interactions in evacuation modeling. Emergency management should therefore improve risk communication, evacuation capacity, and congestion mitigation strategies. This study provides theoretical insights into collective evacuation decision-making under heterogeneous information conditions.

Metaheuristic algorithms have become effective tools for solving complex engineering optimization problems. The Parrot Optimization (PO) algorithm is a recently proposed method with promising performance; however, it still suffers from premature convergence and slow convergence when handling complex tasks. To address these issues, this paper proposes an improved variant termed the Elite Cauchy Multi-scale Multi-directional Parrot Optimization algorithm (ECMPO). The proposed ECMPO incorporates three coordinated strategies. First, an elite learning strategy is introduced before the behavioral update phase to guide the population toward promising regions and accelerate convergence. Second, after the behavioral update, individuals are adaptively processed based on their fitness: elite individuals perform a multi-scale multi-directional search to enhance local exploitation. Third, non-elite individuals undergo Cauchy mutation to increase population diversity and strengthen global exploration. These strategies enable ECMPO to achieve a better balance between exploration and exploitation. To evaluate its performance, ECMPO is examined through ablation studies on the CEC2017 benchmark functions and further compared with eleven algorithms on the CEC2020–2022 benchmark suites. Finally, it is applied to six constrained engineering design problems. The results demonstrate that ECMPO exhibits superior performance compared with competitive algorithms. ECMPO shows strong robustness and applicability in practical engineering optimization tasks.

The rapid acquisition of spatial information over affected areas is critical for emergency decision-making and disaster assessment in complex environments. Unmanned aerial vehicles (UAVs) have become an important tool for disaster information acquisition due to their rapid deployment and flexible observation capabilities. However, multi-UAV remote sensing information acquisition in complex obstacle-laden environments still faces challenges such as insufficient coverage efficiency, limited obstacle avoidance capability, and weak cooperation. This paper proposes a multi-UAV cooperative remote sensing coverage method based on an improved rotational potential field (IRPF). A disaster area model is first constructed. The artificial potential field is then enhanced by integrating separation forces and rotational guidance mechanisms to improve obstacle avoidance and information acquisition capability in complex environments, while coverage feedback is incorporated to enable dynamic observation region allocation. The experimental results show that the proposed method achieves a multi-run success rate of 100.0% over 20 independent experiments, with a final coverage rate of 93.3% in the representative scenario. The UAV system reaches the predefined 85% coverage threshold at step 106 and maintains zero collisions throughout the entire process. This method provides an effective approach for multi-UAV cooperative coverage planning in simulated complex environments, providing a potential approach for cooperative coverage planning in simulated disaster environments.