Abstract Estimating gas source terms is essential and significant for managing a gas emission accident. Optimization method, as a kind of estimation methods, is helpful to figure out the source terms by solving the inverse problem. Significantly, the performance of optimization method on source term estimation is affected by the accuracy of forward dispersion model. To enhance the estimation accuracy, previous works have demonstrated the feasibility of using Back Propagation Neural Network (BPNN) trained by actual experimental datasets as a forward dispersion model. However, the overall accuracy of source estimation is still limited by backward estimation methods. Most related studies used a single optimization algorithm to estimate source terms, which usually fails to realize the requirements of both high calculation accuracy and satisfying computational efficiency. Therefore, a hybrid strategy was proposed in this study to combine optimization algorithms with different characteristics, including particle swarm optimization, genetic algorithm and simulated annealing algorithm, to not only achieve high accuracy in global searching, but also converge to a stable result efficiently. Finally, extensive experiments are conducted to testify our proposed hybrid optimization algorithms. The Skill scores of hybrid optimization algorithms decrease obviously compared to those of single optimization algorithm. Hence, the proposed hybrid strategy is potentially useful for guiding the combination of optimization algorithms for gas source terms estimation, which further contributes to deal with a gas emission accident with satisfying calculation accuracy and computational efficiency.

中文翻译：

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现场案例危险气源项估计组合不同优化算法的混合策略

摘要 估算气源项对于管理瓦斯排放事故至关重要。优化法作为一种估计方法，有助于通过求解逆问题来找出源项。重要的是，源项估计优化方法的性能受前向色散模型精度的影响。为了提高估计精度，以前的工作已经证明了使用由实际实验数据集训练的反向传播神经网络（BPNN）作为前向分散模型的可行性。然而，源估计的整体精度仍然受到后向估计方法的限制。大多数相关研究使用单一优化算法来估计源项，通常不能同时满足高计算精度和满足计算效率的要求。因此，本研究提出了一种混合策略，将粒子群优化、遗传算法和模拟退火算法等不同特性的优化算法结合起来，不仅可以实现全局搜索的高精度，还可以有效地收敛到稳定的结果。最后，进行了大量实验来证明我们提出的混合优化算法。与单一优化算法相比，混合优化算法的技能得分明显下降。因此，所提出的混合策略对于指导气源项估计的优化算法组合具有潜在的作用，