结合鲸鱼算法与ARMA的水压预测模型研究

Research on water pressure prediction model based on whale optimization algorithm and ARMA

各项数据表明由于对消防设备数据的监测与管理不当,导致社区火灾发生时消防水压系统处于雍疾状况而不能运行的情况频发,因此针对ARMA模型阶数难以精准确定的局限,提出并建立通过鲸鱼优化算法改进的ARMA消防水压预测模型,提高消防水压预测模型的精准度。ARMA模型有着相对较高的灵活性与精准度,且不依赖于过多的数据累计,结果表明,通过与鲸鱼优化算法(WOA)相结合,预测的准确性与稳定性进一步提高,此方法使得社区消防水压监测质量得到改善。

The data show that the fire water pressure system is in a state of paralysis and cannot run due to the improper monitoring and management of the fire equipment data when a community fire occurs.As it is difficult to accurately determine the order of the ARMA(auto-regressive and moving average)model,the ARMA fire water pressure prediction model improved by the whale optimization algorithm(WOA)is proposed and established to improve the accuracy of the fire water pressure prediction model.The ARMA model has relatively high flexibility and precision,and does not rely on excessive data accumulation.The research results show that,with the further improvement of stability,this method based on WOA has improved the quality of community fire-fighting water pressure monitoring.