使用改进的ANFIS模型进行洪水排放预测,并结合混合粒子群集优化和粘液模具算法
Sandeep Samantaray1, Pratik Sahoo2, Abinash Sahoo2
1Department of Civil Engineering, NIT Srinagar, Jammu and Kashmir, India. samantaraysandeep963@gmail.com.
概括
一个新的混合人工智能 (AI) 模型,ANFIS-PSOSMA,准确地预测河流洪水排放. 这种先进的洪水预测系统集成了优化算法,以提高河流洪水预测的准确性.
科学领域:
- 环境科学 环境科学
- 水文学的水文学
- 人工智能的人工智能
背景情况:
- 洪水危险带来了重大的社会经济风险,需要准确的预测方法.
- 人工智能 (AI) 模型为模拟洪水事件和提高预测准确性提供了具有成本效益的解决方案.
研究的目的:
- 为预测河流洪水排放 (QFD) 开发一种新的联合模型.
- 通过将其与颗粒群集优化 (PSO) 集成,提高模算法 (SMA) 的勘探能力.
主要方法:
- 通过将自适应神经模糊推理系统 (ANFIS) 与PSO和SMA结合起来,开发了一种混合的元启发算法ANFIS-PSOSMA.
- 该模型的性能使用统计指标进行了评估:相关系数 (R2),纳什-萨特克利夫模型效率 (NSE),根平均平方误差 (RMSE) 和平均绝对误差 (MAE).
- 来自印度奥迪沙邦布拉马尼河的四个测量站的数据被利用.
主要成果:
- 在训练过程中,ANFIS-PSOSMA模型表现出高性能,在训练过程中达到0.9952的NSE和0.9946的R2.
- 杰纳普尔测量站的测试结果显示,NSE为0.9736,R2为0.9731,RMSE为8.4236,MAE为4.3197. 这些测试结果显示,NSE为0.9736,R2为0.9731,RMSE为8.4236,MAE为4.3197.
- 优化算法与ANFIS的集成显著提高了其在模拟每月洪水排放时间序列中的性能.
结论:
- 开发的ANFIS-PSOSMA模型显示了准确的河流洪水排放预测的巨大潜力.
- 将PSO和SMA等优化算法与ANFIS结合起来,可以提高洪水预测能力.
- 该研究强调了综合人工智能方法在应对洪水预测挑战方面的有效性.
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