基于Hadoop的GA-BP算法在降水预测中的应用
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摘要
针对如何从海量的气象数据中挖掘出有用的知识,并提高气象预报的准确度,提出了在Hadoop平台上构建基于遗传神经网络算法的天气预报方法.该方法采用遗传算法与神经网络算法相结合,避免了传统算法容易陷入局部最优的问题,并以天津市13个台站1951–2006年的地面气候资料日值数据为基础,建立了遗传神经网络预测模型,最后以降雨量等级为决策属性进行了实验.结果表明,该方法对所有降水等级的预测准确率都要优于传统的神经网络算法,对于降水等级R0的预测精度最高,达到了87%,不仅可以有效的处理海量气象数据,同时具有较高的预测精准度和良好的扩展性,为天气预报提拱了一种全新的思路和方法.
Aiming at how to dig out useful knowledge from the massive meteorological data and improve the accuracy of meteorological forecast, this paper proposed a weather forecast method based on the genetic neural network algorithm on Hadoop platform. The method combined genetic algorithm with neural network algorithm, which could avoid the problem of local optimization in traditional algorithm. Then, the genetic neural network forecasting model is established, and the daily data of the ground climate from 1951 to 2006 of 13 stations in Tianjin is used as experimental data. Finally, the experiment is performed taking the rainfall level as decision attribute, and the results show that the method proposed in this paper can get better prediction accuracy for all rainfall level than traditional neural network algorithm. It has the highest prediction precision for the rainfall level R0 and reaches 87%, which can not only effectively deal with mass meteorological data, but also has high prediction precision and good scalability, it proposes a new way of thinking and method for weather forecast.
Aiming at how to dig out useful knowledge from the massive meteorological data and improve the accuracy of meteorological forecast, this paper proposed a weather forecast method based on the genetic neural network algorithm on Hadoop platform. The method combined genetic algorithm with neural network algorithm, which could avoid the problem of local optimization in traditional algorithm. Then, the genetic neural network forecasting model is established, and the daily data of the ground climate from 1951 to 2006 of 13 stations in Tianjin is used as experimental data. Finally, the experiment is performed taking the rainfall level as decision attribute, and the results show that the method proposed in this paper can get better prediction accuracy for all rainfall level than traditional neural network algorithm. It has the highest prediction precision for the rainfall level R0 and reaches 87%, which can not only effectively deal with mass meteorological data, but also has high prediction precision and good scalability, it proposes a new way of thinking and method for weather forecast.
引文
1李海涛,刘云生,兰长杰.基于Hadoop的生物质能源工程数据资源管理平台.计算机系统应用, 2018, 27(5):80–85.[doi:10.15888/j.cnki.csa.006341]
2 杨淑群,芮景析,冯汉中.支持向量机(SVM)方法在降水分类预测中的应用.西南农业大学学报(自然科学版),2006, 28(2):252–257.[doi:10.3969/j.issn.1673-9868.2006.02.020]
3 胡邦辉,刘善亮,席岩,等.一种Bayes降水概率预报的最优子集算法.应用气象学报, 2015, 26(2):185–192.[doi:10 .11898/1001-7313.20150206]
4 Prasad N, Kumar P, Mm N. An approach to prediction of precipitation using gini index in SLIQ decision tree.Proceedings of the 4th International Conference on Intelligent Systems, Modelling and Simulation. Bangkok,Thailand. 2013. 56–60.
5 王军,费凯,程勇.基于改进的Adaboost-BP模型在降水中的预测.计算机应用, 2017, 37(9):2689–2693.[doi:10 .11772/j.issn.1001-9081.2017.09.2689]
6 Wu JS, Long J, Liu MZ. Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm. Neurocomputing, 2015, 148:136 –142.[doi:10.1016/j.neucom.2012.10.043]
7 胡健伟,周玉良,金菊良. BP神经网络洪水预报模型在洪水预报系统中的应用.水文, 2015, 35(1):20–25.[doi:10 .3969/j.issn.1674-9405.2015.01.005]
8 赵正佳,黄洪钟,陈新.优化设计求解的遗传-神经网络新算法研究.西南交通大学学报, 2000, 35(1):65–68.[doi:10.3969/j.issn.0258-2724.2000.01.016]
9 郭强,朱若函,张晓萌.基于遗传禁忌算法优化的模糊神经网络垂直切换算法.计算机应用研究, 2016, 33(3):840 –842, 847.[doi:10.3969/j.issn.1001-3695.2016.03.045]
10 谢建宏.基于并行量子遗传神经网络的自诊断智能结构传感器的优化配置.计算机应用研究, 2012, 29(3):919 –922.[doi:10.3969/j.issn.1001-3695.2012.03.033]
11 Jajodia S, Samarati P, Sapino ML, et al. Flexible support for multiple access control policies. ACM Transactions on Database Systems, 2001, 26(2):214–260.[doi:10.1145/383891.383894]
12 金龙,吴建生,林开平,等.基于遗传算法的神经网络短期气候预测模型.高原气象, 2005, 24(6):981–987.[doi:10 .3321/j.issn:1000-0534.2005.06.019]
13 宋连春,肖风劲,李威.我国现代化气候业务现状及未来发展.应用气象学报, 2013, 24(5):513–520.[doi:10.3969/j.issn.1001-7313.2013.05.001]
14 殷长春,孙思源,高秀鹤,等.基于局部相关性约束的三维大地电磁数据和重力数据的联合反演.地球物理学报,2018, 61(1):358–367.[doi:10.6038/cjg2018K0765]
15 陈闯, Chellali R,邢尹.改进遗传算法优化BP神经网络的语音情感识别.计算机应用研究, 2019, 36(2):344–346, 361.
2 杨淑群,芮景析,冯汉中.支持向量机(SVM)方法在降水分类预测中的应用.西南农业大学学报(自然科学版),2006, 28(2):252–257.[doi:10.3969/j.issn.1673-9868.2006.02.020]
3 胡邦辉,刘善亮,席岩,等.一种Bayes降水概率预报的最优子集算法.应用气象学报, 2015, 26(2):185–192.[doi:10 .11898/1001-7313.20150206]
4 Prasad N, Kumar P, Mm N. An approach to prediction of precipitation using gini index in SLIQ decision tree.Proceedings of the 4th International Conference on Intelligent Systems, Modelling and Simulation. Bangkok,Thailand. 2013. 56–60.
5 王军,费凯,程勇.基于改进的Adaboost-BP模型在降水中的预测.计算机应用, 2017, 37(9):2689–2693.[doi:10 .11772/j.issn.1001-9081.2017.09.2689]
6 Wu JS, Long J, Liu MZ. Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm. Neurocomputing, 2015, 148:136 –142.[doi:10.1016/j.neucom.2012.10.043]
7 胡健伟,周玉良,金菊良. BP神经网络洪水预报模型在洪水预报系统中的应用.水文, 2015, 35(1):20–25.[doi:10 .3969/j.issn.1674-9405.2015.01.005]
8 赵正佳,黄洪钟,陈新.优化设计求解的遗传-神经网络新算法研究.西南交通大学学报, 2000, 35(1):65–68.[doi:10.3969/j.issn.0258-2724.2000.01.016]
9 郭强,朱若函,张晓萌.基于遗传禁忌算法优化的模糊神经网络垂直切换算法.计算机应用研究, 2016, 33(3):840 –842, 847.[doi:10.3969/j.issn.1001-3695.2016.03.045]
10 谢建宏.基于并行量子遗传神经网络的自诊断智能结构传感器的优化配置.计算机应用研究, 2012, 29(3):919 –922.[doi:10.3969/j.issn.1001-3695.2012.03.033]
11 Jajodia S, Samarati P, Sapino ML, et al. Flexible support for multiple access control policies. ACM Transactions on Database Systems, 2001, 26(2):214–260.[doi:10.1145/383891.383894]
12 金龙,吴建生,林开平,等.基于遗传算法的神经网络短期气候预测模型.高原气象, 2005, 24(6):981–987.[doi:10 .3321/j.issn:1000-0534.2005.06.019]
13 宋连春,肖风劲,李威.我国现代化气候业务现状及未来发展.应用气象学报, 2013, 24(5):513–520.[doi:10.3969/j.issn.1001-7313.2013.05.001]
14 殷长春,孙思源,高秀鹤,等.基于局部相关性约束的三维大地电磁数据和重力数据的联合反演.地球物理学报,2018, 61(1):358–367.[doi:10.6038/cjg2018K0765]
15 陈闯, Chellali R,邢尹.改进遗传算法优化BP神经网络的语音情感识别.计算机应用研究, 2019, 36(2):344–346, 361.
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