摘要
针对洪水峰高量大、汇流时间短以及流域地貌复杂,导致洪水预报难度大和预报精度不理想的问题,提出一种基于深度信念极限学习机(DBN-ELM)和改进卷积优化算法(ICOA)的ICOA-DBN-ELM模型。以渭河上游北道水文站点2006~2020年的日径流数据作为输入数据,并将该模型与BP、ELM、DBN-BP、DBN-ELM、COA-DBN-ELM模型进行对比。结果表明,所建立的ICOA-DBN-ELM模型有更好的预报精度,在洪水预报领域具有良好的应用前景。
The ICOA-DBN-ELM model based on deep belief network(DBN),extreme learning machine(ELM)and improved convolution optimization algorithm(ICOA)is proposed to solve the problems of flood prediction difficulty and unsatisfactory accuracy caused by large flood peak,short convolution time and complex basin topography.The daily runoff data of the Beidao hydrological station in the upper reaches of the Wei River from 2006 to 2020 were used as input data,and the model was compared with BP,ELM,DBN-BP,DBN-ELM and COA-DBN-ELM models.The results show that the established ICOA-DBN-ELM model has better prediction accuracy,and has a good application prospect in the field of flood prediction.
作者
徐军杨
张奇伟
蔡鹏
罗远林
张坚
张楚
XU Jun-yang;ZHANG Qi-wei;CAI Peng;LUO Yuan-lin;ZHANG Jian;ZHANG Chu(PowerChina Huadong Engineering Corporation Limited,Hangzhou 311122,China;Kunshan Water Bureau,Suzhou 215131,China;Faculty of Automation,Huaiyin Institute of Technology,Huai’an 223003,China)
出处
《水电能源科学》
北大核心
2024年第8期48-52,共5页
Water Resources and Power
基金
国家自然科学基金项目(62303191)
江苏省自然科学基金项目(BK20191052)
江苏省高校自然科学基金面上项目(23KJD480001)
江苏省双创计划(JSSCBS20201037)。
关键词
洪水预报
深度信念极限学习机
参数优化
卷积优化算法
flood forecasting
deep belief extreme learning machine
parameter optimization
convolution optimization algorithm