水力发电2024，Vol.50Issue(7)：17-23,78,8.
基于超参数优化的极限学习机区域水资源短缺风险评价
Risk Assessment of Regional Water Resource Shortage Using Extreme Learning Machine Based on Hyperparameter Optimization
摘要
Abstract
In order to evaluate the risk level of regional water shortage scientifically and improve the performance of Extreme Learning Machine(ELM),two water shortage risk assessment models combining Crystal Structure Algorithm(CryStAl)and Pelican Optimization Algorithm(POA)with ELM is proposed,respectively.The model is validated through an example of water shortage risk assessment in Yunnan Province.Firstly,the principles of CryStAl and POA are briefly introduced,and the simulation tests on them are conducted through four standard functions.Secondly,the risk assessment index system and level standards for water resource shortage are established,the samples are generated using linear interpolation and random selection methods,and an ELM hyperparameter optimization fitness function is constructed.Finally,the CryStAl and POA are used to optimize the fitness function,and the optimal ELM hyperparameters obtained from the optimization are used to establish CryStAl-ELM and POA-ELM models to evaluate the annual water shortage risks.The results are compared with the evaluation results of fuzzy comprehensive evaluation method,CryStAl-SVM,POA-SVM,ELM and SVM models.The results show that:(a)the CryStAl and POA have good optimization accuracy and global search ability;(b)the average absolute percentage error(MAPE)of the CryStAl-ELM and POA-ELM in evaluating test samples are 0.077％and 0.083％,respectively,the evaluation accuracy is more than 57.7％higher than that of CryStAl-SVM and POA-SVM,and more than 83.5％higher than that of SELM and SVM;and(c)the CryStAl and POA can effectively optimize ELM hyperparameters and improve ELM prediction performance.The evaluation results of CryStAl-ELM and POA-ELM models indicate that:(a)the water resource shortage risk in Yunnan Province is as higher-level between 2006 to 2008,medium-level between 2009 to 2012,lower-level between 2013 to 2019 and low-level between 2020 to 2025;and(b)in the past 15 years,the risk level of water resource shortage in Yunnan Province has shown a downward trend,and the downward trend is significant.关键词
水资源短缺/风险等级/极限学习机/晶体结构算法/鹈鹕优化算法/仿真测试/云南省Key words
water shortage/risk level/Extreme Learning Machine/Crystal Structure Algorithm/Pelican Optimization Algorithm/simulation test/Yunnan Province分类
建筑与水利引用本文复制引用
程刚,刀海娅,崔东文..基于超参数优化的极限学习机区域水资源短缺风险评价[J].水力发电,2024,50(7):17-23,78,8.基金项目
国家自然科学基金资助项目(41702278) （41702278）
国家重点研发计划项目(2019YFC0507500) （2019YFC0507500）
中国地质调查局地质调查项目(DD20221758、DD20190326) （DD20221758、DD20190326）
