农业机械学报

刘东,杨丹,张亮亮,李佳民,赵丹.基于BOA-SVM模型的区域洪水灾害风险评估与驱动机制[J].农业机械学报,2023,54(10):304-315.
LIU Dong,YANG Dan,ZHANG Liangliang,LI Jiamin,ZHAO Dan.Regional Flood Disaster Risk Assessment and Driving Mechanism Based on BOA-SVM Model[J].Transactions of the Chinese Society for Agricultural Machinery,2023,54(10):304-315.

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基于BOA-SVM模型的区域洪水灾害风险评估与驱动机制 [下载全文]

Regional Flood Disaster Risk Assessment and Driving Mechanism Based on BOA-SVM Model [Download Pdf][in English]

投稿时间：2023-03-22

DOI：10.6041/j.issn.1000-1298.2023.10.030

中文关键词: 洪水灾害风险评估驱动机制蝴蝶优化算法支持向量机模型

基金项目:国家自然科学基金项目(52179008、51579044、41071053)、国家杰出青年科学基金项目(51825901)、国家自然科学基金联合基金项目(U20A20318)和清华大学水圈科学与水利工程全国重点实验室开放基金项目（sklhse-2023-A-04）

作者	单位
刘东	东北农业大学
杨丹	东北农业大学
张亮亮	东北农业大学；清华大学
李佳民	黑龙江省泥河水库管理处
赵丹	东北农业大学

中文摘要:针对区域洪水灾害风险定量评估方法精度不足问题，构建了一种基于蝴蝶优化算法改进的支持向量机模型（Butterfly optimization algorithm-support vector machine, BOA-SVM），并将其应用于黑龙江省近15年的洪水灾害风险评估与时空特征分析。结果表明：研究时段内，黑龙江省总体洪水风险水平前期升降变化明显，而后期逐渐趋于平稳，并呈现西北部高、东南部低的空间分布格局。其中，大庆地区洪水风险水平最低，绥化地区风险水平最高，其余地区风险水平随年际变化有明显下降趋势。产水模数、人均GDP、月强降水量、农林渔业总产值占比、人口自然增长率、每万人拥有卫生机构床位数、万公顷水库总库容为洪水风险变化的关键驱动因子。构建的BOA-SVM模型与传统支持向量机模型（Support vector machine, SVM）和基于帝国竞争算法改进的支持向量机模型（Imperialist competitive algorithm-support vector machine, ICA-SVM）相比，平均绝对误差（MAE）分别降低38.15%和9.18%，均方误差（MSE）分别降低58.5%和21.56%，平均绝对百分比误差（MAPE）分别降低35.23%和11.42%、决定系数（R2）分别增长0.62%和0.12%，说明BOA-SVM模型在拟合性、适配性、稳定性、可靠性以及评估精度等方面更具优势。研究成果可为洪水灾害风险评估提供一种新模型，同时可为有效调控和降低区域洪水灾害风险提供参考。

LIU Dong YANG Dan ZHANG Liangliang LI Jiamin ZHAO Dan

Northeast Agricultural University;Northeast Agricultural University；Tsinghua University;Nihe Reservoir Management Office of Heilongjiang Province

Key Words:flood disaster risk assessment driving mechanism butterfly optimization algorithm support vector machine model

Abstract:Aiming at the problem of insufficient accuracy of the regional flood disaster risk quantitative assessment method, an improved support vector machine model based on the butterfly optimization algorithm was constructed and applied to the flood disaster risk assessment and spatio-temporal characteristics analysis in Heilongjiang Province in the past 15 years. The results showed that during the study period, the overall flood risk level in Heilongjiang Province fluctuated significantly in the early stage, but gradually stabilized in the later stage, and showed a spatial distribution pattern of high in the northwest and low in the southeast. Among them, the flood risk level in the Daqing area was the lowest, the risk level in the Suihua area was the highest, and the risk level in the rest of the areas had a clear downward trend with the inter-annual variation. Water production modulus, per capita GDP, monthly strongest precipitation, proportion of total output value of agriculture, forestry and fishery, natural population growth rate, number of health care beds per 10000 people, and total storage capacity of 10000 hectares of reservoirs were the key driving factors for changes in flood risk. Compared with the traditional support vector machine model and the improved support vector machine model based on the imperialist competitive algorithm, the constructed BOA-SVM model, mean absolute error was decreased by 38.15% and 9.18%, the mean square error was decreased by 58.5% and 21.56%, the mean absolute percentage error was decreased by 35.23% and 11.42%, respectively, and the model fit was excellent. The coefficent of determination was increased by 0.62% and 0.12% respectively, indicating that the BOA-SVM model had more advantages in terms of fit, adaptability, stability, reliability and evaluation accuracy. The research results can provide a model for flood disaster risk assessment, and provide reference for effective regulation and reduction of regional flood disaster risk.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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