该数据集包含来自长江阳新干堤段的73组样本数据，经过灰色关联法分析，提取了5个关键影响因子：水位高度差、覆盖层厚度、渗透系数、孔隙比及压缩系数。数据经过分析后，记录了影响堤防渗漏的主要因素，为堤防安全评估、防洪模型构建及机器学习算法的训练与验证提供了重要依据。该数据集可广泛应用于水利工程领域，推动相关研究与实际应用的发展。", "ds_source": "
本数据集来源于硕士论文《基于灰色关联和GA-DBN的长江二元堤防管涌险情预测分析》。在该研究中，采用遗传算法优化的深度信念网络（GA-DBN）方法，将收集的长江阳新干堤五个堤防渗漏因素作为实际应用案例，导入模型进行分析，从而实现堤防渗漏预测模型的应用与验证。", "ds_process_way": "
（1）采用深度置信网络（Deep Belief Network, DBN）模型以及结合遗传算法优化的深度置信网络（Genetic Algorithm optimized DBN, GA-DBN）模型进行堤防渗漏预测。DBN模型通过多层非线性结构，能够有效捕捉数据中的复杂特征；而GA-DBN模型则进一步结合遗传算法对网络结构和参数进行优化，从而提升了模型的预测精度和泛化能力。\n
（2）将数据输入支持向量机（SVM）模型，并与GA-DBN模型的训练结果进行对比分析。", "ds_quality": "
基于GA-DBN模型的训练结果进行的数据质量评价表明，本数据集具有极高的质量和可靠性。在验证过程中，GA-DBN模型达到了100%的准确率、召回率、精度和F1分数，进一步验证了数据集的高质量及模型卓越的预测能力，明显优于SVM模型。这些优异的性能指标表明，在影响因子的选择、数据采集和处理过程中，数据的完整性和准确性得到了充分保障，能够有效支持堤防渗漏风险的预测和分析。", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "长江下游", "ds_acq_lon_east": 115.67, "ds_acq_lat_south": 29.92, "ds_acq_lon_west": 115.42, "ds_acq_lat_north": 30.02, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 13362, "ds_files_count": 2, "ds_format": "*.xlxs", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "c6b101ae-8bae-46fb-b05f-c849d92f3373.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "37eb642a-c117-47e4-a677-07ecffb4b8b7", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.50" ], "quality_level": 3, "publish_time": "2025-03-28 09:53:22", "last_updated": "2025-06-30 11:40:13", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NHRI.DB6793.2025", "i18n": { "en": { "title": "Data set of five influencing factors: water level difference, cover thickness, permeability coefficient, pore ratio and compression coefficient of Yangxin dry dyke of Yangtze River", "ds_format": "*.xlxs", "ds_source": "
This dataset is derived from the master's thesis, “Predictive Analysis of Pipe Surge Risks in Yangtze River Binary Embankment Based on Gray Correlation and GA-DBN”. In that study, the deep belief network (GA-DBN) method optimized by genetic algorithm was used to apply and validate the levee leakage prediction model by importing five levee leakage factors of Yangxin Dry Dike of the Yangtze River, which were collected as practical application cases, into the model for analysis.", "ds_quality": "
The data quality evaluation based on the training results of the GA-DBN model indicates that the present dataset is of extremely high quality and reliability. During the validation process, the GA-DBN model achieved 100% accuracy, recall, precision, and F1 score, which further validated the high quality of the dataset and the excellent predictive ability of the model, which was significantly better than the SVM model. These excellent performance indicators show that the integrity and accuracy of the data are fully guaranteed during the selection of influence factors, data collection and processing, and can effectively support the prediction and analysis of embankment seepage risk.", "ds_ref_way": "", "ds_abstract": "
The dataset contains 73 sets of sample data from the Yangxin dry dike section of the Yangtze River, which were analyzed by the gray correlation method to extract five key influencing factors: water level altitude difference, cover layer thickness, permeability coefficient, pore ratio, and compression coefficient. The data were analyzed to record the main factors affecting seepage of embankment, which provides an important basis for embankment safety assessment, flood control model construction, and training and validation of machine learning algorithms. The dataset can be widely used in the field of water conservancy engineering to promote the development of related research and practical applications.
", "ds_time_res": "", "ds_acq_place": "the lower reaches of the yangtze river", "ds_space_res": "", "ds_projection": "", "ds_process_way": "(1) A Deep Belief Network (DBN) model and a Genetic Algorithm optimized DBN (GA-DBN) model are used for the prediction of embankment leakage, which can effectively capture the complex features in the data through the multilayer nonlinear structure. The DBN model can effectively capture the complex features in the data, while the GA-DBN model further combines the genetic algorithm to optimize the network structure and parameters, which improves the prediction accuracy and generalization ability of the model.\n
(2) The data are input into the support vector machine (SVM) model and compared and analyzed with the training results of the GA-DBN model.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "堤防渗漏", "机器学习" ], "ds_subject_tags": [ "地质学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国", "长江下游" ], "ds_time_tags": [], "ds_contributors": [ { "true_name": "鲁明贵", "email": "1907993011@qq.com", "work_for": "南京工业大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "周彦章", "email": "yzzhou@nhri.cn", "work_for": "南京水利科学研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "鲁明贵", "email": "1907993011@qq.com", "work_for": "南京工业大学", "country": "中国" } ], "category": "其他" }