该数据集包含来自长江黄广大堤、安庆长江干堤等多个堤段的193组样本数据，涵盖了8个关键影响因子，包括水位高度差、覆盖层厚度、渗透系数、有效凝聚力、有效内摩擦角、干密度、孔隙比及压缩系数。\n
本数据集在影响因子在数据多样性方面具有显著优势，提供了更高的解析度和可靠性。该数据集广泛适用于堤防安全评估、防洪模型构建及机器学习算法的训练与验证，助力防洪抗灾工作的科学化和精细化管理，推动水利工程领域的研究与应用发展。", "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，通过结合灰色关联分析与遗传算法优化的深度信念网络（GA-DBN）方法，采集和分析了长江黄广大堤、安庆长江干堤等多个堤段的堤防管涌险情相关数据。", "ds_process_way": "
（1）采用灰色关联分析方法从堤防渗漏的8个影响因素中筛选出5个关键因素。灰色关联分析通过评估各影响因素与堤防渗漏之间的关联程度，确保选取的因素对预测模型具有显著的贡献。\n
（2）利用深度置信网络（Deep Belief Network, DBN）模型和结合遗传算法优化的深度置信网络（Genetic Algorithm optimized DBN, GA-DBN）模型对堤防渗漏进行预测。DBN模型通过多层非线性结构有效捕捉数据的复杂特征，而GA-DBN模型进一步结合遗传算法对网络结构和参数进行优化，提升了模型的预测精度和泛化能力。", "ds_quality": "
数据质量评价基于GA-DBN模型的训练结果表明，本数据集具有极高的质量和可靠性。在训练集上，模型实现了98.04%的准确率、98.10%的召回率、97.88%的精度以及97.98%的F1分数，这些卓越的指标反映出数据在各个影响因子上的一致性和高效性。\n
此外，在验证集上，模型更是达到了100%的准确率、召回率、精度和F1分数，进一步证明了数据集的高质量和模型的优越预测能力。这些优异的性能指标表明，数据集在影响因子的选择、数据采集与处理过程中确保了数据的完整性和准确性，能够有效支持堤防渗漏险情的精准预测分析。", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "长江下游", "ds_acq_lon_east": 117.28333333333333, "ds_acq_lat_south": 29.8, "ds_acq_lon_west": 115.4, "ds_acq_lat_north": 30.6, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 21034, "ds_files_count": 2, "ds_format": "*.xlsx", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "552f4653-b5cd-41e0-a92a-59e074f43639.png", "ds_thumb_from": 2, "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 09:55:36", "last_updated": "2025-06-30 11:40:13", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NHRI.DB6792.2025", "i18n": { "en": { "title": "193 datasets of 8 impact factors for certain sections of dykes such as Huangdang Dyke of the Yangtze River and Anqing Yangtze River Dry Dyke", "ds_format": "*.xlsx", "ds_source": "
This dataset is derived from the master's thesis, “Predictive Analysis of Binary Dike Pipe Surge Risks in Yangtze River Based on Gray Correlation and GA-DBN”. In that study, data related to dike pipe-surge hazards were collected and analyzed for several dike sections, including Huangdaodong dike of the Yangtze River and Anqing Yangtze River dry dike, by combining grey correlation analysis and genetic algorithm optimization with a deep belief network (GA-DBN) method.", "ds_quality": "
Data Quality EvaluationThe training results based on the GA-DBN model show that the present dataset is of extremely high quality and reliability. On the training set, the model achieved 98.04% accuracy, 98.10% recall, 97.88% precision, and 97.98% F1 score, which are excellent metrics reflecting the consistency and efficiency of the data on various impact factors.\n
In addition, on the validation set, the model even achieves 100% accuracy, recall, precision, and F1 score, further demonstrating the high quality of the dataset and the superior predictive power of the model. These excellent performance indicators show that the dataset ensures data completeness and accuracy during the selection of influencing factors, data collection and processing, and can effectively support the accurate prediction and analysis of embankment seepage hazards.", "ds_ref_way": "", "ds_abstract": "
This dataset contains 193 sets of sample data from several embankment sections, including Huangdaodi embankment of the Yangtze River and Anqing Yangtze River dry embankment, covering eight key influence factors, including water level height difference, cover thickness, permeability coefficient, effective cohesion, effective angle of internal friction, dry density, pore ratio and compression coefficient.\n
This dataset has significant advantages in terms of influence factors in data diversity, providing higher resolution and reliability. This dataset is widely used in embankment safety assessment, flood control model construction and the training and validation of machine learning algorithms, which helps the scientific and refined management of flood control and disaster prevention, and promotes the development of research and application in the field of water conservancy engineering.
", "ds_time_res": "", "ds_acq_place": "the lower reaches of the yangtze river", "ds_space_res": "", "ds_projection": "", "ds_process_way": "(1) Gray correlation analysis was used to screen five key factors from eight influencing factors of embankment leakage. Gray correlation analysis ensures that the selected factors contribute significantly to the prediction model by assessing the degree of association between each influencing factor and embankment leakage.\n
(2) Deep Belief Network (DBN) model and Genetic Algorithm optimized DBN (GA-DBN) model were used to predict the levee leakage, and the DBN model effectively captured the complex features of the data through the multilayer nonlinear structure. The DBN model effectively captures the complex features of 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.", "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" }