本数据集包含2016年和2020年巢湖地区堤防渗漏灾情信息，旨在为堤防渗漏风险预测与评估提供详尽的支持。数据通过遥感监测与现场调查相结合的方式采集，覆盖巢湖地区，时空分辨率为年和地点精度。数据命名采用标准化格式，结合灾情发生年份和地点，便于管理与查询。2016年数据包括巢湖市、烔炀河、裕溪河、兆河、柘皋堤防渗漏分布图、1991年与2016年受灾情况比较图、洪水淹没对比图、2016年险情统计表及庐江圩口漫溢登记表等内容。2020年数据进一步补充了灾情演变情况，包含巢湖流域险情梳理（按市和河流）、合肥市淹没情况、防汛工作简报、破圩情况统计、马鞍山险情梳理、启用和漫破圩口蓄洪情况等。该数据集具有较高的时空覆盖度，并提供了具体的地理坐标信息，可有效支持堤防渗漏风险预测、灾情预警、堤防安全监测及渗漏风险评估等领域。通过对2016年与2020年灾情数据的对比分析，本数据集为相关领域的研究、决策和灾情防治提供信息支持。", "ds_source": "
本数据集主要来源于南京水科院，涵盖2016年和2020年巢湖地区堤防渗漏灾情信息，包括灾情发生地点、规模等数据。数据通过遥感监测与现场调查相结合的方式采集，时空范围涵盖巢湖地区，时空分辨率为年和地点精度。可以充分了解该地区堤防渗漏灾害类型和种类。", "ds_process_way": "
本数据集经过清洗和标准化处理，确保数据的完整性和一致性。对堤防渗漏灾情信息进行统计和分类后，按照灾情规模、渗漏类型等维度进行分组，整理成适用于饼状图分析的数据格式。通过这些加工步骤，数据集为堤防渗漏原因的分析提供了有效支持，能够直观地展示不同因素对堤防渗漏灾情的影响。", "ds_quality": "
本数据集具有高质量的数据，提供了堤防灾害发生位置分布和详细的灾害种类信息。数据采集过程中，灾情地点通过准确的地理坐标标定，确保了位置数据的精度。同时，数据中详细记录了不同堤防灾害类型及其受灾情况，包括灾害的规模、影响范围等，确保了信息的全面性和准确性。这些高质量的数据为堤防灾害风险评估、预警系统建设和后续的应急管理提供了可靠依据。预测和分析。", "ds_acq_start_time": "2016-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "巢湖", "ds_acq_lon_east": 118.3, "ds_acq_lat_south": 31.4, "ds_acq_lon_west": null, "ds_acq_lat_north": 32.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 78077638, "ds_files_count": 20, "ds_format": "*.pdf,*.xls,*.docx", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "d9291920-58a6-465b-952b-6b5a6ae25030.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 09:51:09", "last_updated": "2025-03-28 09:51:09", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NHRI.DB6791.2025", "i18n": { "en": { "title": "Chaohu Basin Engineering Classification and Information Statistics Dataset", "ds_format": "", "ds_source": "
This dataset is primarily sourced from the Nanjing Hydraulic Research Institute and covers embankment seepage disaster information from the Chaohu region for 2016 and 2020, including data on the location and scale of the disasters. The data was collected through a combination of remote sensing monitoring and on-site surveys, covering the Chaohu area with a temporal and spatial resolution of yearly and location precision. It provides a comprehensive understanding of the types and categories of embankment seepage disasters in the region.", "ds_quality": "
This dataset contains high-quality data, providing precise distribution of embankment disaster locations and detailed information on disaster types. During the data collection process, disaster locations were marked using accurate geographic coordinates, ensuring the precision of the location data. Additionally, the dataset records in detail the various types of embankment disasters and their affected conditions, including disaster scale, impact range, and more, ensuring the comprehensiveness and accuracy of the information. This high-quality data serves as a reliable basis for embankment disaster risk assessment, early warning system development, and subsequent emergency management, supporting effective prediction and analysis.", "ds_ref_way": "", "ds_abstract": "
This dataset contains levee leakage disaster information from the Chaohu area for the years 2016 and 2020, aimed at providing comprehensive support for levee leakage risk prediction and assessment. The data was collected through a combination of remote sensing monitoring and field surveys, covering the Chaohu area with a temporal and spatial resolution at the annual and locational level. The data is named using a standardized format that includes the year and location of the disaster, facilitating management and query. The 2016 data includes levee leakage distribution maps for Chaohu City, Tongyang River, Yuxi River, Zhao River, and Zhegao Levee; comparison maps of disaster situations between 1991 and 2016; flood inundation comparison maps; the 2016 hazard statistics table; and the Lujiang floodgate overflow registration form. The 2020 data further supplements the disaster evolution, including levee hazard analysis for the Chaohu basin (by city and river), flood inundation data for Hefei City, flood control work briefs, breach statistics, hazard analysis for Ma’anshan, and overflow and flood storage data for levee breach locations. This dataset provides high temporal and spatial coverage and includes specific geographic coordinate information, effectively supporting levee leakage risk prediction, disaster early warning, levee safety monitoring, and leakage risk assessment. Through comparative analysis of the 2016 and 2020 disaster data, this dataset offers valuable information for research, decision-making, and disaster prevention in relevant fields.
", "ds_time_res": "", "ds_acq_place": "Chaohu Lake", "ds_space_res": "", "ds_projection": "", "ds_process_way": "This dataset has been cleaned and standardized to ensure data integrity and consistency. After statistical analysis and classification of the embankment seepage disaster information, the data was grouped according to dimensions such as disaster scale and seepage type, and formatted for pie chart analysis. Through these processing steps, the dataset provides effective support for analyzing the causes of embankment seepage, allowing for a clear visualization of the impact of various factors on embankment seepage disasters.", "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", "巢湖" ], "ds_time_tags": [ 2016, 2020 ], "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" }