本数据集为黄河宁蒙河段巴彦高勒水文站1986-2020年流凌、封河、开河预报因子经模型求解整理形成的权重数据。本数据集共包含3个excel文件，由巴彦高勒水文站不同时期段的预报因子权重要素组成，分别是：流凌日期预报因子权重数据、封河日期预报因子权重数据和开河日期预报因子权重数据。", "ds_source": "
源数据与黄河宁蒙河段水文局工作人员协商获取。", "ds_process_way": "
中国水利水电科学研究院相关项目研究人员通过对数据进行模型求解得出。求解方法如下：\n
（1）将影响因素矩阵作为BP网络的输入向量，冰情结果作为目标输出向量，运用L-M算法训练BP神经网络，得到输入层与隐层的权值矩阵W和隐层与输出层的权值向量w。\n
（2）计算整体权值向量ω=|w|*|W|，其中ω=（ω_1，ω_2，…，ω_n）。
（3）计算各影响因素指标的直接关系矩阵B\n
（4）归一化直接关联矩阵X\n
（5）计算全关联矩阵T\n
（6）建立因果关系图。定义D为T的各行之和，R为T的各列之和。将D+R定义为指标i的中心度，其越大, 证明此指标在该影响因素体系中所占的重要程度和作用越大。将D−R定义为指标i的原因度, 可以用来区分原因组和结果组, 如果指标i的D−R大于0, 则此指标属于原因组。如果指标D−R小于0, 则此指标属于结果组。在众多影响因素中, 结果组中的影响因素是原因组中影响因素的影响结果。定义综合重要度，该值可以同时反映中心度和原因度。\n
模型出处：崔强，武春友，匡海波. BP-DEMATEL在空港竞争力影响因素识别中的应用[J]. 系统工程理论与实践，2013,33(6)：1471-1478.", "ds_quality": "
求解人员为专业人员，多次核查，数据质量良好。", "ds_acq_start_time": "1986-11-01 00:00:00", "ds_acq_end_time": "2020-03-31 00:00:00", "ds_acq_place": "黄河宁蒙河段", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 41476, "ds_files_count": 3, "ds_format": "excel", "ds_space_res": null, "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "dab07358-4cc6-4474-8589-b61fdf4f7d7f.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "c55e1118-387b-4a0c-8dc5-70aa438c430d", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "10.12072/ncdc.iceflood.db2371.2022", "subject_codes": [ "170.55" ], "quality_level": 3, "publish_time": "2022-07-30 15:31:23", "last_updated": "2025-06-30 15:58:54", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.iceflood.db2371.2022", "i18n": { "en": { "title": "Data set of forecast factor weights for river flow date, river closure date and river opening date (1986-2020)", "ds_format": "excel", "ds_source": "
The source data was obtained through consultation with the staff of the hydrology bureau of the Ningmeng reach of the Yellow River.", "ds_quality": "
The solver is a professional. It has been checked many times and the data quality is good.", "ds_ref_way": "", "ds_abstract": "
This data set is the weight data formed by the model solution and collation of the prediction factors of river flow, river closure and river opening of Bayan Gaole hydrological station in the Ningmeng section of the Yellow River from 1986 to 2020. This data set contains three Excel files, which are composed of the weight elements of the prediction factors in different periods of Bayan Gaole hydrological station, namely: the weight data of the prediction factors on the date of river flow, the weight data of the prediction factors on the date of river closure and the weight data of the prediction factors on the date of river opening.
", "ds_time_res": "年", "ds_acq_place": "Ningmeng reach of the Yellow River", "ds_space_res": "", "ds_projection": "", "ds_process_way": "The researchers of relevant projects of China Academy of water resources and hydropower research obtained the data by solving the model. The solution is as follows:\n
(1) Taking the influence factor matrix as the input vector of BP network and the ice condition result as the target output vector, BP neural network is trained by L-M algorithm to obtain the weight matrix w of input layer and hidden layer and the weight vector w of hidden layer and output layer.\n
(2) Calculate the overall weight vector ω=| W | * | w |, where ω= （ ω_ 1， ω_ 2，…， ω_ n）。\n
(3) Calculate the direct relationship matrix B of each influencing factor index\n
(4) Normalized direct correlation matrix X\n
(5) Calculate the full incidence matrix t\n
(6) Establish a cause and effect diagram. Define D as the sum of the rows of T, and R as the sum of the columns of T. D+r is defined as the centrality of index I. the larger it is, the greater the importance and role of this index in the influencing factor system. D − R is defined as the cause degree of indicator I, which can be used to distinguish the cause group and the result group. If the D − r of indicator I is greater than 0, this indicator belongs to the cause group. If the indicator D − R is less than 0, this indicator belongs to the result group. Among many influencing factors, the influencing factor in the result group is the influence result of the influencing factor in the cause group. Define the comprehensive importance degree, which can reflect the center degree and cause degree at the same time.\n
Source of model: Cui Qiang, Wu Chunyou, Kuang Haibo Application of bp-dematel in the identification of influencing factors of airport competitiveness [j] System engineering theory and practice, 2013,33 (6):1471-1478", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/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": [ "中心度", "原因度", "综合重要度" ], "ds_subject_tags": [ "水文学" ], "ds_class_tags": [], "ds_locus_tags": [ "黄河宁蒙河段" ], "ds_time_tags": [ 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "王涛", "email": "taozy163@163.com", "work_for": "中国水利水电科学研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "王涛", "email": "taozy163@163.com", "work_for": "中国水利水电科学研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "王涛", "email": "taozy163@163.com", "work_for": "中国水利水电科学研究院", "country": "中国" } ], "category": "水文" }