
浏览全部资源
扫码关注微信
1. 南京大学地球科学与工程学院表生地球化学教育部重点实验室
2. 江苏省生态环境评估中心(江苏省排污权登记与交易管理中心)
Published:2026
移动端阅览
党婧萱, 田涛, 李闯, et al. 基于机器学习与多源数据融合的江苏省潜水位空间分布估计[J]. Bulletin of Geological Science and Technology, 2026, 45(2): 351-360.
党婧萱, 田涛, 李闯, et al. 基于机器学习与多源数据融合的江苏省潜水位空间分布估计[J]. Bulletin of Geological Science and Technology, 2026, 45(2): 351-360. DOI: 10.19509/j.cnki.dzkq.tb20240228.
确定区域潜水位的空间分布对地下水资源管理及地下水污染防治具有重要意义。由于地下水监测井数量有限且空间分布不均,传统插值方法难以准确刻画区域尺度的潜水流场,传统回归方法难以有效捕捉不同影响因素与地下水位间的复杂非线性关系,机器学习方法在表征强非线性关系方面具有显著的优势。综合利用高程、植被覆盖度、降雨量、地表水距离、地表温度、土壤含水量等多源数据,基于机器学习方法构建区域潜水位预测模型。收集了江苏省953个枯水期潜水位实测及补充点位,机器学习模型在测试数据集的决定系数为0.91。模型预测的江苏省潜水流场,所反映的地表水-地下水补排关系与实际水文规律基本一致。选取3个典型示范场区进行验证,模型预测流向与实际相符。研究构建的深度神经网络模型可有效应用于江苏省稳态潜水位空间分布估计,为区域尺度环境影响评价、地下水监测优化提供决策支持。
马佳玉.江苏沿海地区地面沉降多尺度精细化研究[D].南京大学,2018(05).
岳冬冬.沂沭河下游沿海平原地下水化学形成演化与水文地球化学模拟[D].吉林大学,2017(09).
周健兵.文化自信自强视域下文化传承与文化创新的考向探析[J].广东教育(高中版),2025(07).
余焱,王杰,郝梦,汪兰,徐宁,石柳.热处理与离子强度对肌原纤维蛋白溶液与乳液稳定性的比较[J].现代食品科技,2025(05).
汪泉娟,孙敬锋,杨英杰,张将伟,王国梁,马恩泽,刘进,赵鑫.克里金方法与深度学习方法用于浅层地下水位估计的对比研究:以深汕特别合作区为例[J].地质科技通报,2024(04).
郑凌云,张永祥,贾瑞涛,章蓬勃.基于GMS的北京市朝阳区地下水环境数值模拟与预测分析[J].水利水电技术(中英文),2022(01).
吴鑫,孙伯明,陈菁,郭龙珠,陈丹.基于Visual MODFLOW的挠力河流域地下水数值模拟与预测分析[J].水电能源科学,2020(12).
Shao-feng Yan,Shuang-en Yu,Yu-bai Wu,De-feng Pan,Jia-gen Dong.Understanding groundwater table using a statistical model[J].Water Science and Engineering,2018(01).
霍思远,靳孟贵.不同降水及灌溉条件下的地下水入渗补给规律[J].水文地质工程地质,2015(05).
沈晔,李海涛,黎涛,李文鹏.地下水位预测:集合卡尔曼滤波(EnKF)应用概述[J].水文地质工程地质,2014(01).
梁杏,张人权,牛宏,靳孟贵,孙蓉琳.地下水流系统理论与研究方法的发展[J].地质科技情报,2012(05).
吴吉春,冯志祥,姚炳魁.江苏省地下水现代化管理研究[M].中国水利水电出版社,2016.
Elif Ozlem Yilmaz ,Hasan Tonbul ,Taskin Kavzoglu.Marine mucilage mapping with explained deep learning model using water-related spectral indices: a case study of Dardanelles Strait, Turkey[J].Stochastic Environmental Research and Risk Assessment,2024(1).
Hu Ziming,Tang Shinan,Mo Shaoxing,Shi Xiaoqing,Yin Xin,Sun Yuanyuan,Liu Xiaomin,Duan Limin,Miao Ping,Liu Tingxi,Wu Jichun.Water storage changes (2003–2020) in the Ordos Basin, China, explained by GRACE data and interpretable deep learning[J].Hydrogeology Journal,2023.
Júnez Ferreira H. E.,Hernández Hernández M. A.,Herrera G. S.,González Trinidad J.,Cappello C.,Maggio S.,De Iaco S..Assessment of changes in regional groundwater levels through spatio-temporal kriging: application to the southern Basin of Mexico aquifer system[J].Hydrogeology Journal,2023.
Rao Pinzeng,Wang Yicheng,Liu Yang,Wang Xiaoya,Hou Yukun,Pan Shibing,Wang Fang,Zhu Dongsheng.A comparison of multiple methods for mapping groundwater levels in the Mu Us Sandy Land, China[J].Journal of Hydrology: Regional Studies,2022.
Lancia Michele,Yao Yingying,Andrews Charles B.,Wang Xiaoli,Kuang Xingxing,Ni Jinren,Gorelick Steven M.,Scanlon Bridget R.,Wang Yanxin,Zheng Chunmiao.The China groundwater crisis: A mechanistic analysis with implications for global sustainability[J].Sustainable Horizons,2022.
Gonzalez Rebeca Quintero,Arsanjani Jamal Jokar.Prediction of Groundwater Level Variations in a Changing Climate: A Danish Case Study[J].ISPRS International Journal of Geo-Information,2021.
Ao Chang,Zeng Wenzhi,Wu Lifeng,Qian Long,Srivastava Amit Kumar,Gaiser Thomas.Time-delayed machine learning models for estimating groundwater depth in the Hetao Irrigation District, China[J].Agricultural Water Management,2021.
P. J. Sajil Kumar.GIS-based mapping of water-level fluctuations (WLF) and its impact on groundwater in an Agrarian District in Tamil Nadu, India[J].Environment, Development and Sustainability,2021.
Pragnaditya Malakar,Abhijit Mukherjee,Soumendra N. Bhanja,Ranjan Kumar Ray,Sudeshna Sarkar,Anwar Zahid.Machine-learning-based regional-scale groundwater level prediction using GRACE[J].Hydrogeology Journal,2021.
Fahad Alshehri,Mohamed Sultan,Sita Karki,Essam Alwagdani,Saleh Alsefry,Hassan Alharbi,Hossein Sahour,Neil Sturchio.Mapping the Distribution of Shallow Groundwater Occurrences Using Remote Sensing-Based Statistical Modeling over Southwest Saudi Arabia[J].Remote Sensing,2020.
Eric Petermann,Hanna Meyer,Madlene Nussbaum,Peter Bossew.Mapping the geogenic radon potential for Germany by machine learning[J].Science of the Total Environment,2020.
Maryam Malekzadeh,Saeid Kardar,Saeid Shabanlou.Simulation of groundwater level using MODFLOW, extreme learning machine and Wavelet-Extreme Learning Machine models[J].Groundwater for Sustainable Development,2019.
G?khan Aksu,Cem Oktay Güzeller,Mehmet Taha Eser.The Effect of the Normalization Method Used in Different Sample Sizes on the Success of Artificial Neural Network Model[J].International Journal of Assessment Tools in Education,2019(2).
Cynthia Rudin.Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead[J].Nature Machine Intelligence,2019.
Fahimeh Mirzaie-Nodoushan,Omid Bozorg-Haddad,Hugo A. Loáiciga.Optimal design of groundwater-level monitoring networks[J].Journal of Hydroinformatics,2017.
original> .Spatial estimation of water-table depth by artificial neural networks in light of ancillary data[J].Hydrological Sciences Journal,2017.
Aubin Thibaut Guekie simo,Antoine Marache,Roland Lastennet,Denys Breysse.Geostatistical investigations for suitable mapping of the water table: the Bordeaux case (France)[J].Hydrogeology Journal,2016.
Seyed Amir Naghibi,Hamid Reza Pourghasemi.A Comparative Assessment Between Three Machine Learning Models and Their Performance Comparison by Bivariate and Multivariate Statistical Methods in Groundwater Potential Mapping[J].Water Resources Management,2015.
Matthew J. Cracknell,Anya M. Reading.Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information[J].Computers and Geosciences,2014.
Diederik P. Kingma,Jimmy Ba.Adam: A Method for Stochastic Optimization.[J].CoRR,2014.
Nitish Srivastava,Geoffrey E. Hinton,Alex Krizhevsky,Ilya Sutskever,Ruslan Salakhutdinov.Dropout: a simple way to prevent neural networks from overfitting.[J].Journal of Machine Learning Research,2014.
Y. Fan,H. Li,G. Miguez-Macho.Global Patterns of Groundwater Table Depth[J].Science,2013.
Buchanan S,Triantafilis J.Mapping water table depth using geophysical and environmental variables.[J].Ground water,2009.
B. Krishna,Y. R. Satyaji Rao,T. Vijaya.Modelling groundwater levels in an urban coastal aquifer using artificial neural networks[J].Hydrological Processes,2008.
Stephanie Buechler,Gayathri Devi Mekala.Local Responses to Water Resource Degradation in India: Groundwater Farmer Innovations and the Reversal of Knowledge Flows[J].The Journal of Environment & Development,2005.
Fariborz Y Partovi,Murugan Anandarajan.Classifying inventory using an artificial neural network approach[J].Computers & Industrial Engineering,2002.
Alley W M,Taylor C J.The value of long-term ground water level monitoring.[J].Ground water,2001.
Martin Knotters,Marc F.P. Bierkens.Predicting water table depths in space and time using a regionalised time series model[J].Geoderma,2001.
P.M Cornish,R.A Vertessy.Forest age-induced changes in evapotranspiration and water yield in a eucalypt forest[J].Journal of Hydrology,2001.
Cornish P.M..The effects of logging and forest regeneration on water yields in a moist eucalypt forest in New South Wales, Australia[J].Journal of Hydrology,1993(2-4).
Menenti M.,Bastiaanssen W.G.M.,van Eick D..Determination of surface hemispherical reflectance with Thematic Mapper data[J].Remote Sensing of Environment,1989.
0
Views
0
下载量
2
CSCD
Publicity Resources
Related Articles
Related Author
Related Institution
京公网安备11010802024621