Nyström least-squares collocation for regional geoid modeling with a large dataset - Xun Zhang; Guobin Chang; Hefang Bian; Xiannan Han; Houpu Li (Earth Science Informatics, 2026)

DPA-MSCL: A multi-scale supervised contrastive learning method based on dynamic prototype alignment for lithology identification - Xianshan Li; Saiwei Sun; Yao Wang; Ning Shi; Pengwei Zhang; Chao Han; Yurun Shao; Hongjia Ren; Fangfang Pan; Fengda Zhao (Earth Science Informatics, 2026)

Single-BDS GNSS-R water surface altimetry and performance analysis - Peiyuan Wang; Junqiang Han; Fang Cheng; Pengfei Zhang; Xiaolei Wang; Rui Tu; Weisheng Wang; Majid Gulayozov; Yaoming Li; Xiaochun Lu (Earth Science Informatics, 2026)

A novel reasoning random forest imputation algorithm for recovering incomplete datasets in geological hazard prediction - Jiahao Sun; Diyuan Li; Zhenyu Han; Lianku Xie (Earth Science Informatics, 2026)

A multi-dimensional modified deep learning framework for predicting potential creep-type landslides based on geological data - Yue Liu; Peihua Xu; Chen Cao; Liling Jin; Bo Han; Rongchen Ma; Chensi Li (Earth Science Informatics, 2026)

Robust automated identification of diatom microfossils under severe data scarcity via deep ensemble learning - Yun Chang Choi; Tae Hyung Kim; Jungjae Park; Jiwoo Han (Earth Science Informatics, 2026)

Study on the application of knowledge graph and large language model in question answering on water conservancy knowledge field - Xubo Han; Bing Liu; Zhengyu Yang; Wenzhe Tang (Earth Science Informatics, 2026)

Analysis of slope instability factors: An application study of a novel interpretable ensemble model - Shunchuan Wu; Xiaolong Wang; Longqiang Han; Pengbin He; Jingqi Cui; Haokai Shen; Zhuoqing Li (Earth Science Informatics, 2025)

Global to local: a novel encoder-decoder framework for urban real-time rainfall-runoff forecasting - Yuan Tian; Wenlong Fu; Yajing Dong; Qi Han; Qian Xiong; Ruonan Cui; Zhenyu Ao; Xinyu Lei (Earth Science Informatics, 2025)

A refined hybrid method for 3D quaternary geological modeling and its application in coastal deposits - Manqing Hou; Yabin Ye; Liang He; Baoji Han; Liwen Yang; Amin Luo (Earth Science Informatics, 2025)
Refined 3D Quaternary geological modeling can reveal the spatial and geological structures of strata from a 3D perspective, which is an essential subject in current Quaternary geology and 3D GIS research. This research addresses the critical challenge posed by the significant discontinuity and heterogeneity of Quaternary strata, which result from multiple depositional events and lead to complex stratigraphic structures that are difficult to model accurately. To tackle this challenge, we developed the mDEM-GTP-TEN hybrid modeling method, integrating multi-layer digital elevation model (mDEM), generalized prism (GTP), and tetrahedral meshes (TEN) to achieve refined 3D models of Quaternary strata. This method incorporates virtual borehole data to generate multi-layer DEMs, constructs GTPs through vertical connectivity, and discretizes them into TENs, which serve as the fundamental voxels for 3D geological modeling. Additionally, validation tests conducted using geological parameters specific to Nantong City, a coastal region in eastern China, demonstrated the model's high accuracy. The results demonstrated an average error of 3.05 m for strata burial depth and 2.42 m for layer thickness, accounting for only 7.3% of the average layer thickness, confirming the model's effectiveness, reliability and accuracy in reflecting the spatial structure and geological architecture of Quaternary strata. The mDEM-GTP-TEN hybrid modeling method provides a robust theoretical and technical foundation for studying and mitigating geological hazards such as landslides, debris flows, and ground subsidence. By offering a more accurate representation of Quaternary strata, this model enhances our ability to predict and manage geological risks, thereby contributing to the advancement of Quaternary geology and 3D GIS applications.

Fine-tuned BERT-BiLSTM-CRF approach for named entity recognition in geological disaster texts - Yange Li; Li Luo; XinRui Zeng; Zheng Han (Earth Science Informatics, 2025)

Indispensable factors in landslide susceptibility modeling: the critical role of slope unit quantity-sensitivity - Rui Liu; Jialiang Han; Juncheng Gou; Kai Cao; Xin Pan; Decheng Wang (Earth Science Informatics, 2025)

Multi-scenario carbon storage analysis based on PLUS model and InVEST model: a case study of Zhejiang province, China - Yirong Wang; Xueqin Jiang; Song Gao; Qi Jiang; Huaqiang Du; Ning Han (Earth Science Informatics, 2025)

The spatial coordinates projection method for generating digital geologic cross sections from the multi-source terrain and geologic data - Xingwei Wang; Ran Wang; Yonghui Xin; Haidong Zhang; Xin Han; Wanshan Lei; Gangfeng Wei (Earth Science Informatics, 2023)

Crystal-modeler: A tool for geometric analysis and three-dimensional modeling of crystal forms based on rectangular coordinates in space - Huixia Chao; Yue Li; Weihua Yao; Xin Han; Ran Wang (Earth Science Informatics, 2023)

Research on fish identification in tropical waters under unconstrained environment based on transfer learning - Shan Zhang; Weifang Liu; Yupeng Zhu; Wei Han; Yi Huang; Jianqiang Li (Earth Science Informatics, 2022)
Seafloor unconstrained environment video is shot in uncontrolled open sea area. There are multiple backgrounds, complex illumination and weather changes, and rapid growth of algae and attached lenses, which affect the stability of video shooting quality, resulting in difficulty in image recognition. At present, there is still no algorithm that is superior to other methods generally, and it is necessary to build a model combined with specific scenes and applications. In this paper, a fish identification method in tropical waters based on transfer learning under unconstrained environment is proposed. Firstly, the image is pre-processed by affine transformation to realize data enhancement. Furthermore, RestNet50 deep convolutional neural network is constructed based on transfer learning to compare the effect of fish recognition before and after transfer learning. The results show that, the accuracy and loss indicators are better than those of non-transfer learning when the trained model of imagenet is introduced as the initial weight of the network. When the model is trained to 150 epochs, the indicators begin to converge, which can better complete the fish identification task in tropical waters under unconstrained environment.

Deep learning algorithms for prediction of PM10 dynamics in urban and rural areas of Korea - Han-Soo Choi; Kyungmin Song; Myungjoo Kang; Yongcheol Kim; Kang-Kun Lee; Hanna Choi (Earth Science Informatics, 2022)

Spatial–temporal evolution pattern and prediction analysis of flood disasters in China in recent 500 years - Cuixia Wei; Bing Guo; Hailing Zhang; Baomin Han; Xiangshen Li; Huihui Zhao; Yuefeng Lu; Chao Meng; Xiangzhi Huang; Wenqian Zang; Hongwei Wu (Earth Science Informatics, 2022)

Correction to: What are the dominant influencing factors on the soil erosion evolution process in the Yellow River Basin? - Hongwei Wu; Bing Guo; Haorun Xue; Wenqian Zang; Baomin Han; Fei Yang; Yuefeng Lu; Cuixia Wei (Earth Science Informatics, 2021)

What are the dominant influencing factors on the soil erosion evolution process in the Yellow River Basin? - Hongwei Wu; Bing Guo; Haorun Xue; Wenqian Zang; Baomin Han; Fei Yang; Yuefeng Lu; Cuixia Wei (Earth Science Informatics, 2021)

Retrieval and analysis of land surface temperature in permafrost regions in Northeast China based on AMSR2 data - Han Yin; Miao Li; Haoran Man; Xingfeng Dong; Boquan Lu; Shuying Zang (Earth Science Informatics, 2021)

New variogram modeling method using MGGP and SVR - Changik Han; Jiyang Wang; Mingguo Zheng; Ende Wang; Jianming Xia; GwangSu Li; Sunchol Choe (Earth Science Informatics, 2016)

Delivery of agricultural drought information via web services - Chunming Peng; Meixia Deng; Liping Di; Weiguo Han (Earth Science Informatics, 2015)

Predicting streamflows to a multipurpose reservoir using artificial neural networks and regression techniques - Muhammad Hassan; Muhammad Ali Shamim; Hashim Nisar Hashmi; Syed Zishan Ashiq; Imtiaz Ahmed; Ghufran Ahmed Pasha; Usman Ali Naeem; Abdul Razzaq Ghumman; Dawei Han (Earth Science Informatics, 2015)

Service-oriented approach for geospatial feature discovery - Peng Yue; Liping Di; Weiguo Han; Peisheng Zhao; Wenli Yang; Lianlian He (Earth Science Informatics, 2012)