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浏览/检索结果: 共63条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择作者升序作者降序发表日期升序发表日期降序题名升序题名降序提交时间升序提交时间降序 Simulating and predicting lake dynamics by fusing HBV modeling, machine learning approach and remote sensing data 期刊论文  OAI收割 JOURNAL OF HYDROLOGY, 2025, 卷号: 663, 页码: 134303 作者:  Naeem, Muhammad;  Zhang, Yongqiang;  Ma, Ning;  Tang, Zixuan;  Miao, Ping   |  收藏  |  浏览/下载：2/0  |  提交时间：2025/11/17 Hydrological dynamics  HBV model  Machine learning  Random Forest  CA-Markov model  Climate change  Lake area prediction  Water resource management   Modeling the Potential Invasion Risk of Ageratina adenophora in China From an Ecological Suitability Perspective 期刊论文  OAI收割 ECOLOGY AND EVOLUTION, 2025, 卷号: 15, 期号: 10, 页码: e72392 作者:  Xie, Xiaolan;  Ma, Tian;  Chen, Yu;  Zhuo, Jun;  Chen, Shuai   |  收藏  |  浏览/下载：1/0  |  提交时间：2025/11/17 Ageratina Adenophora  climate change  future prediction  human activity  invasion risk  random forest   Pooled Prediction of the Individual and Combined Impact of Extreme Climate Events on Crop Yields in China 期刊论文  OAI收割 AGRONOMY-BASEL, 2025, 卷号: 15, 期号: 10, 页码: 2319 作者:  Liu, Junjie;  Liu, Yujie;  Chen, Jie;  Shi, Zhaoyang;  Huang, Shuyuan   |  收藏  |  浏览/下载：1/0  |  提交时间：2025/12/19 extreme climate events  crop yields  pooled prediction  CMIP6  vulnerability curve assessment   Dynamic Simulation of Land Use Change and Assessment of Ecosystem Services Under Climate Change Scenarios: A Case Study of Shanghai, China 期刊论文  OAI收割 LAND, 2025, 卷号: 14, 期号: 9, 页码: 1791 作者:  Li, Yan;  Wang, Chengdong;  Sun, Mingxing;  Zhang, Hui   |  收藏  |  浏览/下载：1/0  |  提交时间：2025/11/17 ecosystem services  LUCC  climate change  scenario prediction   Assessing relative contributions of climate and socio-environmental factors to ecosystem health through space-time interpretable machine learning 期刊论文  OAI收割 ECOLOGICAL INDICATORS, 2025, 卷号: 178, 页码: 114087 作者:  Meng, Xiaoliang;  Wu, Junyi;  Xie, Yichun;  Bai, Yongfei;  Zhou, Chenghu   |  收藏  |  浏览/下载：1/0  |  提交时间：2025/11/17 Relative contributions of coupled climate and  socioenvironmental factors  Interpretable machine learning  Fine-grained spatial and temporal analysis  Each feature's contribution to individual  prediction (EFCTIP)  Localized EFCTIP   Leaf multi-dimensional stoichiometry as a robust predictor of productivity on the Tibetan Plateau 期刊论文  OAI收割 JOURNAL OF INTEGRATIVE PLANT BIOLOGY, 2025, 卷号: N/A 作者:  Li, Xin;  Zhang, Jiahui;  Rousk, Kathrin;  Zhang, Yinghua;  Jiao, Yi   |  收藏  |  浏览/下载：1/0  |  提交时间：2025/08/21 carbon cycle  climate change  machine learning  productivity prediction  stoichiometry  Tibetan Plateau   A Forecast-Then-Retrieve Framework for Short-Term Forecasting of Downward Solar Radiation Using Geostationary Satellite Data 期刊论文  OAI收割 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2025, 卷号: 63, 页码: 4112518 作者:  Zhao, Ziwei;  Liu, Ronggao;  Zhang, Xuezhen;  Zhu, Mengyao;  Tao, Zexing   |  收藏  |  浏览/下载：1/0  |  提交时间：2026/01/21 Forecasting  Accuracy  Clouds  Atmospheric modeling  Spatial resolution  Predictive models  Land surface  Green energy  Geostationary satellites  Fossil fuels  Climate change  Globalization  Downward shortwave radiation (DSR) forecasting  Himawari-8 satellite  Level 1B (L1B) radiance prediction  multimodal neural networks  spatiotemporal deep learning   In-season maize yield prediction in Northeast China: The phase-dependent benefits of assimilating climate forecast and satellite observations 期刊论文  OAI收割 AGRICULTURAL AND FOREST METEOROLOGY, 2024, 卷号: 358, 页码: 110242 作者:  Lu, Chenxi;  Leng, Guoyong;  Liao, Xiaoyong;  Tu, Haiyang;  Qiu, Jiali   |  收藏  |  浏览/下载：44/0  |  提交时间：2024/10/21 In-season maize yield prediction  Seasonal climate forecasts  Satellite-based vegetation index  Random forest  Northeast China   A novel framework for predicting glacial lake outburst debris flows in the Himalayas amidst climate change 期刊论文  OAI收割 SCIENCE OF THE TOTAL ENVIRONMENT, 2024, 卷号: 946, 页码: 13 作者:  Zhou, Bin;  Zou, Qiang;  Jiang, Hu;  Yang, Tao;  Zhou, Wentao   |  收藏  |  浏览/下载：60/0  |  提交时间：2024/08/19 Glacial lake outburst debris flow  Occurrence probability  Prediction  Climate change  Himalayas   Which riverine water quality parameters can be predicted by meteorologically-driven deep learning? 期刊论文  OAI收割 SCIENCE OF THE TOTAL ENVIRONMENT, 2024, 卷号: 946, 页码: 174357 作者:  Huang, Sheng;  Wang, Yueling;  Xia, Jun   |  收藏  |  浏览/下载：54/0  |  提交时间：2024/09/19 Water quality parameter  Deep learning  Meteorological condition  Extreme value  Climate change  Collective prediction