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机器学习在水文学研究中的应用
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2.
Evaluation of Various Resolution DEMs in Flood Risk Assessment and Practical Rules for Flood Mapping in Data-Scarce Geospatial Areas: A Case Study in Thessaly, Greece
不同分辨率DEM在洪水风险评估中的应用评价及数据稀缺地理空间区域洪水图绘制实用规则：以希腊塞萨利为例
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Xafoulis, N.; Kontos, Y.; Farsirotou, E.; Kotsopoulos, S.; Perifanos, K.; Alamanis, N.; Dedousis, D.; Katsifarakis, K. Evaluation of Various Resolution DEMs in Flood Risk Assessment and Practical Rules for Flood Mapping in Data-Scarce Geospatial Areas: A Case Study in Thessaly, Greece. Hydrology 2023, 10, 91.
3.
Comparison of Tree-Based Ensemble Algorithms for Merging Satellite and Earth-Observed Precipitation Data at the Daily Time Scale
日尺度上基于树的集成算法比较：用于合并卫星和地面观测的降水数据
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Papacharalampous, G.; Tyralis, H.; Doulamis, A.; Doulamis, N. Comparison of Tree-Based Ensemble Algorithms for Merging Satellite and Earth-Observed Precipitation Data at the Daily Time Scale. Hydrology 2023, 10, 50.
4.
Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid Region
将卫星图像和地面测量与机器学习模型相结合，用于监测半干旱地区的湖泊动态
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Ekpetere, K.; Abdelkader, M.; Ishaya, S.; Makwe, E.; Ekpetere, P. Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid Region. Hydrology 2023, 10, 78.
5.
Enhancing Flood Prediction Accuracy through Integration of Meteorological Parameters in River Flow Observations: A Case Study Ottawa River
通过在河流流量观测中整合气象参数来提高洪水预测的准确性：以渥太华河为例
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Letessier, C.; Cardi, J.; Dussel, A.; Ebtehaj, I.; Bonakdari, H. Enhancing Flood Prediction Accuracy through Integration of Meteorological Parameters in River Flow Observations: A Case Study Ottawa River. Hydrology 2023, 10, 164.
6.
Assessing Hydrological Simulations with Machine Learning and Statistical Models
利用机器学习和统计模型评估水文模拟
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Rozos, E. Assessing Hydrological Simulations with Machine Learning and Statistical Models. Hydrology 2023, 10, 49.
7.
A Machine-Learning Framework for Modeling and Predicting Monthly Streamflow Time Series
用于建模和预测月度流量时间序列的机器学习框架
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Dastour, H.; Hassan, Q.K. A Machine-Learning Framework for Modeling and Predicting Monthly Streamflow Time Series. Hydrology 2023, 10, 95.
8.
Differentiation of Multi-Parametric Groups of Groundwater Bodies through Discriminant Analysis and Machine Learning
通过判别分析和机器学习区分多参数地下水体群
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Mohsine, I.; Kacimi, I.; Valles, V.; Leblanc, M.; El Mahrad, B.; Dassonville, F.; Kassou, N.; Bouramtane, T.; Abraham, S.; Touiouine, A.; et al. Differentiation of Multi-Parametric Groups of Groundwater Bodies through Discriminant Analysis and Machine Learning. Hydrology 2023, 10, 230.
9.
Discerning Watershed Response to Hydroclimatic Extremes with a Deep Convolutional Residual Regressive Neural Network
利用深度卷积残差回归神经网络识别流域对水文气候极端事件的响应
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Larson, A.; Hendawi, A.; Boving, T.; Pradhanang, S.M.; Akanda, A.S. Discerning Watershed Response to Hydroclimatic Extremes with a Deep Convolutional Residual Regressive Neural Network. Hydrology 2023, 10, 116
10.
Utilizing Hybrid Machine Learning Techniques and Gridded Precipitation Data for Advanced Discharge Simulation in Under-Monitored River Basins
利用混合机器学习技术及网格化降水数据对监测不足的河流域进行高级流量模拟
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Morovati, R.; Kisi, O. Utilizing Hybrid Machine Learning Techniques and Gridded Precipitation Data for Advanced Discharge Simulation in Under-Monitored River Basins. Hydrology 2024, 11, 48.
11.
Modeling Hydrodynamic Behavior of the Ottawa River: Harnessing the Power of Numerical Simulation and Machine Learning for Enhanced Predictability
模拟渥太华河的水动力行为：利用数值模拟和机器学习增强可预测性
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Cardi, J.; Dussel, A.; Letessier, C.; Ebtehaj, I.; Gumiere, S.J.; Bonakdari, H. Modeling Hydrodynamic Behavior of the Ottawa River: Harnessing the Power of Numerical Simulation and Machine Learning for Enhanced Predictability. Hydrology 2023, 10, 177.
12.
Enhancing Monthly Streamflow Prediction Using Meteorological Factors and Machine Learning Models in the Upper Colorado River Basin
利用气象因素和机器学习模型提高科罗拉多河上游流域月流量预测
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Thota, S.; Nassar, A.; Filali Boubrahimi, S.; Hamdi, S.M.; Hosseinzadeh, P. Enhancing Monthly Streamflow Prediction Using Meteorological Factors and Machine Learning Models in the Upper Colorado River Basin. Hydrology 2024, 11, 66.
13.
A Machine Learning Approach to Map the Vulnerability of Groundwater Resources to Agricultural Contamination
机器学习方法绘制地下水资源对农业污染的脆弱性图
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Gómez-Escalonilla, V.; Martínez-Santos, P. A Machine Learning Approach to Map the Vulnerability of Groundwater Resources to Agricultural Contamination. Hydrology 2024, 11, 153.
14.
Predicting Flood Inundation after a Dike Breach Using a Long Short-Term Memory (LSTM) Neural Network
使用长短期记忆 (LSTM) 神经网络预测堤坝决口后的洪水淹没情况
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Besseling, L.S.; Bomers, A.; Hulscher, S.J.M.H. Predicting Flood Inundation after a Dike Breach Using a Long Short-Term Memory (LSTM) Neural Network. Hydrology 2024, 11, 152.
15.
Assessing Differences in Groundwater Hydrology Dynamics Between In Situ Measurements and GRACE-Derived Estimates via Machine Learning: A Test Case of Consequences for Agroecological Relationships Within the Yazoo–Mississippi Delta (USA)
通过机器学习评估现场测量与GRACE衍生估计在地下水水文动力学差异：以美国亚祖-密西西比三角洲农业生态关系影响的测试案例
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Heintzman, L.J.; Ghaffari, Z.; Awawdeh, A.R.; Barrett, D.E.; Yarbrough, L.D.; Easson, G.; Moore, M.T.; Locke, M.A.; Yasarer, H.I. Assessing Differences in Groundwater Hydrology Dynamics Between In Situ Measurements and GRACE-Derived Estimates via Machine Learning: A Test Case of Consequences for Agroecological Relationships Within the Yazoo–Mississippi Delta (USA). Hydrology 2024, 11, 186.
16.
Applications of Machine Learning and Remote Sensing in Soil and Water Conservation
机器学习和遥感技术在土壤和水资源保护中的应用
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Kim, Y.I.; Park, W.H.; Shin, Y.; Park, J.-W.; Engel, B.; Yun, Y.-J.; Jang, W.S. Applications of Machine Learning and Remote Sensing in Soil and Water Conservation. Hydrology 2024, 11, 183.
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