Suggested Topics within your search.
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Runoff Simulation of Yulong Kashi River Basin Based on CN05.1 Dataset Driven SWAT Model
Published 2024-09-01“…The CN05.1 dataset can accurately reflect the underlying surface and surface atmospheric characteristics of the Basin. Annual runoff in the Basin shows a positive correlation with both precipitation and temperature, whose change has a more significant impact on runoff. …”
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Analogue model of rift linkage and inversion with application to the Western Alps
Published 2023-09-01Get full text
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Simulation of Daily Rainfall from Concurrent Meteorological Parameters over Core Monsoon Region of India: A Novel Approach
Published 2018-01-01“…This approach has a potential to be used as a rain parameterization scheme in the dynamical atmospheric and coupled models to simulate daily rainfall. …”
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Li-doped C20 nanocage and its derivatives for gas sensing application: A density functional theory study
Published 2025-08-01Get full text
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Dual-Granularity Feature Alignment for Change Detection in Remote Sensing Images
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A Machine-Learning-Based Classification Method for Meteorological Conditions of Ozone Pollution
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On the Use of SuperDARN Ground Backscatter Measurements for Ionospheric Propagation Model Validation
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Impact of land-use and fecal contamination on Escherichia populations in environmental samples
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Probabilistic Short‐Term Solar Driver Forecasting With Neural Network Ensembles
Published 2024-03-01“…Abstract Space weather indices are used to drive forecasts of thermosphere density, which directly affects objects in low‐Earth orbit (LEO) through atmospheric drag force. A set of proxies and indices (drivers), F10.7, S10.7, M10.7, and Y10.7 are used as inputs by the JB2008, (https://doi.org/10.2514/6.2008‐6438) thermosphere density model. …”
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Detecting, Monitoring, and Analyzing the Surface Subsidence in the Yellow River Delta (China) Combined with CenterNet Network and SBAS-InSAR
Published 2022-01-01“…Small Baseline Subset InSAR (SBAS-InSAR) can monitor the surface subsidence with millimeter-level accuracy, but it takes a long time to process wide images (Sentinel-1) and is seriously affected by atmospheric errors. To avoid these limitations, we constructed a method combining the CenterNet network and SBAS-InSAR (CNSBAS-InSAR). …”
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