[HTML][HTML] A two-step merging strategy for incorporating multi-source precipitation products and gauge observations using machine learning classification and …
H Lei, H Zhao, T Ao - Hydrology and Earth System Sciences, 2022 - hess.copernicus.org
Although many multi-source precipitation products (MSPs) with high spatiotemporal
resolution have been extensively used in water cycle research, they are still subject to …
resolution have been extensively used in water cycle research, they are still subject to …
Estimation of the visibility in Seoul, South Korea, based on particulate matter and weather data, using machine-learning algorithm
BY Kim, JW Cha, KH Chang, C Lee - Aerosol and Air Quality Research, 2022 - aaqr.org
Visibility is an important indicator of air quality and of any consequent meteorological and
climate change. Therefore, visibility in Seoul, which is the most polluted city in South Korea …
climate change. Therefore, visibility in Seoul, which is the most polluted city in South Korea …
[HTML][HTML] A D-vine copula-based quantile regression towards merging satellite precipitation products over rugged topography: a case study in the upper Tekeze–Atbara …
Precipitation is a vital key element in various studies of hydrology, flood prediction, drought
monitoring, and water resource management. The main challenge in conducting studies …
monitoring, and water resource management. The main challenge in conducting studies …
[HTML][HTML] Precipitation data merging via machine learning: Revisiting conceptual and technical aspects
The development of accurate precipitation products with wide spatio-temporal coverage is
crucial for a wide range of applications. In this context, precipitation data merging (PDM) that …
crucial for a wide range of applications. In this context, precipitation data merging (PDM) that …
[HTML][HTML] Enhancing wildfire mapping accuracy using mono-temporal Sentinel-2 data: A novel approach through qualitative and quantitative feature selection with …
Accurate wildfire severity mapping (WSM) is crucial in environmental damage assessment
and recovery strategies. Machine learning (ML) and remote sensing technologies are …
and recovery strategies. Machine learning (ML) and remote sensing technologies are …