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Using machine learning models, remote sensing, and GIS to investigate the effects of changing climates and land uses on flood probability
The purpose of this study is to investigate the effects of climate and land use changes on
flood susceptibility areas in the Tajan watershed, Iran. To do this, land use changes over the …
flood susceptibility areas in the Tajan watershed, Iran. To do this, land use changes over the …
Evaluation of 23 gridded precipitation datasets across West Africa
This study aims reporting on 23 gridded precipitation datasets (P-datasets) reliability across
West Africa through direct comparisons with rain gauges measurement at the daily and …
West Africa through direct comparisons with rain gauges measurement at the daily and …
Merging multiple satellite-based precipitation products and gauge observations using a novel double machine learning approach
This study proposed a novel double machine learning (DML) approach to merge multiple
satellite-based precipitation products (SPPs) and gauge observations, and tested its …
satellite-based precipitation products (SPPs) and gauge observations, and tested its …
[HTML][HTML] Hydrological performance evaluation of multiple satellite precipitation products in the upper Blue Nile basin, Ethiopia
Abstract Study region Ethiopia, upper Blue Nile. Study focus This study evaluates
hydrological performance of multiple globally available precipitation products in the data …
hydrological performance of multiple globally available precipitation products in the data …
Blending multi-satellite, atmospheric reanalysis and gauge precipitation products to facilitate hydrological modelling
Satellite-retrieved and atmospheric reanalysis precipitation can bridge the spatiotemporal
gaps of in-situ gauging networks, but estimation biases can limit their reliable applications in …
gaps of in-situ gauging networks, but estimation biases can limit their reliable applications in …
[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 …
Coupling random forest and inverse distance weighting to generate climate surfaces of precipitation and temperature with multiple-covariates
Spatially interpolated temperature and precipitation are hydrological variables that are
widely applied in models of ecology, hydrology, agronomy, and other environmental …
widely applied in models of ecology, hydrology, agronomy, and other environmental …
The importance of short lag-time in the runoff forecasting model based on long short-term memory
It is still very challenging to enhance the accuracy and stability of daily runoff forecasts,
especially several days ahead, owing to the non-linearity of the forecasted processes. Here …
especially several days ahead, owing to the non-linearity of the forecasted processes. Here …
[HTML][HTML] Precipitation data merging via machine learning: revisiting conceptual and technical aspects
The development of accurate precipitation products of high spatio-temporal coverage is
crucial for a wide range of applications. In this context, precipitation data merging (PDM) …
crucial for a wide range of applications. In this context, precipitation data merging (PDM) …
Predicting river dissolved oxygen time series based on stand-alone models and hybrid wavelet-based models
Accurate prediction of dissolved oxygen time series is important for improving the water
environment and aiding water resource management. In this study, four stand-alone models …
environment and aiding water resource management. In this study, four stand-alone models …