[HTML][HTML] Exploding the myths: An introduction to artificial neural networks for prediction and forecasting
Abstract Artificial Neural Networks (ANNs), sometimes also called models for deep learning,
are used extensively for the prediction of a range of environmental variables. While the …
are used extensively for the prediction of a range of environmental variables. While the …
Review of soil and water assessment tool (SWAT) applications in Brazil: Challenges and prospects
D de Almeida Bressiani, PW Gassman… - International Journal of …, 2015 - ijabe.org
The geographical extent of Brazil exceeds 8.5 million km2 and encompasses a complex mix
of biomes and other environmental conditions. Multiple decision support tools are needed to …
of biomes and other environmental conditions. Multiple decision support tools are needed to …
[HTML][HTML] Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets
Regional rainfall–runoff modeling is an old but still mostly outstanding problem in the
hydrological sciences. The problem currently is that traditional hydrological models degrade …
hydrological sciences. The problem currently is that traditional hydrological models degrade …
Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling
We undertook a comprehensive evaluation of 22 gridded (quasi-) global (sub-) daily
precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P …
precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P …
Time to update the split‐sample approach in hydrological model calibration
Abstract Model calibration and validation are critical in hydrological model robustness
assessment. Unfortunately, the commonly used split‐sample test (SST) framework for data …
assessment. Unfortunately, the commonly used split‐sample test (SST) framework for data …
The CAMELS data set: catchment attributes and meteorology for large-sample studies
We present a new data set of attributes for 671 catchments in the contiguous United States
(CONUS) minimally impacted by human activities. This complements the daily time series of …
(CONUS) minimally impacted by human activities. This complements the daily time series of …
Caravan-A global community dataset for large-sample hydrology
High-quality datasets are essential to support hydrological science and modeling. Several
CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) datasets exist …
CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) datasets exist …
[HTML][HTML] Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual …
Long short-term memory (LSTM) models are recurrent neural networks from the field of deep
learning (DL) which have shown promise for time series modelling, especially in conditions …
learning (DL) which have shown promise for time series modelling, especially in conditions …
[HTML][HTML] The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies–Chile dataset
C Alvarez-Garreton, PA Mendoza… - Hydrology and Earth …, 2018 - hess.copernicus.org
We introduce the first catchment dataset for large sample studies in Chile. This dataset
includes 516 catchments; it covers particularly wide latitude (17.8 to 55.0∘ S) and elevation …
includes 516 catchments; it covers particularly wide latitude (17.8 to 55.0∘ S) and elevation …
Global‐scale regionalization of hydrologic model parameters
Current state‐of‐the‐art models typically applied at continental to global scales (hereafter
called macroscale) tend to use a priori parameters, resulting in suboptimal streamflow (Q) …
called macroscale) tend to use a priori parameters, resulting in suboptimal streamflow (Q) …