Generating ensemble streamflow forecasts: A review of methods and approaches over the past 40 years

M Troin, R Arsenault, AW Wood, F Brissette, JL Martel - 2021 - Wiley Online Library
Ensemble forecasting applied to the field of hydrology is currently an established area of
research embracing a broad spectrum of operational situations. This work catalogs the …

Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods

O Rahmati, B Choubin, A Fathabadi, F Coulon… - Science of the Total …, 2019 - Elsevier
Although estimating the uncertainty of models used for modelling nitrate contamination of
groundwater is essential in groundwater management, it has been generally ignored. This …

Quantile regression as a generic approach for estimating uncertainty of digital soil maps produced from machine-learning

B Kasraei, B Heung, DD Saurette, MG Schmidt… - … Modelling & Software, 2021 - Elsevier
Digital soil mapping (DSM) techniques have provided soil information that has
revolutionized soil management across multiple spatial extents and scales. DSM …

[HTML][HTML] The risk assessment of arsenic contamination in the urbanized coastal aquifer of Rayong groundwater basin, Thailand using the machine learning approach

N Sumdang, S Chotpantarat, KH Cho… - … and Environmental Safety, 2023 - Elsevier
The rapid expansion of urbanization has resulted in an insufficient of groundwater resource.
In order to use groundwater more efficiently, a risk assessment of groundwater pollution …

[HTML][HTML] A framework for recalibrating pedotransfer functions using nonlinear least squares and estimating uncertainty using quantile regression

M Schmidt, D Saurette, J Zhang, C Bulmer, D Filatow… - Geoderma, 2023 - Elsevier
Pedotransfer functions (PTFs) have been developed for many regions to estimate values
missing from soil profile databases. However, globally there are many areas without existing …

Bayesian Model averaging ensemble approach for multi-time-ahead groundwater level prediction combining the GRACE, GLEAM, and GLDAS data in arid areas

T Zhou, X Wen, Q Feng, H Yu, H Xi - Remote Sensing, 2022 - mdpi.com
Accurate groundwater level (GWL) prediction is essential for the sustainable management of
groundwater resources. However, the prediction of GWLs remains a challenge due to …

Hydrological ensemble forecasting using a multi-model framework

P Dion, JL Martel, R Arsenault - Journal of Hydrology, 2021 - Elsevier
Ensemble streamflow predictions (ESP) from a single hydrological model tend to under-
sample the variability needed to provide a good representation of streamflow observations …

Deep learning-based predictive framework for groundwater level forecast in arid irrigated areas

W Liu, H Yu, L Yang, Z Yin, M Zhu, X Wen - Water, 2021 - mdpi.com
An accurate groundwater level (GWL) forecast at multi timescales is vital for agricultural
management and water resource scheduling in arid irrigated areas such as the Hexi …

[HTML][HTML] A novel local-global dependency deep learning model for soil mapping

Q Li, C Zhang, W Shangguan, L Li, Y Dai - Geoderma, 2023 - Elsevier
The accurate and cost-effective mapping of soil texture is essential for agricultural
development and environmental activities. Soil texture exhibits high spatial heterogeneity …

A runoff probability density prediction method based on B-spline quantile regression and kernel density estimation

Y He, H Fan, X Lei, J Wan - Applied Mathematical Modelling, 2021 - Elsevier
Exact and dependable runoff forecasting plays a vital role in water resources management
and utilization. This paper proposes a B-spline quantile regression probability density …