nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
1 |
A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases
|
Niraula, Poshan |
|
|
36 |
8 |
p. 2265-2283 |
artikel |
2 |
A hybrid framework for forecasting monthly reservoir inflow based on machine learning techniques with dynamic climate forecasts, satellite-based data, and climate phenomenon information
|
Tian, Di |
|
|
36 |
8 |
p. 2353-2375 |
artikel |
3 |
A method to increase the number of positive samples for machine learning-based urban waterlogging susceptibility assessments
|
Tang, Xianzhe |
|
|
36 |
8 |
p. 2319-2336 |
artikel |
4 |
Analysis of precipitation dynamics at different timescales based on entropy theory: an application to the State of Ceará, Brazil
|
Rolim, Larissa Zaira Rafael |
|
|
36 |
8 |
p. 2285-2301 |
artikel |
5 |
A new picking algorithm based on the variance piecewise constant models
|
D’Angelo, Nicoletta |
|
|
36 |
8 |
p. 2101-2113 |
artikel |
6 |
A PCA-based clustering algorithm for the identification of stratiform and convective precipitation at the event scale: an application to the sub-hourly precipitation of Sicily, Italy
|
Sottile, Gianluca |
|
|
36 |
8 |
p. 2303-2317 |
artikel |
7 |
Bayesian time-varying occupancy model for West Nile virus in Ontario, Canada
|
Temple, Seth D. |
|
|
36 |
8 |
p. 2337-2352 |
artikel |
8 |
Data-driven approaches for runoff prediction using distributed data
|
Han, Heechan |
|
|
36 |
8 |
p. 2153-2171 |
artikel |
9 |
From scenario-based seismic hazard to scenario-based landslide hazard: fast-forwarding to the future via statistical simulations
|
Lombardo, Luigi |
|
|
36 |
8 |
p. 2229-2242 |
artikel |
10 |
From scenario-based seismic hazard to scenario-based landslide hazard: rewinding to the past via statistical simulations
|
Luo, Luguang |
|
|
36 |
8 |
p. 2243-2264 |
artikel |
11 |
Geostatistical based framework for spatial modeling of groundwater level during dry and wet seasons in an arid region: a case study at Hadat Ash-Sham experimental station, Saudi Arabia
|
Budiman, Jaka S. |
|
|
36 |
8 |
p. 2085-2099 |
artikel |
12 |
Horizontal grid spacing comparison among Random Forest algorithms to nowcast Cloud-to-Ground lightning occurrence
|
La Fata, Alice |
|
|
36 |
8 |
p. 2195-2206 |
artikel |
13 |
On the prediction of landslide occurrences and sizes via Hierarchical Neural Networks
|
Aguilera, Quinton |
|
|
36 |
8 |
p. 2031-2048 |
artikel |
14 |
Solving three major biases of the ETAS model to improve forecasts of the 2019 Ridgecrest sequence
|
Grimm, Christian |
|
|
36 |
8 |
p. 2133-2152 |
artikel |
15 |
Spatiotemporal data science: theoretical advances and applications
|
Amato, Federico |
|
|
36 |
8 |
p. 2027-2029 |
artikel |
16 |
Spatio-temporal estimation of wind speed and wind power using extreme learning machines: predictions, uncertainty and technical potential
|
Amato, Federico |
|
|
36 |
8 |
p. 2049-2069 |
artikel |
17 |
Stacking ensemble of deep learning methods for landslide susceptibility mapping in the Three Gorges Reservoir area, China
|
Li, Wenjuan |
|
|
36 |
8 |
p. 2207-2228 |
artikel |
18 |
Statistical spatiotemporal analysis of hydro-morphological processes in China during 1950–2015
|
Wang, Nan |
|
|
36 |
8 |
p. 2377-2397 |
artikel |
19 |
Temporal downscaling of precipitation from climate model projections using machine learning
|
Kajbaf, Azin Al |
|
|
36 |
8 |
p. 2173-2194 |
artikel |
20 |
Unified landslide hazard assessment using hurdle models: a case study in the Island of Dominica
|
Bryce, Erin |
|
|
36 |
8 |
p. 2071-2084 |
artikel |
21 |
Using data-driven algorithms for semi-automated geomorphological mapping
|
Giaccone, Elisa |
|
|
36 |
8 |
p. 2115-2131 |
artikel |