A review of drought monitoring with big data: Issues, methods, challenges and research directions
Over recent years, the frequency and intensity of droughts have increased and there has
been a large drying trend over many parts of the world. Consequently, drought monitoring …
been a large drying trend over many parts of the world. Consequently, drought monitoring …
Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting
Operational flood control systems depend on reliable and accurate forecasts with a suitable
lead time to take necessary actions against flooding. This study proposed a Long Short …
lead time to take necessary actions against flooding. This study proposed a Long Short …
Incorporating social system into water-food-energy nexus
The current study introduces a conceptual socio-hydrological-based framework for the water-
energy-food (WEF) nexus. The proposed conceptual framework aims to investigate how …
energy-food (WEF) nexus. The proposed conceptual framework aims to investigate how …
A review of drought monitoring using remote sensing and data mining methods
Today, drought has become part of the identity as well as the fate of many countries. In fact,
drought is considered among the most damaging natural disasters. The severe …
drought is considered among the most damaging natural disasters. The severe …
Hybrid wavelet-M5 model tree for rainfall-runoff modeling
In this study, the hybrid wavelet-M5 model was introduced to model the rainfall-runoff
process via three different data division strategies (75%–25%, 60%–40%, and 50%–50%) …
process via three different data division strategies (75%–25%, 60%–40%, and 50%–50%) …
A novel method for sea surface temperature prediction based on deep learning
X Yu, S Shi, L Xu, Y Liu, Q Miao… - … Problems in Engineering, 2020 - Wiley Online Library
Sea surface temperature (SST) forecasting is the task of predicting future values of a given
sequence using historical SST data, which is beneficial for observing and studying …
sequence using historical SST data, which is beneficial for observing and studying …
Conjunction of emotional ANN (EANN) and wavelet transform for rainfall-runoff modeling
The current research introduces a combined wavelet-emotional artificial neural network
(WEANN) approach for one-time-ahead rainfall-runoff modeling of two watersheds with …
(WEANN) approach for one-time-ahead rainfall-runoff modeling of two watersheds with …
Emotional artificial neural networks (EANNs) for multi-step ahead prediction of monthly precipitation; case study: northern Cyprus
The target of the current paper was to examine the performance of three Markovian and
seasonal based artificial neural network (ANN) models for one-step ahead and three-step …
seasonal based artificial neural network (ANN) models for one-step ahead and three-step …
Potential of kernel and tree-based machine-learning models for estimating missing data of rainfall
In this study, two kernel-based models were used which include Support Vector Regression
(SVR) and Gaussian Process Regression (GPR) and were compared with two tree-based …
(SVR) and Gaussian Process Regression (GPR) and were compared with two tree-based …
Combining time varying filtering based empirical mode decomposition and machine learning to predict precipitation from nonlinear series
C Song, X Chen, P Wu, H ** - Journal of Hydrology, 2021 - Elsevier
In recent years, due to the influence of human activities and changing climate conditions,
precipitation time series have had increasing non-stationarity and randomness. Therefore …
precipitation time series have had increasing non-stationarity and randomness. Therefore …