Seasonal drought prediction: Advances, challenges, and future prospects
Drought prediction is of critical importance to early warning for drought managements. This
review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid …
review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid …
[HTML][HTML] Hybrid forecasting: blending climate predictions with AI models
Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine
learning) methods to harness and integrate a broad variety of predictions from dynamical …
learning) methods to harness and integrate a broad variety of predictions from dynamical …
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 …
Stacking ensemble learning models for daily runoff prediction using 1D and 2D CNNs
Y Xie, W Sun, M Ren, S Chen, Z Huang… - Expert Systems with …, 2023 - Elsevier
In recent years, applications of convolutional neural networks (CNNs) to runoff prediction
have received some attention due to their excellent feature extraction capabilities. However …
have received some attention due to their excellent feature extraction capabilities. However …
Introductory overview: Optimization using evolutionary algorithms and other metaheuristics
Environmental models are used extensively to evaluate the effectiveness of a range of
design, planning, operational, management and policy options. However, the number of …
design, planning, operational, management and policy options. However, the number of …
An integrated statistical-machine learning approach for runoff prediction
Nowadays, great attention has been attributed to the study of runoff and its fluctuation over
space and time. There is a crucial need for a good soil and water management system to …
space and time. There is a crucial need for a good soil and water management system to …
Process‐guided deep learning predictions of lake water temperature
The rapid growth of data in water resources has created new opportunities to accelerate
knowledge discovery with the use of advanced deep learning tools. Hybrid models that …
knowledge discovery with the use of advanced deep learning tools. Hybrid models that …
IHACRES, GR4J and MISD-based multi conceptual-machine learning approach for rainfall-runoff modeling
As a complex hydrological problem, rainfall-runoff (RR) modeling is of importance in runoff
studies, water supply, irrigation issues, and environmental management. Among the variety …
studies, water supply, irrigation issues, and environmental management. Among the variety …
Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling
S Razavi - Environmental Modelling & Software, 2021 - Elsevier
Recent breakthroughs in artificial intelligence (AI), and particularly in deep learning (DL),
have created tremendous excitement and opportunities in the earth and environmental …
have created tremendous excitement and opportunities in the earth and environmental …
Simulation and forecasting of streamflows using machine learning models coupled with base flow separation
Efficient simulation of rainfall-runoff relationships is one of the most complex problems owing
to the high number of interrelated hydrological processes. It is well-known that machine …
to the high number of interrelated hydrological processes. It is well-known that machine …