Hybrid hydrological data-driven approach for daily streamflow forecasting
Hydrological forecasting is key for water resources allocation and flood risk management.
Although a number of advanced hydrological forecasting methods have been developed in …
Although a number of advanced hydrological forecasting methods have been developed in …
Monthly rainfall forecasting by a hybrid neural network of discrete wavelet transformation and deep learning
M Wei, X You - Water Resources Management, 2022 - Springer
Rainfall forecast is critical to the management and allocation of water resources. Deep
learning is used to predict rainfall time series with high temporal and spatial variability …
learning is used to predict rainfall time series with high temporal and spatial variability …
Implementation of univariate paradigm for streamflow simulation using hybrid data-driven model: Case study in tropical region
The performance of the bio-inspired adaptive neuro-fuzzy inference system (ANFIS) models
are proposed for forecasting highly non-linear streamflow of Pahang River, located in a …
are proposed for forecasting highly non-linear streamflow of Pahang River, located in a …
FlowDyn: A daily streamflow prediction pipeline for dynamical deep neural network applications
This paper presents a dynamical neural network framework to understand how catchment
systems respond to daily rainfall-runoff processes over time. We developed an interactive …
systems respond to daily rainfall-runoff processes over time. We developed an interactive …
Stream flow predictions using nature-inspired Firefly Algorithms and a Multiple Model strategy–Directions of innovation towards next generation practices
R Khatibi, MA Ghorbani, FA Pourhosseini - Advanced Engineering …, 2017 - Elsevier
Stream flow prediction is studied by Artificial Intelligence (AI) in this paper using Artificial
Neural Network (ANN) as a hybrid of Multi-Layer Perceptron (MLP) with the Levenberg …
Neural Network (ANN) as a hybrid of Multi-Layer Perceptron (MLP) with the Levenberg …
Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach
H Moeeni, H Bonakdari, I Ebtehaj - Journal of Earth System Science, 2017 - Springer
Forecasting reservoir inflow is one of the most important components of water resources and
hydroelectric systems operation management. Seasonal autoregressive integrated moving …
hydroelectric systems operation management. Seasonal autoregressive integrated moving …
Short-term forecasting of streamflow by integrating machine learning methods combined with metaheuristic algorithms
F Jia, Z Zhu, W Dai - Expert Systems with Applications, 2024 - Elsevier
This paper aims to introduce an artificial intelligence approach to enhance short-term daily
streamflow forecasts through the integration of machine learning (ML) techniques with meta …
streamflow forecasts through the integration of machine learning (ML) techniques with meta …
[PDF][PDF] A critical review on artificial intelligence models in hydrological forecasting how reliable are artificial intelligence models
DC Damian - Int J Eng Res, 2019 - academia.edu
There has been a variety of techniques employed in the forecasting hydrological events.
These techniques have shown efficacies in some circumstances and also shown poor …
These techniques have shown efficacies in some circumstances and also shown poor …
Modeling the effect of meteorological variables on streamflow estimation: Application of data mining techniques in mixed rainfall–snowmelt regime munzur river …
OM Katipoğlu - Environmental Science and Pollution Research, 2023 - Springer
Revealing the dynamic link between rainfall and runoff, which are the main components of
the hydrological cycle, is significant for the planning and managing water resources, disaster …
the hydrological cycle, is significant for the planning and managing water resources, disaster …
Self-training approach to improve the predictability of data-driven rainfall-runoff model in hydrological data-sparse regions
S Yoon, KH Ahn - Journal of Hydrology, 2024 - Elsevier
Numerous data-driven models have been introduced to establish reliable predictions in the
rainfall-runoff relationship. The majority of these models are trained using a supervised …
rainfall-runoff relationship. The majority of these models are trained using a supervised …