Rainfall–runoff modelling using long short-term memory (LSTM) networks
Rainfall–runoff modelling is one of the key challenges in the field of hydrology. Various
approaches exist, ranging from physically based over conceptual to fully data-driven …
approaches exist, ranging from physically based over conceptual to fully data-driven …
A review of different dimensionality reduction methods for the prediction of sugar content from hyperspectral images of wine grape berries
Several dimensionality reduction techniques were applied to hyperspectral reflectance
images of wine grape berries, leading a study of the machine learning models' efficiency in …
images of wine grape berries, leading a study of the machine learning models' efficiency in …
Application of artificial intelligence models for prediction of groundwater level fluctuations: Case study (Tehran-Karaj alluvial aquifer)
The nonlinear groundwater level fluctuations depend on the interaction of many factors such
as evapotranspiration, precipitation, groundwater abstraction, and hydrogeological …
as evapotranspiration, precipitation, groundwater abstraction, and hydrogeological …
Application of artificial neural networks in forecasting a standardized precipitation evapotranspiration index for the Upper Blue Nile basin
The occurrence frequency of drought has intensified with the unprecedented effect of global
warming. Knowledge about the spatiotemporal distributions of droughts and their trends is …
warming. Knowledge about the spatiotemporal distributions of droughts and their trends is …
Evaluation of data-driven models (SVR and ANN) for groundwater-level prediction in confined and unconfined systems
Modeling the behavior of groundwater levels is necessary to implement sustainable
groundwater resource management. Groundwater is a non-linear and complex system …
groundwater resource management. Groundwater is a non-linear and complex system …
A new few-shot learning model for runoff prediction: Demonstration in two data scarce regions
Most existing hydrologic models and machine learning models failed to perform well on
runoff prediction in data scarce regions. As an alternative to this, the Long Short-Term …
runoff prediction in data scarce regions. As an alternative to this, the Long Short-Term …
Modeling flood plain wetland transformation in consequences of flow alteration in Punarbhaba river in India and Bangladesh
Ecologically precious and economically remunerative wetland resources in the riparian
flood plain of Punarbhaba river basin are under massive transformation in post hydrological …
flood plain of Punarbhaba river basin are under massive transformation in post hydrological …
Univariate streamflow forecasting using commonly used data-driven models: literature review and case study
Eight data-driven models and five data pre-processing methods were summarized; the
multiple linear regression (MLR), artificial neural network (ANN) and wavelet decomposition …
multiple linear regression (MLR), artificial neural network (ANN) and wavelet decomposition …
Hybrid meta-heuristics artificial intelligence models in simulating discharge passing the piano key weirs
Piano key weirs (PKWs) are acquired and developed for free surface control structures
which improve their performance by increasing the storage capacity and flood evacuation. In …
which improve their performance by increasing the storage capacity and flood evacuation. In …
Advancing flood warning procedures in ungauged basins with machine learning
Flood prediction across scales and more specifically in ungauged areas remains a great
challenge that limits the efficiency of flood risk mitigation strategies and disaster …
challenge that limits the efficiency of flood risk mitigation strategies and disaster …