Flood prediction using machine learning models: Literature review
Floods are among the most destructive natural disasters, which are highly complex to model.
The research on the advancement of flood prediction models contributed to risk reduction …
The research on the advancement of flood prediction models contributed to risk reduction …
Watershed modeling and its applications: A state-of-the-art review
EB Daniel, JV Camp, EJ LeBoeuf… - The Open Hydrology …, 2011 - benthamopen.com
Advances in the understanding of physical, chemical, and biological processes influencing
water quality, coupled with improvements in the collection and analysis of hydrologic data …
water quality, coupled with improvements in the collection and analysis of hydrologic data …
A rainfall‐runoff model with LSTM‐based sequence‐to‐sequence learning
Rainfall‐runoff modeling is a complex nonlinear time series problem. While there is still
room for improvement, researchers have been developing physical and machine learning …
room for improvement, researchers have been developing physical and machine learning …
Performance comparison of an LSTM-based deep learning model versus conventional machine learning algorithms for streamflow forecasting
Streamflow forecasting plays a key role in improvement of water resource allocation,
management and planning, flood warning and forecasting, and mitigation of flood damages …
management and planning, flood warning and forecasting, and mitigation of flood damages …
[HTML][HTML] Application of ANN and HEC-RAS model for flood inundation mapping in lower Baro Akobo River Basin, Ethiopia
Abstract Study region Lower Baro River, Ethiopia. Study focus This paper presents the
novelty of ANN and HEC-RAS model for flood inundation mapping in lower Baro Akobo …
novelty of ANN and HEC-RAS model for flood inundation mapping in lower Baro Akobo …
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 …
Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management
M Romero, Y Luo, B Su, S Fuentes - Computers and electronics in …, 2018 - Elsevier
Remote sensing can provide a fast and reliable alternative for traditional in situ water status
measurement in vineyards. Several vegetation indices (VIs) derived from aerial multispectral …
measurement in vineyards. Several vegetation indices (VIs) derived from aerial multispectral …
Deep transfer learning based on transformer for flood forecasting in data-sparse basins
There exists a substantial disparity in the distribution of streamflow gauge and basin
characteristic information, with a majority of flood observations being recorded from a limited …
characteristic information, with a majority of flood observations being recorded from a limited …
Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors
This article describes design and application of feed-forward, fully-connected, three-layer
perceptron neural network model for computing the water quality index (WQI) 1 for Kinta …
perceptron neural network model for computing the water quality index (WQI) 1 for Kinta …
Combining statistical machine learning models with ARIMA for water level forecasting: The case of the Red river
XH Nguyen - Advances in Water Resources, 2020 - Elsevier
Forecasting water level is an extremely important task as it allows to mitigate the effects of
floods, reduce and prevent disasters. Physically based models often give good results but …
floods, reduce and prevent disasters. Physically based models often give good results but …