Evapotranspiration evaluation models based on machine learning algorithms—A comparative study
F Granata - Agricultural Water Management, 2019 - Elsevier
The constant need to increase agricultural production, together with the more and more
frequent drought events in many areas of the world, requires a more careful assessment of …
frequent drought events in many areas of the world, requires a more careful assessment of …
Machine learning techniques for monthly river flow forecasting of Hunza River, Pakistan
The forecast of river flow has high great importance in water resources and hazard
management. It becomes more important in mountain areas because most of the …
management. It becomes more important in mountain areas because most of the …
A deep learning approach for hydrological time-series prediction: A case study of Gilgit river basin
Streamflow prediction is a significant undertaking for water resources planning and
management. Accurate forecasting of streamflow always being a challenging task for the …
management. Accurate forecasting of streamflow always being a challenging task for the …
Reliability evaluation of groundwater quality index using data-driven models
A trustworthy evaluation of the groundwater quality situations for different usages (ie,
drinking, industry, and agriculture) can definitely improve the management of groundwater …
drinking, industry, and agriculture) can definitely improve the management of groundwater …
Artificial Intelligence models for prediction of the tide level in Venice
The city of Venice is an extraordinary architectural, artistic and cultural heritage.
Unfortunately, its conservation is increasingly threatened by particularly significant high …
Unfortunately, its conservation is increasingly threatened by particularly significant high …
Dissolved oxygen concentration predictions for running waters with different land use land cover using a quantile regression forest machine learning technique
Modeling dissolved oxygen (DO) in running water represents a challenge due to complex
interactions among various processes affecting its concentration and the intricacy of using …
interactions among various processes affecting its concentration and the intricacy of using …
[HTML][HTML] Comparative study of machine learning methods and GR2M model for monthly runoff prediction
Monthly runoff time-series estimation is imperative information for water resources planning
and development projects. This article aims to comparatively investigate the applicability of …
and development projects. This article aims to comparatively investigate the applicability of …
Development of machine learning flood model using artificial neural network (ann) at var river
Data-driven flow forecasting models, such as Artificial Neural Networks (ANNs), are
increasingly used for operational flood warning systems. In this research, we systematically …
increasingly used for operational flood warning systems. In this research, we systematically …
Novel ensemble machine learning modeling approach for groundwater potential map** in Parbhani District of Maharashtra, India
Groundwater is an essential source of water especially in arid and semi-arid regions of the
world. The demand for water due to exponential increase in population has created stresses …
world. The demand for water due to exponential increase in population has created stresses …
Forecasting of extreme storm tide events using NARX neural network-based models
The extreme values of high tides are generally caused by a combination of astronomical and
meteorological causes, as well as by the conformation of the sea basin. One place where …
meteorological causes, as well as by the conformation of the sea basin. One place where …