Deep learning in hydrology and water resources disciplines: Concepts, methods, applications, and research directions
Over the past few years, Deep Learning (DL) methods have garnered substantial
recognition within the field of hydrology and water resources applications. Beginning with a …
recognition within the field of hydrology and water resources applications. Beginning with a …
[HTML][HTML] Groundwater level prediction using machine learning models: A comprehensive review
Develo** accurate soft computing methods for groundwater level (GWL) forecasting is
essential for enhancing the planning and management of water resources. Over the past two …
essential for enhancing the planning and management of water resources. Over the past two …
The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction
Precise streamflow prediction is necessary for better planning and managing available
water and future water resources, especially for high altitude mountainous glacier melting …
water and future water resources, especially for high altitude mountainous glacier melting …
The state of the art in deep learning applications, challenges, and future prospects: A comprehensive review of flood forecasting and management
Floods are a devastating natural calamity that may seriously harm both infrastructure and
people. Accurate flood forecasts and control are essential to lessen these effects and …
people. Accurate flood forecasts and control are essential to lessen these effects and …
Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks
Streamflow (Q flow) prediction is one of the essential steps for the reliable and robust water
resources planning and management. It is highly vital for hydropower operation, agricultural …
resources planning and management. It is highly vital for hydropower operation, agricultural …
Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)
Groundwater quality appraisal is one of the most crucial tasks to ensure safe drinking water
sources. Concurrently, a water quality index (WQI) requires some water quality parameters …
sources. Concurrently, a water quality index (WQI) requires some water quality parameters …
An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions
ZM Yaseen - Chemosphere, 2021 - Elsevier
The development of computer aid models for heavy metals (HMs) simulation has been
remarkably advanced over the past two decades. Several machine learning (ML) models …
remarkably advanced over the past two decades. Several machine learning (ML) models …
A survey on river water quality modelling using artificial intelligence models: 2000–2020
There has been an unsettling rise in the river contamination due to the climate change and
anthropogenic activities. Last decades' research has immensely focussed on river basin …
anthropogenic activities. Last decades' research has immensely focussed on river basin …
Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization
Accurate runoff estimation is crucial for optimal reservoir operation and irrigation purposes.
In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla …
In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla …
River water quality index prediction and uncertainty analysis: A comparative study of machine learning models
Abstract The Water Quality Index (WQI) is the most common indicator to characterize surface
water quality. This study introduces a new ensemble machine learning model called Extra …
water quality. This study introduces a new ensemble machine learning model called Extra …