Ensemble machine learning paradigms in hydrology: A review
Recently, there has been a notable tendency towards employing ensemble learning
methodologies in assorted areas of engineering, such as hydrology, for simulation and …
methodologies in assorted areas of engineering, such as hydrology, for simulation and …
Predicting water quality with artificial intelligence: a review of methods and applications
D Irwan, M Ali, AN Ahmed, G Jacky, A Nurhakim… - … Methods in Engineering, 2023 - Springer
The water is the main pivotal sources of irrigation in agricultural activities and affects human
daily activities such as drinking. The water quality has a significant impact on various …
daily activities such as drinking. The water quality has a significant impact on various …
Modelling and prediction of water quality by using artificial intelligence
M Hmoud Al-Adhaileh, F Waselallah Alsaade - Sustainability, 2021 - mdpi.com
Artificial intelligence methods can remarkably reduce costs for water supply and sanitation
systems and help ensure compliance with the quality of drinking and wastewater treatment …
systems and help ensure compliance with the quality of drinking and wastewater treatment …
Common irrigation drivers of freshwater salinisation in river basins worldwide
Freshwater salinisation is a growing problem, yet cross-regional assessments of freshwater
salinity status and the impact of agricultural and other sectoral uses are lacking. Here, we …
salinity status and the impact of agricultural and other sectoral uses are lacking. Here, we …
Drought indicator analysis and forecasting using data driven models: case study in Jaisalmer, India
Agricultural droughts are a prime concern for economies worldwide as they negatively
impact the productivity of rain-fed crops, employment, and income per capita. In this study …
impact the productivity of rain-fed crops, employment, and income per capita. In this study …
Deep learning for prediction of water quality index classification: tropical catchment environmental assessment
River water quality modeling using crucial artificial intelligent (AI) models has become an
essential tool for river assessment and management. The simplified approach of river health …
essential tool for river assessment and management. The simplified approach of river health …
Enhancing flood susceptibility modeling using multi-temporal SAR images, CHIRPS data, and hybrid machine learning algorithms
Flood susceptibility maps are useful tool for planners and emergency management
professionals in the early warning and mitigation stages of floods. In this study, Sentinel-1 …
professionals in the early warning and mitigation stages of floods. In this study, Sentinel-1 …
Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh
The rising salinity trend in the country's coastal groundwater has reached an alarming rate
due to unplanned use of groundwater in agriculture and seawater seeping into the …
due to unplanned use of groundwater in agriculture and seawater seeping into the …
Application of machine learning and process-based models for rainfall-runoff simulation in Dupage River basin, Illinois
Rainfall-runoff simulation is vital for planning and controlling flood control events. Hydrology
modeling using Hydrological Engineering Center—Hydrologic Modeling System (HEC …
modeling using Hydrological Engineering Center—Hydrologic Modeling System (HEC …
Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?
The global decline of water quality in rivers and streams has resulted in a pressing need to
design new watershed management strategies. Water quality can be affected by multiple …
design new watershed management strategies. Water quality can be affected by multiple …