A reliable hybrid outlier robust non-tuned rapid machine learning model for multi-step ahead flood forecasting in Quebec, Canada
I Ebtehaj, H Bonakdari - Journal of Hydrology, 2022 - Elsevier
Reliable and accurate flood forecasting is a complex and challenging problem that is
essential for the creation of disaster preparedness plans to protect life and reduce economic …
essential for the creation of disaster preparedness plans to protect life and reduce economic …
Forecasting groundwater anomaly in the future using satellite information and machine learning
Abstract By applying Gravity Recovery and Climate Experiment (GRACE) and GRACE
Follow-On (GFO) satellites, this study estimates the value of Terrestrial Water Storage …
Follow-On (GFO) satellites, this study estimates the value of Terrestrial Water Storage …
A novel machine learning tool for current and future flood susceptibility mapping by integrating remote sensing and geographic information systems
Flood mapping is essential for managing and mitigating the risks associated with flood
events. This study integrates weighted overlay analysis and analytical network process to …
events. This study integrates weighted overlay analysis and analytical network process to …
Enhancing water use efficiency in precision irrigation: data-driven approaches for addressing data gaps in time series
Real-time soil matric potential measurements for determining potato production's water
availability are currently used in precision irrigation. It is well known that managing irrigation …
availability are currently used in precision irrigation. It is well known that managing irrigation …
Terrestrial water storage anomaly estimating using machine learning techniques and satellite‐based data (a case study of Lake Urmia Basin)
In this study, the Terrestrial Water Storage Anomaly (TWSA) in the Lake Urmia Basin (LUB)
was obtained by using the GRACE satellites. The whole study area was covered by 10 …
was obtained by using the GRACE satellites. The whole study area was covered by 10 …
[HTML][HTML] Advanced Forecasting of Drought Zones in Canada Using Deep Learning and CMIP6 Projections
This study addresses the critical issue of drought zoning in Canada using advanced deep
learning techniques. Drought, exacerbated by climate change, significantly affects …
learning techniques. Drought, exacerbated by climate change, significantly affects …
[HTML][HTML] Artificial intelligence-driven assessment of critical inputs for lead adsorption by agro-food wastes in wastewater treatment
Due to environmental concerns and economic value, the adsorption process using
agricultural wastes is one of the promising methods to remove lead (Pb) from contaminated …
agricultural wastes is one of the promising methods to remove lead (Pb) from contaminated …
Predicting bulk density of dehydrated food products: a comparative study of three machine learning techniques, potential opportunities, and limitations of artificial …
Bulk density is among the fundamental properties for defining quality attributes of
dehydrated foods. Mathematical models offer elegant tools for predicting bulk density and …
dehydrated foods. Mathematical models offer elegant tools for predicting bulk density and …
Application of mathematical models to assess the impact of the COVID-19 pandemic on logistics businesses and recovery solutions for sustainable development
HK Nguyen - Mathematics, 2021 - mdpi.com
The logistics industry can be considered as the economic lifeline of each country because of
its role in connecting production and business activities of enterprises and promoting socio …
its role in connecting production and business activities of enterprises and promoting socio …
Assessment of artificial intelligence for predicting porosity of dehydrated food products
Porosity is a relevant characteristic of dried products, and it depends on several parameters
such as food material properties, drying technologies, and process conditions. The …
such as food material properties, drying technologies, and process conditions. The …