Ahmad Sharafati

Orcid: 0000-0003-0448-2871

According to our database1, Ahmad Sharafati authored at least 12 papers between 2020 and 2024.

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Bibliography

2024
Estimation of soil moisture from remote sensing products using an ensemble machine learning model: a case study of Lake Urmia Basin, Iran.
Earth Sci. Informatics, February, 2024

Lake total suspended matter retrieval by wind speed: A machine learning model trained by time-series satellite imagery.
Ecol. Informatics, 2024

2023
Investigating hybrid deep learning models and meta-heuristic algorithms in predicting evaporation from a reservoir: a case study of Dez dam.
Earth Sci. Informatics, December, 2023

MOOTLBO: a new multi-objective observer-teacher-learner-based optimization.
Soft Comput., October, 2023

A novel intelligent approach for predicting meteorological drought based on satellite-based precipitation product: Application of an EMD-DFA-DBN hybrid model.
Comput. Electron. Agric., August, 2023

2022
An intelligent approach for estimating aeration efficiency in stepped cascades: optimized support vector regression models and mutual information theory.
Soft Comput., December, 2022

Correction: An intelligent approach for estimating aeration efficiency in stepped cascades: optimized support vector regression models and mutual information theory.
Soft Comput., 2022

A novel boosting ensemble committee-based model for local scour depth around non-uniformly spaced pile groups.
Eng. Comput., 2022

2021
Prediction of channel sinuosity in perennial rivers using Bayesian Mutual Information theory and support vector regression coupled with meta-heuristic algorithms.
Earth Sci. Informatics, 2021

Application of Artificial Intelligence Models for Evapotranspiration Prediction along the Southern Coast of Turkey.
Complex., 2021

Estimation of Daily Suspended Sediment Load Using a Novel Hybrid Support Vector Regression Model Incorporated with Observer-Teacher-Learner-Based Optimization Method.
Complex., 2021

2020
Global Solar Radiation Estimation and Climatic Variability Analysis Using Extreme Learning Machine Based Predictive Model.
IEEE Access, 2020


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