S. Asadollah

发表

The potential of new ensemble machine learning models for effluent quality parameters prediction and related uncertainty

A. Sharafati, S. Asadollah, M. Hosseinzadeh, 2020 .

A new artificial intelligence strategy for predicting the groundwater level over the Rafsanjan aquifer in Iran

A. Sharafati, S. Asadollah, A. Neshat, 2020 .

River water quality index prediction and uncertainty analysis: A comparative study of machine learning models

Z. Yaseen, A. Sharafati, S. Asadollah, 2020 .

A robust stochastic approach in correcting the TRMM precipitation product and simulating flood features

A. Sharafati, S. Asadollah, A. Neshat, 2022, Environmental Monitoring and Assessment.

Prediction of heat waves using meteorological variables in diverse regions of Iran with advanced machine learning models

A. Sharafati, S. Asadollah, Najeebullah Khan, 2021, Stochastic Environmental Research and Risk Assessment.

Post-processing of the UKMO ensemble precipitation product over various regions of Iran: integration of long short-term memory model with principal component analysis

A. Sharafati, S. Asadollah, Sepideh Alizadeh, 2022, Theoretical and Applied Climatology.

Application of ensemble machine learning model in downscaling and projecting climate variables over different climate regions in Iran

A. Sharafati, S. Asadollah, Shamsuddin Shahid, 2021, Environmental Science and Pollution Research.

Estimating the transient storage parameters for pollution modeling in small streams: a comparison of newly developed hybrid optimization algorithms

A. Sharafati, S. Asadollah, A. Neshat, 2021, Environmental Monitoring and Assessment.

Application of bagging ensemble model for predicting compressive strength of hollow concrete masonry prism

A. Sharafati, S. Asadollah, N. Al-Ansari, 2021 .

Satellite-based prediction of surface dust mass concentration in southeastern 1 Iran using gradient boosting regression

A. Sharafati, S. Asadollah, D. Motta, 2022 .

Developing a generic relation for predicting sediment pick-up rate using symbolic soft computing techniques

A. Sharafati, S. Asadollah, D. Motta, 2022, Environmental Science and Pollution Research.

Machine learning model development for predicting aeration efficiency through Parshall flume

Ahmad Sharafati, Nadhir Al-Ansari, Sangeeta, 2021 .

An efficient strategy for predicting river dissolved oxygen concentration: application of deep recurrent neural network model

A. Sharafati, S. Asadollah, D. Motta, 2021, Environmental Monitoring and Assessment.

Assessing the impact of climate change on urban water demand and related uncertainties: a case study of Neyshabur, Iran

A. Sharafati, S. Asadollah, A. Shahbazi, 2021, Theoretical and Applied Climatology.

A novel intelligent approach for predicting meteorological drought based on satellite-based precipitation product: Application of an EMD-DFA-DBN hybrid model

A. Sharafati, S. Asadollah, D. Motta, 2023, Comput. Electron. Agric..

Satellite-based prediction of surface dust mass concentration in southeastern Iran using an intelligent approach

A. Sharafati, S. Asadollah, D. Motta, 2023, Stochastic Environmental Research and Risk Assessment.

Optimizing machine learning for agricultural productivity: A novel approach with RScv and remote sensing data over Europe

S. Asadollah, Antonio Jodar-Abellan, M. A. Pardo, 2024, Agricultural Systems.

Assessing current and future available resources to supply urban water demands using a high-resolution SWAT model coupled with recurrent neural networks and validated through the SIMPA model in karstic Mediterranean environments.

S. Asadollah, Antonio Jodar-Abellan, M. A. Pardo, 2024, Environmental science and pollution research international.