Theo dơi
Mohammad Taghi Sattari
Mohammad Taghi Sattari
Associate Professor, Department of Water Engineering, University of Tabriz and Ankara University
Email được xác minh tại - Trang chủ
Tiêu đề
Trích dẫn bởi
Trích dẫn bởi
Comparative analysis of recurrent neural network architectures for reservoir inflow forecasting
H Apaydin, H Feizi, MT Sattari, MS Colak, S Shamshirband, KW Chau
Water 12 (5), 1500, 2020
Assessment of different methods for estimation of missing data in precipitation studies
MT Sattari, A Rezazadeh-Joudi, A Kusiak
Hydrology Research 48 (4), 1032-1044, 2017
Modeling pan evaporation using Gaussian process regression K-nearest neighbors random forest and support vector machines; comparative analysis
S Shabani, S Samadianfard, MT Sattari, A Mosavi, S Shamshirband, ...
Atmosphere 11 (1), 66, 2020
M5 model tree application in daily river flow forecasting in Sohu Stream, Turkey
M Taghi Sattari, M Pal, H Apaydin, F Ozturk
Water Resources 40, 233-242, 2013
Performance evaluation of artificial neural network approaches in forecasting reservoir inflow
M Taghi Sattari, K Yurekli, M Pal
Applied Mathematical Modelling 36 (6), 2649-2657, 2012
Ground water quality classification by decision tree method in Ardebil region, Iran
SM Saghebian, MT Sattari, R Mirabbasi, M Pal
Arabian Journal of Geosciences 7 (11), 4767-4777, 2014
Artificial intelligence modelling integrated with Singular Spectral analysis and Seasonal-Trend decomposition using Loess approaches for streamflow predictions
H Apaydin, MT Sattari, K Falsafian, R Prasad
Journal of Hydrology 600, 126506, 2021
Prediction of groundwater level in Ardebil plain using Support Vector Regression and M5 tree model
MT Sattari, R Mirabbasi, R Shamsi, J Abraham
Groundwater, 2018
Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation
MT Sattari, H Apaydin, SS Band, A Mosavi, R Prasad
Hydrology and Earth System Sciences 25 (2), 603-618, 2021
Estimation of Water Quality Parameters with Data-Driven Models
MT Sattari, AR Joudi, A Kusiak
Journal-American Water Works Association 108 (4), 2016
Performance Evaluation of Deep Learning-Based Gated Recurrent Units (GRUs) and Tree-Based Models for Estimating ETo by Using Limited Meteorological Variables
MT Sattari, H Apaydin, S Shamshirband
Mathematics 8 (6), 2020
Flow estimations for the Sohu Stream using artificial neural networks
MT Sattari, H Apaydin, F Ozturk
Environmental Earth Sciences 66, 2031-2045, 2012
Potential of kernel and tree-based machine-learning models for estimating missing data of rainfall
MT Sattari, K Falsafian, A Irvem, SN Qasem
Engineering Applications of Computational Fluid Mechanics 14 (1), 1078-1094, 2020
Threshold-based hybrid data mining method for long-term maximum precipitation forecasting
V Nourani, MT Sattari, A Molajou
Water Resources Management 31, 2645-2658, 2017
Determining Flow Friction Factor in Irrigation Pipes Using Data Mining and Artificial Intelligence Approaches
KÖKH Samadianfard S, Sattari MT
Applied Artificial Intelligence 28 (8), 793-813, 2014
M5 model trees and neural network based modelling of ET0 in Ankara, Turkey
MT Sattari, M Pal, K Yurekli, A Unlukara
Turkish Journal of Engineering and Environmental Sciences 37 (2), 211-219, 2013
Estimation of sodium adsorption ratio indicator using data mining methods: a case study in Urmia Lake basin, Iran
MT Sattari, A Farkhondeh, JP Abraham
Environmental Science and Pollution Research, 2018
Operation analysis of Eleviyan irrigation reservoir dam by optimization and stochastic simulation
MT Sattari, H Apaydin, F Ozturk
Stochastic Environmental Research and Risk Assessment 23, 1187-1201, 2009
Trend and abrupt change analysis in water quality of Urmia Lake in comparison with changes in lake water level
MT Sattari, R Mirabbasi, S Jarhan, F Shaker Sureh, S Ahmad
Environmental monitoring and assessment 192, 1-16, 2020
Performance evaluation of ANNs and an M5 model tree in Sattarkhan Reservoir inflow prediction
B Esmaeilzadeh, MT Sattari, S Samadianfard
ISH Journal of Hydraulic Engineering 23 (3), 283-292, 2017
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