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 | 241 | 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 | 170 | 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 | 168 | 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 | 121 | 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 | 105 | 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 | 92 | 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 | 85 | 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 | 77 | 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 | 68 | 2021 |
Estimation of Water Quality Parameters with Data-Driven Models MT Sattari, AR Joudi, A Kusiak Journal-American Water Works Association 108 (4), 2016 | 58 | 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 | 50 | 2020 |
Flow estimations for the Sohu Stream using artificial neural networks MT Sattari, H Apaydin, F Ozturk Environmental Earth Sciences 66, 2031-2045, 2012 | 43 | 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 | 38 | 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 | 35 | 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 | 32 | 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 | 32 | 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 | 31 | 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 | 30 | 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 | 29 | 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 | 26 | 2017 |