Mohammad Taghi Sattari

2.2k total citations
68 papers, 1.6k citations indexed

About

Mohammad Taghi Sattari is a scholar working on Environmental Engineering, Global and Planetary Change and Water Science and Technology. According to data from OpenAlex, Mohammad Taghi Sattari has authored 68 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Environmental Engineering, 31 papers in Global and Planetary Change and 30 papers in Water Science and Technology. Recurrent topics in Mohammad Taghi Sattari's work include Hydrological Forecasting Using AI (35 papers), Hydrology and Watershed Management Studies (21 papers) and Hydrology and Drought Analysis (15 papers). Mohammad Taghi Sattari is often cited by papers focused on Hydrological Forecasting Using AI (35 papers), Hydrology and Watershed Management Studies (21 papers) and Hydrology and Drought Analysis (15 papers). Mohammad Taghi Sattari collaborates with scholars based in Iran, Türkiye and United States. Mohammad Taghi Sattari's co-authors include Halit Apaydın, Mahesh Pal, Shahaboddin Shamshirband, Andrew Kusiak, Hajar Feizi, Rasoul Mirabbasi, Fazlı Öztürk, Ramendra Prasad, John Abraham and Kwok‐wing Chau and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Hydrology and Environmental Science and Pollution Research.

In The Last Decade

Mohammad Taghi Sattari

64 papers receiving 1.6k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mohammad Taghi Sattari Iran 21 858 689 650 228 206 68 1.6k
Saad Sh. Sammen Iraq 26 888 1.0× 694 1.0× 808 1.2× 240 1.1× 231 1.1× 109 2.1k
Paresh Chandra Deka India 25 1.0k 1.2× 628 0.9× 692 1.1× 242 1.1× 241 1.2× 44 2.0k
Hadi Sanikhani Iran 23 925 1.1× 630 0.9× 496 0.8× 276 1.2× 237 1.2× 41 1.5k
Saeed Samadianfard Iran 24 951 1.1× 630 0.9× 670 1.0× 290 1.3× 230 1.1× 54 1.9k
Saeid Mehdizadeh Iran 27 1.0k 1.2× 892 1.3× 547 0.8× 374 1.6× 327 1.6× 42 1.7k
C.L. Wu Hong Kong 9 990 1.2× 609 0.9× 729 1.1× 227 1.0× 372 1.8× 9 1.6k
Sujay Raghavendra Naganna India 20 864 1.0× 470 0.7× 583 0.9× 168 0.7× 169 0.8× 57 1.8k
P. C. Nayak India 15 1.2k 1.4× 1.0k 1.5× 919 1.4× 203 0.9× 277 1.3× 22 1.8k
Fabio Di Nunno Italy 21 879 1.0× 637 0.9× 687 1.1× 133 0.6× 143 0.7× 72 1.5k
Mukesh Tiwari India 24 1.2k 1.4× 829 1.2× 802 1.2× 239 1.0× 423 2.1× 83 2.1k

Countries citing papers authored by Mohammad Taghi Sattari

Since Specialization
Citations

This map shows the geographic impact of Mohammad Taghi Sattari's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mohammad Taghi Sattari with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohammad Taghi Sattari more than expected).

Fields of papers citing papers by Mohammad Taghi Sattari

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mohammad Taghi Sattari. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mohammad Taghi Sattari. The network helps show where Mohammad Taghi Sattari may publish in the future.

Co-authorship network of co-authors of Mohammad Taghi Sattari

This figure shows the co-authorship network connecting the top 25 collaborators of Mohammad Taghi Sattari. A scholar is included among the top collaborators of Mohammad Taghi Sattari based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mohammad Taghi Sattari. Mohammad Taghi Sattari is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Sattari, Mohammad Taghi, et al.. (2025). Evaluating pipe failure risk in water distribution networks using GIS fuzzy overlay, ROC curves and machine learning logistic regression. Water Science & Technology Water Supply. 25(11). 1539–1555.
2.
Sattari, Mohammad Taghi, et al.. (2025). An integrated artificial intelligence-deep learning approach for vegetation canopy assessment and monitoring through satellite images. Stochastic Environmental Research and Risk Assessment. 39(4). 1623–1645. 1 indexed citations
3.
Sattari, Mohammad Taghi, et al.. (2025). Groundwater quality mapping based on the Wilcox Classification method for agricultural purposes: Qazvin Plain aquifer case. Turkish Journal of Engineering. 9(1). 116–128. 2 indexed citations
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Sattari, Mohammad Taghi, et al.. (2024). A Novel Stochastic Tree Model for Daily Streamflow Prediction Based on A Noise Suppression Hybridization Algorithm and Efficient Uncertainty Quantification. Water Resources Management. 38(6). 1943–1964. 6 indexed citations
7.
Sattari, Mohammad Taghi, et al.. (2023). Evaluation of Feature Selection Methods in Estimation of Precipitation Based on Deep Learning Artificial Neural Networks. Water Resources Management. 37(15). 5871–5891. 1 indexed citations
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Feizi, Hajar, Mohammad Taghi Sattari, Mohammad Mosaferi, & Halit Apaydın. (2022). An image-based deep learning model for water turbidity estimation in laboratory conditions. International Journal of Environmental Science and Technology. 20(1). 149–160. 10 indexed citations
10.
Sattari, Mohammad Taghi, et al.. (2022). Comprehensive review of solar radiation modeling based on artificial intelligence and optimization techniques: future concerns and considerations. Clean Technologies and Environmental Policy. 25(4). 1079–1097. 14 indexed citations
11.
Sattari, Mohammad Taghi, Halit Apaydın, Shahab S. Band, Amir Mosavi, & Ramendra Prasad. (2021). Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation. Hydrology and earth system sciences. 25(2). 603–618. 69 indexed citations
12.
Apaydın, Halit, et al.. (2020). Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting. Water. 12(5). 1500–1500. 202 indexed citations
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Sattari, Mohammad Taghi, Halit Apaydın, Shahab Shamshirband, & Amir Mosavi. (2020). Comparative analysis of Kernel-based versus BFGS-ANN and deeplearning methods in monthly reference evaporation estimation. 2 indexed citations
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Sattari, Mohammad Taghi, et al.. (2019). Estimation of monthly precipitation based on machine learning methods by using meteorological variables. Mustafa Kemal Üniversitesi Tarım Bilimleri Dergisi. 24. 149–154. 1 indexed citations
17.
Sakizadeh, Mohamad, Mohammad Taghi Sattari, & Hadi Ghorbani. (2017). A new method to consider spatial risk assessment of cross-correlated heavy metals using geo-statistical simulation. Journal of mining and environment. 8(3). 373–391. 1 indexed citations
18.
Sattari, Mohammad Taghi, et al.. (2012). Decision trees to determine the possible drought periods in Ankara. Atmósfera. 25(1). 65–83. 9 indexed citations
19.
Yürekli, Kadri, et al.. (2012). Seasonal and annual regional drought prediction by using data-mining approach. Atmósfera. 25(1). 85–105. 6 indexed citations
20.
Sattari, Mohammad Taghi, Kadri Yürekli, & Ali Ünlükara. (2011). Drought prediction by using artificial neural networks approach in Karaman Province.. DergiPark (Istanbul University). 4(1). 7–13. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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