Ata Akbari Asanjan

1.4k total citations
25 papers, 1.0k citations indexed

About

Ata Akbari Asanjan is a scholar working on Global and Planetary Change, Atmospheric Science and Environmental Engineering. According to data from OpenAlex, Ata Akbari Asanjan has authored 25 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Global and Planetary Change, 12 papers in Atmospheric Science and 8 papers in Environmental Engineering. Recurrent topics in Ata Akbari Asanjan's work include Precipitation Measurement and Analysis (11 papers), Meteorological Phenomena and Simulations (11 papers) and Climate variability and models (6 papers). Ata Akbari Asanjan is often cited by papers focused on Precipitation Measurement and Analysis (11 papers), Meteorological Phenomena and Simulations (11 papers) and Climate variability and models (6 papers). Ata Akbari Asanjan collaborates with scholars based in United States, Canada and Iran. Ata Akbari Asanjan's co-authors include Soroosh Sorooshian, Kuolin Hsu, Tiantian Yang, Xiaomang Liu, Xiaogang Gao, E. Welles, Pari‐Sima Katiraie‐Boroujerdy, Phu Nguyen, Mojtaba Sadeghi and Junqiang Lin and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Water Resources Research.

In The Last Decade

Ata Akbari Asanjan

25 papers receiving 995 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ata Akbari Asanjan United States 14 527 451 399 268 75 25 1.0k
Jiufu Liu China 18 674 1.3× 378 0.8× 340 0.9× 533 2.0× 112 1.5× 69 1.2k
S. Adarsh India 22 769 1.5× 208 0.5× 481 1.2× 345 1.3× 91 1.2× 141 1.4k
Guangyuan Kan China 22 858 1.6× 312 0.7× 406 1.0× 554 2.1× 80 1.1× 54 1.3k
Shivam Tripathi India 19 1.1k 2.1× 624 1.4× 418 1.0× 502 1.9× 70 0.9× 60 1.7k
Leonardo Alfonso Netherlands 21 506 1.0× 251 0.6× 375 0.9× 539 2.0× 232 3.1× 59 1.1k
Md Abul Ehsan Bhuiyan United States 19 401 0.8× 401 0.9× 342 0.9× 147 0.5× 17 0.2× 33 938
Christian Charron Canada 17 531 1.0× 184 0.4× 381 1.0× 316 1.2× 29 0.4× 33 1.0k
Farshad Ahmadi Iran 19 649 1.2× 169 0.4× 506 1.3× 388 1.4× 83 1.1× 52 1.1k
Ke‐Sheng Cheng Taiwan 18 565 1.1× 336 0.7× 393 1.0× 203 0.8× 54 0.7× 59 1.1k

Countries citing papers authored by Ata Akbari Asanjan

Since Specialization
Citations

This map shows the geographic impact of Ata Akbari Asanjan'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 Ata Akbari Asanjan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ata Akbari Asanjan more than expected).

Fields of papers citing papers by Ata Akbari Asanjan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ata Akbari Asanjan. 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 Ata Akbari Asanjan. The network helps show where Ata Akbari Asanjan may publish in the future.

Co-authorship network of co-authors of Ata Akbari Asanjan

This figure shows the co-authorship network connecting the top 25 collaborators of Ata Akbari Asanjan. A scholar is included among the top collaborators of Ata Akbari Asanjan 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 Ata Akbari Asanjan. Ata Akbari Asanjan 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.
Moftakhari, Hamed, David F. Muñoz, Ata Akbari Asanjan, et al.. (2024). Nonlinear Interactions of Sea‐Level Rise and Storm Tide Alter Extreme Coastal Water Levels: How and Why?. SHILAP Revista de lepidopterología. 5(2). 10 indexed citations
2.
Sorek‐Hamer, Meytar, Ata Akbari Asanjan, Esra Süel, et al.. (2023). Using deep transfer learning and satellite imagery to estimate urban air quality in data-poor regions. Environmental Pollution. 342. 122914–122914. 3 indexed citations
3.
Templin, Thomas J., et al.. (2023). Anomaly detection in aeronautics data with quantum-compatible discrete deep generative model. Machine Learning Science and Technology. 4(3). 35018–35018. 3 indexed citations
4.
Asanjan, Ata Akbari, Lucas T. Brady, Milad Memarzadeh, et al.. (2023). Quantum-Assisted Variational Segmentation for Image-to-Image Wildfire Detection Using Satellite Data. 624–626. 1 indexed citations
5.
Asanjan, Ata Akbari, et al.. (2023). Probabilistic Wildfire Segmentation Using Supervised Deep Generative Model from Satellite Imagery. Remote Sensing. 15(11). 2718–2718. 9 indexed citations
6.
Sorek‐Hamer, Meytar, Ata Akbari Asanjan, Esra Süel, et al.. (2022). A Deep Learning Approach for Meter-Scale Air Quality Estimation in Urban Environments Using Very High-Spatial-Resolution Satellite Imagery. Atmosphere. 13(5). 696–696. 9 indexed citations
7.
Süel, Esra, Meytar Sorek‐Hamer, Ata Akbari Asanjan, et al.. (2022). What You See Is What You Breathe? Estimating Air Pollution Spatial Variation Using Street-Level Imagery. Remote Sensing. 14(14). 3429–3429. 9 indexed citations
8.
Asanjan, Ata Akbari, et al.. (2022). Quantum-Compatible Variational Segmentation for Image-to-Image Wildfire Detection Using Satellite Data. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. 4919–4922. 5 indexed citations
9.
Memarzadeh, Milad, Ata Akbari Asanjan, & Bryan Matthews. (2022). Robust and Explainable Semi-Supervised Deep Learning Model for Anomaly Detection in Aviation. Aerospace. 9(8). 437–437. 19 indexed citations
10.
Alizadeh, Mohammad Reza, John T. Abatzoglou, Jan Adamowski, et al.. (2022). Increasing Heat‐Stress Inequality in a Warming Climate. Earth s Future. 10(2). 78 indexed citations
11.
Raei, Ehsan, et al.. (2022). A deep learning image segmentation model for agricultural irrigation system classification. Computers and Electronics in Agriculture. 198. 106977–106977. 34 indexed citations
12.
Gorooh, Vesta Afzali, Ata Akbari Asanjan, Phu Nguyen, Kuolin Hsu, & Soroosh Sorooshian. (2022). Deep Neural Network High Spatiotemporal Resolution Precipitation Estimation (Deep-STEP) Using Passive Microwave and Infrared Data. Journal of Hydrometeorology. 23(4). 597–617. 14 indexed citations
13.
Anjileli, Hassan, Laurie S. Huning, Hamed Moftakhari, et al.. (2021). Extreme heat events heighten soil respiration. Scientific Reports. 11(1). 6632–6632. 24 indexed citations
14.
Katiraie‐Boroujerdy, Pari‐Sima, et al.. (2020). Bias Correction of Satellite-Based Precipitation Estimations Using Quantile Mapping Approach in Different Climate Regions of Iran. Remote Sensing. 12(13). 2102–2102. 62 indexed citations
15.
Sadeghi, Mojtaba, Ata Akbari Asanjan, Phu Nguyen, et al.. (2019). PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Convolutional Neural Networks. Journal of Hydrometeorology. 20(12). 2273–2289. 130 indexed citations
16.
Sadeghi, Mojtaba, Ata Akbari Asanjan, Vesta Afzali Gorooh, et al.. (2019). Evaluation of PERSIANN-CDR Constructed Using GPCP V2.2 and V2.3 and A Comparison with TRMM 3B42 V7 and CPC Unified Gauge-Based Analysis in Global Scale. Remote Sensing. 11(23). 2755–2755. 22 indexed citations
17.
Asanjan, Ata Akbari. (2019). An Advanced Deep Learning Framework For Short-Term Precipitation Forecasting FromSatellite Information. 1 indexed citations
18.
Asanjan, Ata Akbari, Tiantian Yang, Kuolin Hsu, et al.. (2018). Short‐Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks. Journal of Geophysical Research Atmospheres. 123(22). 160 indexed citations
19.
Yang, Tiantian, Ata Akbari Asanjan, E. Welles, et al.. (2017). Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information. Water Resources Research. 53(4). 2786–2812. 256 indexed citations
20.
Asanjan, Ata Akbari, et al.. (2017). Short-range quantitative precipitation forecasting using Deep Learning approaches. AGUFM. 2017. 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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