Journals
12. Kishore, S. N., Mandal, S. & Thakur, M. (2025). Domain-informed multi-step wind speed forecasting: evaluating extreme wind conditions and seasonal variations. Earth Sci Inform 18, 79.
DOI: https://doi.org/10.1007/s12145-024-01493-2
11. Kusuru, D., Turlapaty, A. C., & Thakur, M. (2024). An improved compound gaussian model for bivariate surface emg signals related to strength training. IEEE Transactions on Human-Machine Systems.
DOI: https://doi.org/10.1109/THMS.2024.3486450
10. Mandal, S., Boppani, S., Dasari, V., & Thakur, M. (2024). A bivariate simultaneous pollutant forecasting approach by Unified Spectro-Spatial Graph Neural Network (USSGNN) and its application in prediction of O3 and NO2 for New Delhi, India. Sustainable Cities and Society, 114, 105741.
DOI: https://doi.org/10.1016/j.scs.2024.105741
9. Thakur, M., Mandal, S., Manohar, P., & Chatterjee, S. (2024). Spatio-temporal characteristics of particulate matter in Delhi, India due to the combined effects of fireworks and crop burning during pre-COVID festival seasons. Natural Hazards, 1-28.
DOI : https://doi.org/10.1007/s11069-024-06606-0
8. Gorripati, R., Thakur, M., & Kolagani, N. (2023). Promoting Climate Resilient Sustainable Agriculture through Participatory System Dynamics with Crop-Water-Income Dynamics. Water Resources Management, 37, 3935–3951. [Published, Impact Factor: 4.3]
DOI : https://doi.org/10.1007/s11269-023-03533-w
7. Mandal, S., & Thakur, M. (2023). A city-based PM2. 5 forecasting framework using Spatially Attentive Cluster-based Graph Neural Network model. Journal of Cleaner Production, 405, 137036. [Published, Impact Factor: 11.1]
DOI : https://doi.org/10.1016/j.jclepro.2023.137036
6. Ejurothu, P. S. S., Mandal, S., & Thakur, M. (2022). Forecasting PM2. 5 Concentration in India Using a Cluster Based Hybrid Graph Neural Network Approach. Asia-Pacific Journal of Atmospheric Sciences, 59, 545–561. [Published, Impact Factor: 2.3]
DOI: https://doi.org/10.1007/s13143-022-00291-4
5. Gorripati, R., Thakur, M., & Kolagani, N. (2022) A framework for optimal rank identification of resource management systems using probabilistic approaches in analytic hierarchy process. Water Policy, 24 (6): 878–898 [Published, Impact Factor: 1.6]
DOI: https://doi.org/10.2166/wp.2022.236
4. Reddy, G. V., Mukherjee, S., & Thakur, M. (2020). Measuring photography aesthetics with deep CNNs. IET Image Processing, 14(8), 1561-1570. [Published, Impact Factor: 2.3]
DOI: https://doi.org/10.1049/iet-ipr.2019.1300
3. Thakur, M., Samanta, B., & Chakravarty, D. (2018). A non-stationary geostatistical approach to multigaussian kriging for local reserve estimation. Stochastic Environmental Research and Risk Assessment, 32, 2381–2404 [Published, Impact Factor: 4.2]
DOI: https://doi.org/10.1007/s00477-018-1533-1
2. Thakur, M., Samanta, B., & Chakravarty, D. (2016). A non-stationary spatial approach to disjunctive kriging in reserve estimation. Spatial Statistics, 17: 131- 160. [Published, Impact Factor: 2.3]
DOI: https://doi.org/10.1016/j.spasta.2016.06.001
1. Thakur, M., Samanta, B., & Chakravarty, D. (2014). Support and Information Effect Modeling for Recoverable Reserve Estimation of a Beach Sand Deposit in India. Natural resources research, 23(2): 231-245. [Published, Impact Factor: 5.4]
DOI: https://doi.org/10.1007/s11053-013-9225-5
Conferences
Mandal, S., Thakur, M., Balamurugan, V., Chen, J., & Roy, A. (2025). A Deep-Pollutant-Spatial-Operator-Network (DPSON) for spatial estimation of PM2. 5, PM10, O3 and NO2, case study at Delhi, India (No. EGU25-20076). Copernicus Meetings.
Mandal, S., Balamurugan, V., Datla, M. V., Thakur, M., Chen, J., & Roy, A. (2024, December). Enhancing PM2. 5 Prediction in Urban Areas Using Satellite-Derived AOD Data: A Comparative Analysis of MODIS and VIIRS Products with Machine Learning Models. In AGU Fall Meeting Abstracts (Vol. 2024, No. 1792, pp. A21F-1792).
Mandal, S., Kommireddy, P., Kancharla, N. A., Thakur, M., Das, K., & Jalagam, P. R. (2024, December). Advancing Total Suspended Solids Modeling Using Machine Learning and Remote Sensing Data. In AGU Fall Meeting Abstracts (Vol. 2024, pp. H23R-08).
Mandal, S., Das, K., Thakur, M., Padmanaban, M. & Hazra, J. (2024, July). Improved Dissolved Organic Carbon Prediction in Diverse Inland Water Bodies: Utilizing Machine Learning and Remote Sensing. In IEEE International Geoscience and Remote Sensing Symposium.
Imran, S., Mandal, S., Goswami, A., Thakur, M., & Raju, A. (2024, July). A Novel Deep Learning-based Landsat 7 ETM+ Multi-Spectral to Hyperspectral Reconstruction Model: Application for Water Bodies in an Indian Region. In IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium (pp. 3121-3124). IEEE.
Mandal, S. & Thakur, M. (2023) A Study on the Estimation of Surface Ozone Pollution in the Indian Megacity, Delhi at pre-, during- and post-COVID Years using Statistical and Machine Learning Models. In 9th International Congress on Environmental Geotechnics (9ICEG), Volume 5, June 25th to June 28th 2023 in Chania, Crete, Greece. DOI: https://doi.org/10.53243/ICEG2023-422
Bhojanapalli, A., Mandal, S., Thakur, M., & Das, K. (2023, July). A CALIPSO Observation Based 3-Dimensional Tropospheric Aerosol Classification Model Over the Indian City Delhi. In IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium (pp. 305-308). IEEE. DOI: 10.1109/IGARSS52108.2023.10282609
Ejurothu, P. S. S., Mandal, S., & Thakur, M. (2023). A Machine Learning Approach for PM2. 5 Estimation for the Capital City of New Delhi Using Multispectral LANDSAT-8 Satellite Observations. In Computer Vision and Machine Intelligence: Proceedings of CVMI 2022 (pp. 389-400). Singapore: Springer Nature Singapore. DOI:https://doi.org/10.1007/978-981-19-7867-8_31
Mandal, S., Thakur, M., Turlapaty, A. C., Shaik, R. U., & Giovanni, L. (2022, July). Application of Prisma Hyperspectral Data for PM 2.5 Estimation: A Case Study on New Delhi, India. In IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 5069-5072). IEEE. DOI: https://doi.org/10.1109/IGARSS46834.2022.9884594
Kusuru, D., B. N. Jyothi V, Imandi, R., Turlapaty, A.C., & Thakur, M. (2022, February). A Gaussian Gamma mixture model for Indian ocean surface wind speed. In OCEANS 2022-Chennai (pp. 1-5). IEEE. DOI: https://doi.org/10.1109/OCEANSChennai45887.2022.9775268
Kishore, S. N., Thakur, M., & Mandal, S. (2022, February). Forecasting of Wind Speed at Offshore Wind Site-A Case Study. In OCEANS 2022-Chennai (pp. 1-6). IEEE. DOI: https://doi.org/10.1109/OCEANSChennai45887.2022.9775427
Kusuru, D., Turlapaty, A. C., & Thakur, M. (2021, November). A Laplacian- Gaussian Mixture Model for Surface EMG Signals from Upper Limbs. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 681-685). IEEE. DOI: https://doi.org/10.1109/EMBC46164.2021.9630143
Das, K., Mandal, S., & Thakur, M. (2020). High resolution spatial mapping of soil nutrients using k-nearest neighbor based cnn approach. In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium (pp. 1102-1105). IEEE. DOI: https://doi.org/10.1109/IGARSS39084.2020.9324149
Thakur, M., Samanta, B., & Chakravarty, D. (2015). Comparison between disjunctive kriging and multi-Gaussian kriging to estimate the recoverable reserve for a beach sand deposit in India, Proceedings of Application of Computers and Operations Research in the Mineral Industry (APCOM 2015), Fairbanks, Alaska, USA, May, 2015.
Thakur, M., Samanta, B., & Chakravarty, D. (2014). A Nonstationary Nonlinear Geostatistical Model and its Application in a Beach Sand Deposit for Recoverable Reserve Estimation, Proceedings of International Association for Mathematical Geology (IAMG), Annual Conference in New Delhi, India, October, 2014. DOI: https://doi.org/10.1007/978-3-319-18663-4_98