Siam Maksud
Focus: Computational Hydrology, Watershed Modelling and Nutrient Management, Physics Informed Machine Learning application in Soil Moisture detection.
Siam's research focuses on bridging physics informed machine learning with hydrological models. He is also working on developing hydrological model to distribute nutrient load from dairy farm outputs. The goal of his research is to find insight into catchment responses to various atmospheric variables and understand the role of antecedent soil moisture in it.
Maksud, S., Asfaw, B. W., Fuka, D. R., & Easton, Z. M. (2024, December). Physics-Informed Machine Learning for Downscaling Remote Sensing Soil Moisture Products. In AGU Fall Meeting Abstracts (Vol. 2024, pp. H12F-08).
Asfaw, B. W., Maksud, S., Fuka, D. R., Collick, A., White, R. R., & Easton, Z. M. (2024, December). Integrating Soil Moisture Informed Topographic Index Classes, Satellite Soil Moisture Data and Streamflow for Enhanced Identification of Soil Wetness Patterns. In AGU Fall Meeting Abstracts (Vol. 2024, No. 109, pp. H53B-109).
Batarseh, F. A., Kulkarni, A., Sreng, C., Lin, J., & Maksud, S. (2023). ACWA: An AI-driven cyber-physical testbed for intelligent water systems. Water Practice & Technology, 18(12), 3399-3418.
Maksud, S., Hannan, A. B., & Jahan, N. (2023). Assessing the potential of satellite-retrieved and global land data assimilation system-simulated soil moisture datasets for soil moisture mapping in Bangladesh. Authorea Preprints.