Zobrazeno 1 - 10 of 318 pro vyhledávání: '"Gupta, Hoshin V."'
1
Report
Towards Interpretable Physical-Conceptual Catchment-Scale Hydrological Modeling using the Mass-Conserving-Perceptron
Autor: Wang, Yuan-Heng, Gupta, Hoshin V.
We investigate the applicability of machine learning technologies to the development of parsimonious, interpretable, catchment-scale hydrologic models using directed-graph architectures based on the mass-conserving perceptron (MCP) as the fundamental
Externí odkaz: http://arxiv.org/abs/2401.14521
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2
Report
A Mass-Conserving-Perceptron for Machine Learning-Based Modeling of Geoscientific Systems
Autor: Wang, Yuan-Heng, Gupta, Hoshin V.
Although decades of effort have been devoted to building Physical-Conceptual (PC) models for predicting the time-series evolution of geoscientific systems, recent work shows that Machine Learning (ML) based Gated Recurrent Neural Network technology c
Externí odkaz: http://arxiv.org/abs/2310.08644
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3
Akademický článek
Using satellite-based evapotranspiration estimates to improve the structure of a simple conceptual rainfall–runoff model
Autor: Roy, Tirthankar, Gupta, Hoshin V., Serrat-Capdevila, Aleix, Valdes, Juan B.
Daily, quasi-global (50° N–S and 180° W–E), satellite-based estimates of actual evapotranspiration at 0.25° spatial resolution have recently become available, generated by the Global Land Evaporation Amsterdam Model (GLEAM). We investigate the
Externí odkaz: http://hdl.handle.net/10150/623239
http://arizona.openrepository.com/arizona/handle/10150/623239
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4
Akademický článek
Modeling the distributed effects of forest thinning on the long-term water balance and streamflow extremes for a semi-arid basin in the southwestern US
Autor: Moreno, Hernan A., Gupta, Hoshin V., White, Dave D., Sampson, David A.
To achieve water resource sustainability in the water-limited southwestern US, it is critical to understand the potential effects of proposed forest thinning on the hydrology of semi-arid basins, where disturbances to headwater catchments can cause s
Externí odkaz: http://hdl.handle.net/10150/617399
http://arizona.openrepository.com/arizona/handle/10150/617399
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7
Report
Nowcasting-Nets: Deep Neural Network Structures for Precipitation Nowcasting Using IMERG
Autor: Ehsani, Mohammad Reza, Zarei, Ariyan, Gupta, Hoshin V., Barnard, Kobus, Behrangi, Ali
Accurate and timely estimation of precipitation is critical for issuing hazard warnings (e.g., for flash floods or landslides). Current remotely sensed precipitation products have a few hours of latency, associated with the acquisition and processing
Externí odkaz: http://arxiv.org/abs/2108.06868
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8
Report
Computing Accurate Probabilistic Estimates of One-D Entropy from Equiprobable Random Samples
Autor: Gupta, Hoshin V, Ehsani, Mohammed Reza, Roy, Tirthankar, Sans-Fuentes, Maria A, Ehret, Uwe, Behrangi, Ali
We develop a simple Quantile Spacing (QS) method for accurate probabilistic estimation of one-dimensional entropy from equiprobable random samples, and compare it with the popular Bin-Counting (BC) method. In contrast to BC, which uses equal-width bi
Externí odkaz: http://arxiv.org/abs/2102.12675
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10
Akademický článek
On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data.
Autor: Álvarez Chaves, Manuel1 (AUTHOR) manuel.alvarez-chaves@simtech.uni-stuttgart.de, Gupta, Hoshin V.2 (AUTHOR), Ehret, Uwe3 (AUTHOR), Guthke, Anneli1 (AUTHOR)
Publikováno v: Entropy. May2024, Vol. 26 Issue 5, p387. 34p.
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