Zobrazeno 1 - 10 of 30 pro vyhledávání: '"Kironmala Chanda"'
1
Akademický článek
Potential of Deep Learning in drought assessment by extracting information from hydrometeorological precursors
Autor: Rajib Maity, Mohd Imran Khan, Subharthi Sarkar, Riya Dutta, Subhra Sekhar Maity, Manali Pal, Kironmala Chanda
Publikováno v: Journal of Water and Climate Change, Vol 12, Iss 6, Pp 2774-2796 (2021)
This study explores the potential of the Deep Learning (DL) approach to develop a model for basin-scale drought assessment using information from a set of primary hydrometeorological precursors, namely air temperature, surface pressure, wind speed, r
Externí odkaz: https://doaj.org/article/752bb8734eb9453f885d498ec3e5dc41
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2
Akademický článek
Contrasting features of hydroclimatic teleconnections and the predictability of seasonal rainfall over east and west Japan
Autor: Rajib Maity, Kironmala Chanda, Riya Dutta, J. V. Ratnam, Masami Nonaka, Swadhin Behera
Publikováno v: Meteorological Applications, Vol 27, Iss 1, Pp n/a-n/a (2020)
Abstract Hydroclimatic teleconnections between global sea surface temperature (SST) anomaly fields and monthly rainfall over east and west Japan (divided along 138° E longitude) are identified for summer (June–August) and winter (December–Februa
Externí odkaz: https://doaj.org/article/96147a09075347f8b61657779aeac1a5
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5
Comparison of Neural network based and Kernel based Machine Learning approaches for daily forecasting of reference evapotranspiration in data scarce regions
Autor: Nehar Mandal, Kironmala Chanda
Efficient estimation and forecast of reference evapotranspiration (ETO) is crucial for water resources management and for developing an efficient irrigation practice that will help better utilization of scanty water resources. This is a more challeng
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::55ae56f23ed23450d4c82d38f62b42df
https://doi.org/10.5194/egusphere-egu23-11950
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6
Influence of large-scale climate modes and local hydrometeorological factors in predicting basin scale rainfall and streamflow: A Bayesian network approach
Autor: Prabal Das, Kironmala Chanda
This article reports the findings of a recent study by Das and Chanda (2022), wherein a Bayesian Network (BN) approach was applied to analyze the influence of large-scale climate modes and local hydro-meteorological variables on streamflow and rainfa
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ccb335f6e083653f025327d6642f8757
https://doi.org/10.5194/egusphere-egu23-3826
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