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    Geographical Transferability of Pretrained K-Means Clustering–Artificial Neural Network Model for Disaggregation of Rainfall Data in an Indian Monsoon Climate

    Source: Journal of Hydrologic Engineering:;2023:;Volume ( 028 ):;issue: 012::page 04023040-1
    Author:
    Debarghya Bhattacharyya
    ,
    Ujjwal Saha
    DOI: 10.1061/JHYEFF.HEENG-6058
    Publisher: ASCE
    Abstract: High temporal resolution rainfall data are among the most demanded resources for water resource engineers. In modern times, this need has only multiplied day by day due to the need for training large parameter-heavy models for the prediction of climatic features, analysis of extreme rainfall, etc. However, the availability of such high temporal resolution data is low, which can cause hindrances in research or development projects in several regions. It is therefore imperative to find newer and better models for the disaggregation of rainfall data from lower to higher temporal resolutions, such as a model that uses deep learning neural networks. The main issue with such a model is the requirement for historical rainfall data at different time scales for training, testing, and validating prior to use in practical scenarios, data that may not always be available for all regions necessary. In this paper, an attempt has been to test the accuracy and applicability of pretrained models for the purpose of disaggregating rainfall in other geographical locations, thus reducing the requirement for historical rainfall data for training and validation purposes. A large data set comprising rainfall data from 68 rain gauge stations across the Indian subcontinent has been used to test models pretrained using rainfall data from seven major stations in India (Bikaner, Chennai, Cherrapunji, Delhi, Kolkata, Mumbai, and Mangalore). The pretrained models are tested in their ability to conserve extreme rainfall characteristics by comparing intensity–duration–frequency (IDF) curves generated from observed and disaggregated rainfall, further which the errors in these IDF curves are used to generate heatmaps for the country using the inverse distance weighted interpolation method. At the end of this paper, a map is provided that covers the entire country of study, detailing that a pretrained model can be used for a certain region based on its accuracy of disaggregation and proximity to the city of pretraining data.
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      Geographical Transferability of Pretrained K-Means Clustering–Artificial Neural Network Model for Disaggregation of Rainfall Data in an Indian Monsoon Climate

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4296075
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    contributor authorDebarghya Bhattacharyya
    contributor authorUjjwal Saha
    date accessioned2024-04-27T20:50:24Z
    date available2024-04-27T20:50:24Z
    date issued2023/12/01
    identifier other10.1061-JHYEFF.HEENG-6058.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4296075
    description abstractHigh temporal resolution rainfall data are among the most demanded resources for water resource engineers. In modern times, this need has only multiplied day by day due to the need for training large parameter-heavy models for the prediction of climatic features, analysis of extreme rainfall, etc. However, the availability of such high temporal resolution data is low, which can cause hindrances in research or development projects in several regions. It is therefore imperative to find newer and better models for the disaggregation of rainfall data from lower to higher temporal resolutions, such as a model that uses deep learning neural networks. The main issue with such a model is the requirement for historical rainfall data at different time scales for training, testing, and validating prior to use in practical scenarios, data that may not always be available for all regions necessary. In this paper, an attempt has been to test the accuracy and applicability of pretrained models for the purpose of disaggregating rainfall in other geographical locations, thus reducing the requirement for historical rainfall data for training and validation purposes. A large data set comprising rainfall data from 68 rain gauge stations across the Indian subcontinent has been used to test models pretrained using rainfall data from seven major stations in India (Bikaner, Chennai, Cherrapunji, Delhi, Kolkata, Mumbai, and Mangalore). The pretrained models are tested in their ability to conserve extreme rainfall characteristics by comparing intensity–duration–frequency (IDF) curves generated from observed and disaggregated rainfall, further which the errors in these IDF curves are used to generate heatmaps for the country using the inverse distance weighted interpolation method. At the end of this paper, a map is provided that covers the entire country of study, detailing that a pretrained model can be used for a certain region based on its accuracy of disaggregation and proximity to the city of pretraining data.
    publisherASCE
    titleGeographical Transferability of Pretrained K-Means Clustering–Artificial Neural Network Model for Disaggregation of Rainfall Data in an Indian Monsoon Climate
    typeJournal Article
    journal volume28
    journal issue12
    journal titleJournal of Hydrologic Engineering
    identifier doi10.1061/JHYEFF.HEENG-6058
    journal fristpage04023040-1
    journal lastpage04023040-19
    page19
    treeJournal of Hydrologic Engineering:;2023:;Volume ( 028 ):;issue: 012
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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