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dc.contributor.authorHossain, Md Monowar
dc.contributor.supervisorFaisal Anwaren_US
dc.date.accessioned2022-08-10T01:37:02Z
dc.date.available2022-08-10T01:37:02Z
dc.date.issued2022en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11937/89149
dc.description.abstract

This study assesses the monthly precipitation of CMIP5 decadal experiment over Brisbane River catchment for a spatial resolution of 0.050 and then predicts the monthly precipitation for decadal timescale through a Bidirectional LSTM and Machine Learning Algorithms using GCMs and observed data. To use GCM data in this future prediction, investigations were carried out for a suitable spatial interpolation method, a better simulation period, model drifts, and drift correction alternatives based on different skill tests.

en_US
dc.publisherCurtin Universityen_US
dc.titleCMIP5 Decadal Precipitation at Catchment Level and Its Implication to Future Predictionen_US
dc.typeThesisen_US
dcterms.educationLevelPhDen_US
curtin.departmentSchool of Civil and Mechanical Engineeringen_US
curtin.accessStatusFulltext not availableen_US
curtin.facultyScience and Engineeringen_US
curtin.contributor.orcidHossain, Md Monowar [0000-0002-1814-2299]en_US
dc.date.embargoEnd2024-08-08


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