In-depth simulation of rainfall–runoff relationships using machine learning methods

Mehdi Fuladipanah; Alireza Shahhosseini; Namal Rathnayake; Hazi Md Azamathulla; Upaka Rathnayake; D. P. P. Meddage; Kiran Tota-Maharaj

doi:10.2166/wpt.2024.147

In-depth simulation of rainfall–runoff relationships using machine learning methods

Mehdi Fuladipanah, Alireza Shahhosseini, Namal Rathnayake, Hazi Md Azamathulla, Upaka Rathnayake, D. P. P. Meddage, Kiran Tota-Maharaj

Department of Civil Engineering & Construction

Research output: Contribution to journal › Article › peer-review

Original language	Undefined/Unknown
Journal	Water Practice & Technology
DOIs	https://doi.org/10.2166/wpt.2024.147
Publication status	Published - 1 Jun 2024

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10.2166/wpt.2024.147

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author = "Mehdi Fuladipanah and Alireza Shahhosseini and Namal Rathnayake and Azamathulla, {Hazi Md} and Upaka Rathnayake and Meddage, {D. P. P.} and Kiran Tota-Maharaj",

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T1 - In-depth simulation of rainfall–runoff relationships using machine learning methods

AU - Fuladipanah, Mehdi

AU - Shahhosseini, Alireza

AU - Rathnayake, Namal

AU - Azamathulla, Hazi Md

AU - Rathnayake, Upaka

AU - Meddage, D. P. P.

AU - Tota-Maharaj, Kiran

PY - 2024/6/1

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UR - https://doi.org/10.2166/wpt.2024.147

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DO - 10.2166/wpt.2024.147

M3 - Article

JO - Water Practice & Technology

JF - Water Practice & Technology

ER -

In-depth simulation of rainfall–runoff relationships using machine learning methods

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