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Performance Evaluation of Deep Learning Models for Water Quality Index Prediction: A Comparative Study of LSTM, TCN, ANN, and MLP

Fuente: arXiv

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Bibliographische Detailangaben
Hauptverfasser:	Ismail, Muhammad, Abbas, Farkhanda, Shah, Shahid Munir, Aljawarneh, Mahmoud, Dhomeja, Lachhman Das, Abbas, Fazila, Shoaib, Muhammad, Alrefaei, Abdulwahed Fahad, Albeshr, Mohammed Fahad
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Machine Learning
Online-Zugang:	Acceder al recurso
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_version_	1866912102941196288
author	Ismail, Muhammad Abbas, Farkhanda Shah, Shahid Munir Aljawarneh, Mahmoud Dhomeja, Lachhman Das Abbas, Fazila Shoaib, Muhammad Alrefaei, Abdulwahed Fahad Albeshr, Mohammed Fahad
author_facet	Ismail, Muhammad Abbas, Farkhanda Shah, Shahid Munir Aljawarneh, Mahmoud Dhomeja, Lachhman Das Abbas, Fazila Shoaib, Muhammad Alrefaei, Abdulwahed Fahad Albeshr, Mohammed Fahad
contents	Environmental monitoring and predictive modeling of the Water Quality Index (WQI) through the assessment of the water quality.
format	Preprint
id	arxiv_https___arxiv_org_abs_2411_01527
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Performance Evaluation of Deep Learning Models for Water Quality Index Prediction: A Comparative Study of LSTM, TCN, ANN, and MLP Ismail, Muhammad Abbas, Farkhanda Shah, Shahid Munir Aljawarneh, Mahmoud Dhomeja, Lachhman Das Abbas, Fazila Shoaib, Muhammad Alrefaei, Abdulwahed Fahad Albeshr, Mohammed Fahad Machine Learning Environmental monitoring and predictive modeling of the Water Quality Index (WQI) through the assessment of the water quality.
title	Performance Evaluation of Deep Learning Models for Water Quality Index Prediction: A Comparative Study of LSTM, TCN, ANN, and MLP
topic	Machine Learning
url	https://arxiv.org/abs/2411.01527