River Water Quality Prediction and Analysis�Deep Learning Predictive Models Approach

In depth research about river water qualities are no more outlandish nowadays due to river water pollutions and contaminations. In order to have an accurate and precise measurement taken towards these river water pollution, advanced and new technologies need to be applied rather than old technique o...

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Main Authors: Rizal N.N.M., Hayder G., Yussof S.
Other Authors: 57654708600
Format: Conference Paper
Published: Springer Nature 2024
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spelling my.uniten.dspace-345452024-10-14T11:20:33Z River Water Quality Prediction and Analysis�Deep Learning Predictive Models Approach Rizal N.N.M. Hayder G. Yussof S. 57654708600 56239664100 16023225600 ANFIS Deep learning River River water quality prediction Water quality In depth research about river water qualities are no more outlandish nowadays due to river water pollutions and contaminations. In order to have an accurate and precise measurement taken towards these river water pollution, advanced and new technologies need to be applied rather than old technique of everyday lab testing. Therefore, with the usage of deep learning predictive models approach, the decision makers able to provide immediate response and give precautionary measures to prevent a disastrous event. In the current research, Adaptive Neuro-fuzzy Inference System (ANFIS) has been used to predict six different types of river water quality parameters at Langat River, Malaysia. Root mean square error (RMSE) and determination of coefficient (R2) were used to assess the performances of the models. The results have been proven that ANFIS able to predict the parameters of river water quality as ANFIS Model 5 has achieved the highest value of R2 (0.9712). It also obtained the low values of RMSE which were 0.0028, 0.0144 and 0.0924 for training, testing and checking data sets, respectively. Overall, the six ANFIS models have successfully predict six different water quality parameters. � 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. Final 2024-10-14T03:20:33Z 2024-10-14T03:20:33Z 2023 Conference Paper 10.1007/978-3-031-26580-8_5 2-s2.0-85161600571 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161600571&doi=10.1007%2f978-3-031-26580-8_5&partnerID=40&md5=970aef1335ace964658e737a6438d362 https://irepository.uniten.edu.my/handle/123456789/34545 25 29 Springer Nature Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic ANFIS
Deep learning
River
River water quality prediction
Water quality
spellingShingle ANFIS
Deep learning
River
River water quality prediction
Water quality
Rizal N.N.M.
Hayder G.
Yussof S.
River Water Quality Prediction and Analysis�Deep Learning Predictive Models Approach
description In depth research about river water qualities are no more outlandish nowadays due to river water pollutions and contaminations. In order to have an accurate and precise measurement taken towards these river water pollution, advanced and new technologies need to be applied rather than old technique of everyday lab testing. Therefore, with the usage of deep learning predictive models approach, the decision makers able to provide immediate response and give precautionary measures to prevent a disastrous event. In the current research, Adaptive Neuro-fuzzy Inference System (ANFIS) has been used to predict six different types of river water quality parameters at Langat River, Malaysia. Root mean square error (RMSE) and determination of coefficient (R2) were used to assess the performances of the models. The results have been proven that ANFIS able to predict the parameters of river water quality as ANFIS Model 5 has achieved the highest value of R2 (0.9712). It also obtained the low values of RMSE which were 0.0028, 0.0144 and 0.0924 for training, testing and checking data sets, respectively. Overall, the six ANFIS models have successfully predict six different water quality parameters. � 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
author2 57654708600
author_facet 57654708600
Rizal N.N.M.
Hayder G.
Yussof S.
format Conference Paper
author Rizal N.N.M.
Hayder G.
Yussof S.
author_sort Rizal N.N.M.
title River Water Quality Prediction and Analysis�Deep Learning Predictive Models Approach
title_short River Water Quality Prediction and Analysis�Deep Learning Predictive Models Approach
title_full River Water Quality Prediction and Analysis�Deep Learning Predictive Models Approach
title_fullStr River Water Quality Prediction and Analysis�Deep Learning Predictive Models Approach
title_full_unstemmed River Water Quality Prediction and Analysis�Deep Learning Predictive Models Approach
title_sort river water quality prediction and analysis�deep learning predictive models approach
publisher Springer Nature
publishDate 2024
_version_ 1814061126703382528
score 13.222552