Forecasting the Water Quality Class in a river basin using an artificial neural network with the softmax activation function
Classification of river water quality needs an efficient method to reduce energy, save time and decrease the risk of errors. This study describes the application of an Artificial Neural Network (ANN) with the softmax activation function to forecast the Water Quality Class (WQC) under the National Wa...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Published: |
Asian Research Publishing Network (ARPN)
2019
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/79938/ |
| _version_ | 1848858792367751168 |
|---|---|
| author | Azhar, Shah Christirani Aris, Ahmad Zaharin Yusoff, Mohd Kamil Ramli, Mohammad Firuz |
| author_facet | Azhar, Shah Christirani Aris, Ahmad Zaharin Yusoff, Mohd Kamil Ramli, Mohammad Firuz |
| author_sort | Azhar, Shah Christirani |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Classification of river water quality needs an efficient method to reduce energy, save time and decrease the risk of errors. This study describes the application of an Artificial Neural Network (ANN) with the softmax activation function to forecast the Water Quality Class (WQC) under the National Water Quality Standard (NWQS) of the Muda River Basin (MRB) (Malaysia). The water quality was classified automatically without Water Quality Index (WQI) calculation. Two different sets of Water Quality Variables (WQVs) were applied as input variables. The modelling discover that the optimal network architecture was the 1:6-1:6-1:1 and used a 60-20-20% splitting plan. ANN1 with the six WQVs was selected to predict the WQC in the MRB. Predictions of the WQC rendered by this model for the training set were very accurate (96.8% correct, Percent Incorrect Prediction (PIP) = 3.2, CEE = 3.44). The approach presented is a very useful and offers a compelling alternative to forecasting of river class, mainly because WQI calculation involves a complex and lengthy calculations. Subsequently, this approach could be applied to water quality classification in other river basins for better water quality management. |
| first_indexed | 2025-11-15T12:19:04Z |
| format | Article |
| id | upm-79938 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T12:19:04Z |
| publishDate | 2019 |
| publisher | Asian Research Publishing Network (ARPN) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-799382023-03-30T03:44:25Z http://psasir.upm.edu.my/id/eprint/79938/ Forecasting the Water Quality Class in a river basin using an artificial neural network with the softmax activation function Azhar, Shah Christirani Aris, Ahmad Zaharin Yusoff, Mohd Kamil Ramli, Mohammad Firuz Classification of river water quality needs an efficient method to reduce energy, save time and decrease the risk of errors. This study describes the application of an Artificial Neural Network (ANN) with the softmax activation function to forecast the Water Quality Class (WQC) under the National Water Quality Standard (NWQS) of the Muda River Basin (MRB) (Malaysia). The water quality was classified automatically without Water Quality Index (WQI) calculation. Two different sets of Water Quality Variables (WQVs) were applied as input variables. The modelling discover that the optimal network architecture was the 1:6-1:6-1:1 and used a 60-20-20% splitting plan. ANN1 with the six WQVs was selected to predict the WQC in the MRB. Predictions of the WQC rendered by this model for the training set were very accurate (96.8% correct, Percent Incorrect Prediction (PIP) = 3.2, CEE = 3.44). The approach presented is a very useful and offers a compelling alternative to forecasting of river class, mainly because WQI calculation involves a complex and lengthy calculations. Subsequently, this approach could be applied to water quality classification in other river basins for better water quality management. Asian Research Publishing Network (ARPN) 2019 Article PeerReviewed Azhar, Shah Christirani and Aris, Ahmad Zaharin and Yusoff, Mohd Kamil and Ramli, Mohammad Firuz (2019) Forecasting the Water Quality Class in a river basin using an artificial neural network with the softmax activation function. Journal of Engineering and Applied Sciences, 14 (23). pp. 8585-8593. ISSN 1819-6608 https://medwelljournals.com/abstract/?doi=jeasci.2019.8585.8593 10.36478/jeasci.2019.8585.8593 |
| spellingShingle | Azhar, Shah Christirani Aris, Ahmad Zaharin Yusoff, Mohd Kamil Ramli, Mohammad Firuz Forecasting the Water Quality Class in a river basin using an artificial neural network with the softmax activation function |
| title | Forecasting the Water Quality Class in a river basin using an artificial neural network with the softmax activation function |
| title_full | Forecasting the Water Quality Class in a river basin using an artificial neural network with the softmax activation function |
| title_fullStr | Forecasting the Water Quality Class in a river basin using an artificial neural network with the softmax activation function |
| title_full_unstemmed | Forecasting the Water Quality Class in a river basin using an artificial neural network with the softmax activation function |
| title_short | Forecasting the Water Quality Class in a river basin using an artificial neural network with the softmax activation function |
| title_sort | forecasting the water quality class in a river basin using an artificial neural network with the softmax activation function |
| url | http://psasir.upm.edu.my/id/eprint/79938/ http://psasir.upm.edu.my/id/eprint/79938/ http://psasir.upm.edu.my/id/eprint/79938/ |