Formulation of fitness function to estimate pH value of adjacent block via pH value and water flow
Water quality is measured by several factors, such as the potential of hydrogen (pH), the concentration of dissolved oxygen (DO), bacteria levels, salinity, or turbidity. This project focuses on the pH of water as it gives more impact on determining the quality of water. It is noticed that the speed...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
Springer International Publishing
2024
|
| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/46751/1/Formulation%20of%20fitness%20function%20to%20estimate%20pH%20value%20of%20adjacent%20block.pdf https://umpir.ump.edu.my/id/eprint/46751/ http://doi.org/10.1007/978-981-97-3851-9_50 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Water quality is measured by several factors, such as the potential of hydrogen (pH), the concentration of dissolved oxygen (DO), bacteria levels, salinity, or turbidity. This project focuses on the pH of water as it gives more impact on determining the quality of water. It is noticed that the speed of water flow does manipulate the value of pH water. A large set of data which comprises five locations, four of the locations pH and water speed are used to determine the fifth location pH (known as unsampled pH). To estimate the un-sampled pH, a fitness function has been formulated using Multi-Layer Neural Network by Genetic Algorithm (MLNN-GA) and compares the results in terms of accuracy between the estimation of pH without water speed and pH with water speed. Both estimated results will be compared with the actual pH value. The results of the estimated data pH with speed is 94.27% compared to the estimated data without speed is 93.83%. The result showed that speed is one of the factors that can be used to increase accuracy in estimating the pH value. |
|---|