DEVELOPMENT OF ALGORITHM FOR WATER QUALITY MONITORING BASED ON SMARTPHONE IN VANAME SHRIMP PONDS, SUBANG REGENCY
The traditional method of water quality monitoring involves steps such as sample collection, transportation, laboratory testing, and data analysis, which are time-consuming and resourceintensive. These limitations have led to the need for more efficient and real-time monitoring approaches. Severa...
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Format: | Final Project |
Language: | Indonesia |
Subjects: | |
Online Access: | https://digilib.itb.ac.id/gdl/view/77548 |
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Institution: | Institut Teknologi Bandung |
Language: | Indonesia |
Summary: | The traditional method of water quality monitoring involves steps such as sample collection,
transportation, laboratory testing, and data analysis, which are time-consuming and resourceintensive.
These limitations have led to the need for more efficient and real-time monitoring
approaches. Several studies have indicated the potential of using smartphone sensors to collect data
on parameters such as atmosphere, chlorophyll-a, temperature, and seismic activity.
In this context, a simple mobile application like HydroColor can be utilized for crowdsourcing
water quality data, equivalent to traditional methods. An empirical model is developed through
multiple regression analysis, utilizing the reflectance of RGB wavelengths as independent variables
and observation data from the WQM Horiba U-50 device as dependent variables. A total of 48 data
parameters of salinity, temperature, and turbidity collected from vaname shrimp ponds in Subang
Regency during the third week of June 2023 were utilized in the development process of the empirical
model.
The empirical model for salinity, using a polynomial order of 4 (Sal = 13.448 – 6.542 (KB) +
15.938 (KB)2 + 150.169 (KB)3 – 192.544 (KB)4 where KB stands for Blue Chromaticity, has proven
to be the best-fit model. However, accurate empirical models for turbidity and temperature have not
been identified. These findings highlight the potential use of smartphone applications and empirical
models to enhance the effectiveness of water quality monitoring. |
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