Kamal, N., Hammad, A., Salem, T., Omar, M. (2019). EARLY WARNING AND WATER QUALITY, LOW-COST IOT BASED MONITORING SYSTEM. JES. Journal of Engineering Sciences, 47(No 6), 795-806. doi: 10.21608/jesaun.2019.115742
Noha Kamal; Abdallah Hammad; Talaat Salem; Mohie Omar. "EARLY WARNING AND WATER QUALITY, LOW-COST IOT BASED MONITORING SYSTEM". JES. Journal of Engineering Sciences, 47, No 6, 2019, 795-806. doi: 10.21608/jesaun.2019.115742
Kamal, N., Hammad, A., Salem, T., Omar, M. (2019). 'EARLY WARNING AND WATER QUALITY, LOW-COST IOT BASED MONITORING SYSTEM', JES. Journal of Engineering Sciences, 47(No 6), pp. 795-806. doi: 10.21608/jesaun.2019.115742
Kamal, N., Hammad, A., Salem, T., Omar, M. EARLY WARNING AND WATER QUALITY, LOW-COST IOT BASED MONITORING SYSTEM. JES. Journal of Engineering Sciences, 2019; 47(No 6): 795-806. doi: 10.21608/jesaun.2019.115742
EARLY WARNING AND WATER QUALITY, LOW-COST IOT BASED MONITORING SYSTEM
1Nile Research Institute (NRI), National Water Research Center (NWRC), Egypt.
2Depart. of Electrical Eng., Faculty of Engineering at Shoubra, Benha University, Egypt.
Abstract
In Egypt, the current water quality monitoring program involves thorough physio-chemical and biological analyses. However, there is still a lack of real-time water quality data that influences the urgent decision making process. The field acquisition of such data has still been costly, lengthy and laborious. Therefore, this paper aims to present a low-cost and labor-saving Early Warning Framework (EWF) for water quality monitoring of the River Nile based on the Internet of Things (IOT). A newly developed Prototype was introduced to monitor the in-situ water quality parameters; pH, turbidity and temperature at a pilot location along the River Nile within Egypt. The same parameters were also monitored using the current state-of-the-art multi-probe EXO.Then, both sets of data measurements were sent to a real-time monitoring control center for comparison and calibration. The comparison results revealed that there is no significant difference between the two measurements according to a statistical analysis done using the Minitab 16 statistical model. The Root Mean Squared Error (RMSE) values showed that the error percentages were accepted for the three monitored parameters (0.19 for pH, 0.056 for temperature, and 0.52 for turbidity). Moreover, the overall cost of the developed Prototype including sensors, raspberry Pi and all other expenses was found to be only 197 $ as compared to 11,130 $ when using EXO. Accordingly, it could be concluded that the developed Prototype can provide a low-cost early warning system for water quality monitoring. Finally, it is strongly recommended to install developed real-time water quality monitoring stations as economic wireless hotspots at a number of strategic sites along the River Nile within Egypt.
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