dc.description.abstract |
Water treatment can be promoted through keen consideration
of raw water quality parameters (Turbidity and pH). This
paper discusses the development of a real-time water quality
monitoring system using wireless sensor networks. At first,
we present performance experiments on LoRa technology
connectivity for wireless sensor networks in a rural set up of
Dedan Kimathi University of Technology in Kenya. The
specific sensors used for the developed system included: The
DFRobot gravity Arduino turbidity sensor and the DFRobot's
Gravity Analog pH Sensor. The sensed data values of these
parameters were relayed to a gateway by a LoRaWAN
transceiver. The gateway then uploaded the received
parameter data values to The Things Network platform which
was interfaced with a Google Cloud Platform, where an
InfluxdB Virtual Machine database stored the received data. A
web-based application (Dash Plotly app) was developed and
interlinked with the database for analysis and visualization of
the received data in real time. The system was deployed at the
Nyeri Water and Sanitation Company treatment plant based at
Nyeri town, Kenya, from 4th November, 2020 to 4th January,
2021. The dataset obtained contained a total of 2,658 records,
each collected after every 30 minutes. Using a subset of 291
records, extensive experiments were performed for the
evaluation and assessment of machine learning anomaly
detection algorithms of the Local Outlier Factor and the
Robust Random Cut Forest for each of the two parameters;
Turbidity and pH. From analysis results, the Local Outlier
Factor algorithm outperformed its counterpart. |
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