# Evaluation of LoRa Network Performance for Water Quality Monitoring Systems  
[PERSON]  
1 Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; [EMAIL_ADDRESS] (S.N.S.T.); [EMAIL_ADDRESS] (M.M.); [EMAIL_ADDRESS] (T.F.M.M.N.L) 1  
[PERSON]  
1 Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; [EMAIL_ADDRESS] (S.N.S.T.); [EMAIL_ADDRESS] (M.M.); [EMAIL_ADDRESS] (T.F.M.M.N.L) 1  
[PERSON]  
1 Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; [EMAIL_ADDRESS] (S.N.S.T.); [EMAIL_ADDRESS] (M.M.); [EMAIL_ADDRESS] (T.F.M.M.N.L) 1  
[PERSON]  
2 Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia; [EMAIL_ADDRESS] 2  
[PERSON]  
1 Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; [EMAIL_ADDRESS] (S.N.S.T.); [EMAIL_ADDRESS] (M.M.); [EMAIL_ADDRESS] (T.F.M.N.L) 1  
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In this study, packet loss was integrated with the results of K-means from water quality data to predict anomalies in the data. The observation of K-means from the water quality data integrated with packet loss from all water stations is shown in Figures 13-15.
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While the trend of K-means of turbidity trends at station 1 was stable as referred to in Figure 13, TDS had constant fluctuation between 150 and 250 mg/L. However, its packet data operated with a stable trend and a low packet loss rate, which were below 1%. Hence, it can be concluded that the water bodies at station 1 varied in salinity and dissolved content. Water bodies at Station 2 based on Figure 14 showed stable fluctuations in turbidity and TDS throughout the experimental timeline. There was a high packet loss at Station 2 recorded between November and December 2022 because of the affected transmitted packet data caused by the seasonal change in the monsoon season in Malaysia. At station 3, TDS increased from October 2022 and had the highest record at 250 mg/L in early 2023 with high cloudiness at 400 NTU as shown in Figure 15. The recorded packet loss at station 3 stayed within acceptable range; hence, water quality properties observed in the data were not affected by the network performance, though the increment of salinity and turbidity were affected by the increment speed of the river during monsoon season in Malaysia. Observations of packet loss and water quality data allow environmentalists to inspect and maintain the condition of water sensors and network performance. By using packet loss as an additional input feature, the proposed solution can better distinguish between true and false positives, resulting in improved overall performance. The integration analysis resulted in enhancements in the water quality monitoring and network performance analysis.  
Although the trend of packet loss from Station 2 had a high loss compared to the other stations, the packet loss rates were still within acceptable ranges for low packet data. A previous study by [PERSON] et al. concluded that an increase in communication distance increases the packet loss rate [48].  
Thus, monitoring packet loss in this study was important for troubleshooting the network performance and ensuring the efficiency of operational tasks.