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|dc.description.abstract||In water resources management (WRM), the use of an advanced, efficient and low-cost technique for monitoring an urban stream was carried out. Physicochemical parameters (PcP) of the Jungnangcheon stream (Seoul, South Korea) were assessed using the Internet of Things (IoT) platform. Temperature, dissolved oxygen (DO) and pH parameters were monitored for the 3 Summer and Fall at a fixed location. Analysis was performed using multivariate statistical and clustering techniques (CTs) such as K-means clustering, agglomerative hierarchical clustering (AHC) and density-based spatial clustering of applications with noise (DBSCAN). An IoT-based Arduino modular sensor (AMS) network with 99.99% efficient communication, the platform was developed to allow the collection of stream data with user-friendly software and hardware and facilitate data analysis by interested individuals using their smartphones, multivariate statistical techniques. Clustering was used to formulate relationships among physicochemical parameters. K-means clustering was used to identify natural clusters using the silhouette coefficient based on cluster compactness and looseness. AHC grouped all data into 2 clusters as well as temperature, DO and pH into 4, 8 and 4 clusters, respectively. DBSCAN analysis was also performed to evaluate yearly variations in physicochemical parameters. The trial values of eps in cm and M_in P_ts were set to (1.9073-06,3), (2.3842-07,12), and (-50,0.01), respectively. Noise points (NOISE) of temperature in 2016 were border points (ƥ), whereas in 2014 and 2015 they remained core points (ɋ), indicating a trend toward increasing temperature. We found the stream parameters were within the permissible limits set by the Water Quality Standards for River Water, South Korea.||-|
|dc.title||Internet of Things Platform for Smart Water Resources Management using Artificial Neural Networks||-|
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