{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:27:01Z","timestamp":1760232421028,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,5]],"date-time":"2022-11-05T00:00:00Z","timestamp":1667606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council, Taiwan","award":["MOST 111-2625-M-A49-006"],"award-info":[{"award-number":["MOST 111-2625-M-A49-006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Extreme weather events cause stream overflow and lead to urban inundation. In this study, a decentralized flood monitoring system is proposed to provide water level predictions in streams three hours ahead. The customized sensor in the system measures the water levels and implements edge computing to produce future water levels. It is very different from traditional centralized monitoring systems and considered an innovation in the field. In edge computing, traditional physics-based algorithms are not computationally efficient if microprocessors are used in sensors. A correlation analysis was performed to identify key factors that influence the variations in the water level forecasts. For example, the second-order difference in the water level is considered to represent the acceleration or deacceleration of a water level rise. According to different input factors, three artificial neural network (ANN) models were developed. Four streams or canals were selected to test and evaluate the performance of the models. One case was used for model training and testing, and the others were used for model validation. The results demonstrated that the ANN model with the second-order water level difference as an input factor outperformed the other ANN models in terms of RMSE. The customized microprocessor-based sensor with an embedded ANN algorithm can be adopted to improve edge computing capabilities and support emergency response and decision making.<\/jats:p>","DOI":"10.3390\/s22218532","type":"journal-article","created":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T03:02:22Z","timestamp":1667790142000},"page":"8532","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Development of an Artificial Neural Network Algorithm Embedded in an On-Site Sensor for Water Level Forecasting"],"prefix":"10.3390","volume":"22","author":[{"given":"Cheng-Han","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1627-9208","authenticated-orcid":false,"given":"Tsun-Hua","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7749-2494","authenticated-orcid":false,"given":"Obaja Triputera","family":"Wijaya","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan"},{"name":"Department of Civil Engineering, Parahyangan Catholic University, Bandung 40141, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,5]]},"reference":[{"key":"ref_1","unstructured":"Centre for Research on the Epidemiology of Disasters (CRED) (2021). 2021 Disasters in Numbers, CRED."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1080\/02626667.2011.583249","article-title":"Development of a global flood risk index based on natural and socio-economic factors","volume":"56","author":"Okazawa","year":"2011","journal-title":"Hydrol. 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