[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122794-en":3,"doc-seo-122794-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122794,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Water level identification with laser sensors, inertial units, and machine learning","Flood risk management relies on accurate water level identification in urban streams such as rivers and creeks. While ultrasonic sensors are widely used as contactless water level monitoring technology, research on LiDAR benefits in flood contexts remains limited. This work builds a synchronized laboratory dataset combining a LiDAR, an ultrasonic reference sensor, and an IMU, controlling incidence angle, distance, and water turbidity. Machine-learning fusion models reduce error rates versus single-sensor predictions, with tree-based ensembles improving mean absolute error, RMSE, and R² across conditions.","Graphical Abstract  \nWater level identification with laser sensors, inertial units, and machine learning  \nCaetano M. Ranieri, Angelo V. K. Foletto, Rodrigo D. Garcia, Saulo N. Matos, Maria M. G. Medina, Leandro S. Marcolino, J´o Ueyama  \nSetup for the data collection procedure Water level identification  \n\n| 1 | Data collection using\u003Cbr>different settings |  |\n| --- | --- | --- |\n|  |  |  |\n|  = {50, 100, 150, 200, 250, 300}\u003Cbr> = {0, 2.5, 5, 7 .5, 10}\u003Cbr> = {low, medium, high} |  |  |\n\n| 3 | Machine Learning Models |\n| --- | --- |\n|  |  |\n\nHighlights  \nWater level identification with laser sensors, inertial units, and machine learning  \nCaetano M. Ranieri, Angelo V. K. Foletto, Rodrigo D. Garcia, Saulo N. Matos, Maria M. G. Medina, Leandro S. Marcolino, J´o Ueyama  \n• This work presents an experimental scenario to evaluate water level measurement using an experimental device integrating a Light Detection and Ranging (LiDAR) sensor, an Inertial Measurement Unit (IMU) and a fusion method based on machine learning.  \n• We collected data in a controlled environment considering a discrete set of water turbidity levels, sensors’ tilt angles and distances between the sensor and the water surface.  \n• LiDAR predictions were compared to those from an ultrasonic sensor employed as a reference for performance, with and without the machine learning-based approach for error minimisation.  \n• This methodology has significantly improved results in challenging conditions, including situations with lower water turbidity or increased tilt angle, so that the enhanced results for the LiDAR sensor were close to those of the ultrasonic sensor.  \n• Nonetheless, the LiDAR sensor is more robust against flood-related weather conditions such as heavy rainfall, fog, dust, and variations in temperature and humidity, which highlights its relevance for flood risk management.  \nWater level identification with laser sensors, inertial units, and machine learning  \nCaetano M. Ranieria , Angelo V. K. Folettoa , Rodrigo D. Garciaa , Saulo N. Matosa , Maria M. G. Medinab , Leandro S. Marcolinoc , J´o Ueyamaa  \na Institute of Mathematical and Computer Sciences, University of S˜ao Paulo, Av. Trab.  \nS˜ao Carlense, 400, S˜ao Carlos, 13566-590, SP, Brazil bS˜ao Carlos School of Engineering, University of S˜ao Paulo, Av. Trab. S˜ao Carlense, 400, S˜ao Carlos, 13566-590, SP, Brazil  \nc School of Computing and Communication, Lancaster  \nUniversity, Bailrigg, Lancaster, LA1 4YW, United Kingdom  \nAbstract  \nFlood risk management usually hinges on accurate water level identification in urban streams such as rivers or creeks. Although research has emphasised the applicability of ultrasonic sensors as a contactless technology for sensorbased water level monitoring, Light Detection and Ranging (LiDAR) sensors are less sensitive to weather conditions that typically happen during flood events, such as dust, fog and rainfall. However, there has been little research on the applicability of LiDAR sensors in this field. No previous literature has analysed the impact of complicating variables on the quality of predictions or evaluated the possible benefits of using a combined approach with Inertial Measurement Units (IMU) and machine learning to produce superior predictions. In this work, we collected a dataset in a laboratory condition synchronising data from a LiDAR, an ultrasonic sensor and an IMU in an experimental device. We controlled the incidence angle, the distance, and the water turbidity to analyse their effect on the predictions. Traditional machine-learning techniques were evaluated as models to combine data from distance and inertial sensors, reducing the error rates compared to individual sensors’ predictions. Results indicated a sharp drop in the mean absolute error, root mean squared error and coefficient of determination for all water turbidity and incidence angles considered, especially when tree-based ensembles were used. The ultrasonic sensor led to improved re","cbCaiu1yPCOtEIUr","https://ap.wps.com/l/cbCaiu1yPCOtEIUr","pdf",3011819,1,52,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What data sources does the study combine for water level identification?\",\"answer\":\"The dataset synchronizes readings from a LiDAR sensor, an ultrasonic sensor used as a reference, and an Inertial Measurement Unit (IMU).\"},{\"question\":\"Which variables are controlled to evaluate prediction quality?\",\"answer\":\"Incidence angle, distance, and water turbidity are controlled to analyze their impact on prediction results.\"},{\"question\":\"How do machine-learning fusion models affect performance compared with individual sensors?\",\"answer\":\"Traditional machine-learning fusion models combining distance and inertial sensors reduce error rates compared with predictions from each individual sensor, with tree-based ensembles showing especially strong improvements.\"}]","Water level identification 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data sources does the study combine for water level identification?","Question",{"text":76,"@type":77},"The dataset synchronizes readings from a LiDAR sensor, an ultrasonic sensor used as a reference, and an Inertial Measurement Unit (IMU).","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which variables are controlled to evaluate prediction quality?",{"text":81,"@type":77},"Incidence angle, distance, and water turbidity are controlled to analyze their impact on prediction results.",{"name":83,"@type":74,"acceptedAnswer":84},"How do machine-learning fusion models affect performance compared with individual sensors?",{"text":85,"@type":77},"Traditional machine-learning fusion models combining distance and inertial sensors reduce error rates compared with predictions from each individual sensor, with tree-based ensembles showing especially strong 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