{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:31:50Z","timestamp":1778347910316,"version":"3.51.4"},"reference-count":34,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T00:00:00Z","timestamp":1636675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001807","name":"S\u00e3o Paulo Research Foundation","doi-asserted-by":"publisher","award":["2020\/05426-0"],"award-info":[{"award-number":["2020\/05426-0"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Automatic flood detection may be an important component for triggering damage control systems and minimizing the risk of social or economic impacts caused by flooding. Riverside images from regular cameras are a widely available resource that can be used for tackling this problem. Nevertheless, state-of-the-art neural networks, the most suitable approach for this type of computer vision task, are usually resource-consuming, which poses a challenge for deploying these models within low-capability Internet of Things (IoT) devices with unstable internet connections. In this work, we propose a deep neural network (DNN) architecture pruning algorithm capable of finding a pruned version of a given DNN within a user-specified memory footprint. Our results demonstrate that our proposed algorithm can find a pruned DNN model with the specified memory footprint with little to no degradation of its segmentation performance. Finally, we show that our algorithm can be used in a memory-constraint wireless sensor network (WSN) employed to detect flooding events of urban rivers, and the resulting pruned models have competitive results compared with the original models.<\/jats:p>","DOI":"10.3390\/s21227506","type":"journal-article","created":{"date-parts":[[2021,11,14]],"date-time":"2021-11-14T20:51:53Z","timestamp":1636923113000},"page":"7506","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Memory-Based Pruning of Deep Neural Networks for IoT Devices Applied to Flood Detection"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2301-8820","authenticated-orcid":false,"given":"Francisco Erivaldo","family":"Fernandes Junior","sequence":"first","affiliation":[{"name":"SIDIA R&D Institute, Manaus 69055-035, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8514-8033","authenticated-orcid":false,"given":"Luis Gustavo","family":"Nonato","sequence":"additional","affiliation":[{"name":"Institute of Mathematical and Computer Sciences, University of S\u00e3o Paulo (USP), S\u00e3o Carlos 13566-590, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5680-9085","authenticated-orcid":false,"given":"Caetano Mazzoni","family":"Ranieri","sequence":"additional","affiliation":[{"name":"Institute of Mathematical and Computer Sciences, University of S\u00e3o Paulo (USP), S\u00e3o Carlos 13566-590, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5591-3750","authenticated-orcid":false,"given":"J\u00f3","family":"Ueyama","sequence":"additional","affiliation":[{"name":"Institute of Mathematical and Computer Sciences, University of S\u00e3o Paulo (USP), S\u00e3o Carlos 13566-590, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e1211","DOI":"10.1002\/wat2.1211","article-title":"Natural flood management","volume":"4","author":"Lane","year":"2017","journal-title":"Wiley Interdiscip. Rev. Water"},{"key":"ref_2","first-page":"102","article-title":"IoT, big data and HPC based smart flood management framework","volume":"20","author":"Sood","year":"2018","journal-title":"Sustain. Comput. Inform. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1080\/02626667.2019.1617868","article-title":"Evaluation of a global ensemble flood prediction system in Peru","volume":"64","author":"Bischiniotis","year":"2019","journal-title":"Hydrol. Sci. J."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gude, V., Corns, S., and Long, S. (2020). Flood Prediction and Uncertainty Estimation Using Deep Learning. Water, 12.","DOI":"10.3390\/w12030884"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e12582","DOI":"10.1111\/jfr3.12582","article-title":"Can we still predict the future from the past? Implementing non-stationary flood frequency analysis in the UK","volume":"13","author":"Faulkner","year":"2020","journal-title":"J. Flood Risk Manag."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Raj, J.R., Charless, I., Latheef, M.A., and Srinivasulu, S. (2021, January 28\u201330). Identifying the Flooded Area Using Deep Learning Model. Proceedings of the 2021 2nd International Conference on Intelligent Engineering and Management, ICIEM 2021, London, UK.","DOI":"10.1109\/ICIEM51511.2021.9445356"},{"key":"ref_7","unstructured":"Kamilaris, A., and Prenafeta-Bold\u00fa, F.X. (2017, January 13\u201315). Disaster Monitoring using Unmanned Aerial Vehicles and Deep Learning. Proceedings of the Disaster Management for Resilience and Public Safety Workshop, Proceedings of EnviroInfo 2017, Luxembourg."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4435","DOI":"10.5194\/hess-25-4435-2021","article-title":"Deep learning for automated river-level monitoring through river-camera images: An approach based on water segmentation and transfer learning","volume":"25","author":"Vandaele","year":"2021","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s13173-011-0029-3","article-title":"A middleware platform to support river monitoring using wireless sensor networks","volume":"17","author":"Hughes","year":"2011","journal-title":"J. Braz. Comput. Soc."},{"key":"ref_10","unstructured":"Ortigossa, E.S., Dias, F., Ueyama, J., and Nonato, L.G. (2015, January 26\u201329). Using digital image processing to estimate the depth of urban streams. Proceedings of the Workshop of Undergraduate Works in Conjunction with Conference on Graphics, Patterns and Images (SIBGRAPI), Bahia, Brazil."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1007\/978-3-319-24834-9_56","article-title":"A Distributed Approach to Flood Prediction Using a WSN and ML: A Comparative Study of ML Techniques in a WSN Deployed in Brazil","volume":"Volume 9375","author":"Jackowski","year":"2015","journal-title":"Intelligent Data Engineering and Automated Learning\u2014IDEAL 2015"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.compenvurbsys.2017.05.001","article-title":"Enhancing reliability in Wireless Sensor Networks for adaptive river monitoring systems: Reflections on their long-term deployment in Brazil","volume":"65","author":"Ueyama","year":"2017","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Furquim, G., Filho, G., Jalali, R., Pessin, G., Pazzi, R., and Ueyama, J. (2018). How to Improve Fault Tolerance in Disaster Predictions: A Case Study about Flash Floods Using IoT, ML and Real Data. Sensors, 18.","DOI":"10.3390\/s18030907"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_15","unstructured":"Lakshmanan, V., Robinson, S., and Munn, M. (2020). Machine Learning Design Patterns, O\u2019Reilly Media."},{"key":"ref_16","unstructured":"Li, H., Kadav, A., Durdanovic, I., Samet, H., and Graf, H.P. (2017, January 24\u201326). Pruning Filters for Efficient ConvNets. Proceedings of the International Conference on Learning Representations, Toulon, France."},{"key":"ref_17","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.neucom.2019.11.118","article-title":"A Brief Survey on Semantic Segmentation with Deep Learning","volume":"406","author":"Hao","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_19","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning; Adaptive Computation and Machine Learning, The MIT Press."},{"key":"ref_20","unstructured":"Dumoulin, V., and Visin, F. (2018). A guide to convolution arithmetic for deep learning. arXiv."},{"key":"ref_21","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"Volume 9351","author":"Navab","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., and Han, B. (2015, January 7\u201313). Learning Deconvolution Network for Semantic Segmentation. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","article-title":"Microsoft COCO: Common Objects in Context","volume":"Volume 8693","author":"Fleet","year":"2014","journal-title":"Computer Vision\u2014ECCV 2014"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_25","unstructured":"Molchanov, P., Tyree, S., Karras, T., Aila, T., and Kautz, J. (2016). Pruning Convolutional Neural Networks for Resource Efficient Inference. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Luo, J.H., Wu, J., and Lin, W. (2017, January 22\u201329). ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.541"},{"key":"ref_27","unstructured":"Luo, J.H., and Wu, J. (2017). An Entropy-based Pruning Method for CNN Compression. arXiv."},{"key":"ref_28","first-page":"1","article-title":"Automatic Searching and Pruning of Deep Neural Networks for Medical Imaging Diagnostic","volume":"Early Access","author":"Fernandes","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.ins.2020.11.009","article-title":"Pruning Deep Convolutional Neural Networks Architectures with Evolution Strategy","volume":"552","author":"Fernandes","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s00138-018-01001-9","article-title":"Studying the plasticity in deep convolutional neural networks using random pruning","volume":"30","author":"Mittal","year":"2019","journal-title":"Mach. Vis. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 16\u201320). Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_32","first-page":"3320","article-title":"How transferable are features in deep neural networks?","volume":"Volume 27","author":"Ghahramani","year":"2014","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/978-3-540-76386-4_17","article-title":"Object Detection Combining Recognition and Segmentation","volume":"Volume 4843","author":"Yagi","year":"2007","journal-title":"Computer Vision\u2014ACCV 2007"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ding, X., Ding, G., Han, J., and Tang, S. (2018, January 2\u20137). Auto-Balanced Filter Pruning for Efficient Convolutional Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.12262"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/22\/7506\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:28:58Z","timestamp":1760167738000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/22\/7506"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,12]]},"references-count":34,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21227506"],"URL":"https:\/\/doi.org\/10.3390\/s21227506","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,12]]}}}