{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T23:59:21Z","timestamp":1780444761399,"version":"3.54.1"},"reference-count":71,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T00:00:00Z","timestamp":1670198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>River floods are listed among the natural disasters that can directly influence different aspects of life, ranging from human lives, to economy, infrastructure, agriculture, etc. Organizations are investing heavily in research to find more efficient approaches to prevent them. The Artificial Intelligence of Things (AIoT) is a recent concept that combines the best of both Artificial Intelligence and Internet of Things, and has already demonstrated its capabilities in different fields. In this paper, we introduce an AIoT architecture where river flood sensors, in each region, can transmit their data via the LoRaWAN to their closest local broadcast center. The latter will relay the collected data via 4G\/5G to a centralized cloud server that will analyze the data, predict the status of the rivers countrywide using an efficient Artificial Intelligence approach, and thus, help prevent eventual floods. This approach has proven its efficiency at every level. On the one hand, the LoRaWAN-based communication between sensor nodes and broadcast centers has provided a lower energy consumption and a wider range. On the other hand, the Artificial Intelligence-based data analysis has provided better river flood predictions.<\/jats:p>","DOI":"10.3390\/s22239485","type":"journal-article","created":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T08:10:57Z","timestamp":1670227857000},"page":"9485","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["An Integrated Artificial Intelligence of Things Environment for River Flood Prevention"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4891-3760","authenticated-orcid":false,"given":"Zakaria","family":"Boulouard","sequence":"first","affiliation":[{"name":"LIM, Hassan II University of Casablanca, Casablanca 20000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3993-8405","authenticated-orcid":false,"given":"Mariyam","family":"Ouaissa","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Moulay Ismail University, Meknes 50050, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0088-3742","authenticated-orcid":false,"given":"Mariya","family":"Ouaissa","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Moulay Ismail University, Meknes 50050, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farhan","family":"Siddiqui","sequence":"additional","affiliation":[{"name":"Data Science Department, NED University of Engineering and Technology, Karachi 75270, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4611-6493","authenticated-orcid":false,"given":"Mutiq","family":"Almutiq","sequence":"additional","affiliation":[{"name":"Department of Management Information Systems and Production Management, College of Business and Economics, Qassim University, P.O. Box 6640, Buraidah 51452, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8873-9755","authenticated-orcid":false,"given":"Moez","family":"Krichen","sequence":"additional","affiliation":[{"name":"FCSIT, Al-Baha University, Al-Baha 65528, Saudi Arabia"},{"name":"ReDCAD Laboratory, University of Sfax, Sfax 3038, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,5]]},"reference":[{"key":"ref_1","unstructured":"European Commission (2022, July 20). River Floods. EU Science Hub. Available online: https:\/\/bit.ly\/EURiverF."},{"key":"ref_2","unstructured":"BBC Bitesize (2022, July 20). Rivers and Flooding\u2014KS3 Geography Revision. BBC. Available online: https:\/\/bbc.in\/3coAZxn."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, Y., Xu, Y., Zhao, Y., and Long, Y. (2022). Using SWAT Model to Assess the Impacts of Land Use and Climate Changes on Flood in the Upper Weihe River, China. Water, 14.","DOI":"10.3390\/w14132098"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, L.-C., Hoang, D.V., and Liou, Y.-A. (2022). Quantifying the Impacts of the 2020 Flood on Crop Production and Food Security in the Middle Reaches of the Yangtze River, China. Remote Sens., 14.","DOI":"10.3390\/rs14133140"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s41748-021-00283-w","article-title":"Hydrodynamic Modelling of Floods and Estimating Socio-economic Impacts of Floods in Ugandan River Malaba Sub-catchment","volume":"6","author":"Mubialiwo","year":"2022","journal-title":"Earth Syst. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e12805","DOI":"10.1111\/jfr3.12805","article-title":"Flood impact on income inequality in the Itapocu River basin, Brazil","volume":"15","author":"Ohara","year":"2022","journal-title":"J. Flood Risk Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"105367","DOI":"10.1016\/j.envsoft.2022.105367","article-title":"Towards reducing flood risk disasters in a tropical urban basin by the development of flood alert web application","volume":"151","author":"Mattos","year":"2022","journal-title":"Environ. Model. Softw."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103097","DOI":"10.1016\/j.ijdrr.2022.103097","article-title":"Flood risk mapping and urban infrastructural susceptibility assessment using a GIS and analytic hierarchical raster fusion approach in the Ona River Basin, Nigeria","volume":"77","author":"Nkeki","year":"2022","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1016\/j.matpr.2021.11.561","article-title":"Evaluating the application of metaheuristic approaches for flood simulation using GIS: A case study of Baitarani river Basin, India","volume":"61","author":"Samantaray","year":"2022","journal-title":"Mater. Today Proc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"100235","DOI":"10.1016\/j.pdisas.2022.100235","article-title":"Applying Google earth engine for flood mapping and monitoring in the downstream provinces of Mekong river","volume":"14","author":"Nghia","year":"2022","journal-title":"Prog. Disaster Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Senouci, M.R., and Mellouk, A. (2016). Deploying Wireless Sensor Networks, Elsevier.","DOI":"10.1016\/B978-1-78548-099-7.50001-5"},{"key":"ref_12","first-page":"567","article-title":"Wireless sensor network (WSN) based early flood warning system","volume":"12","author":"Siddique","year":"2020","journal-title":"Int. J. Inf. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"118","DOI":"10.5775\/fg.2018.097.d","article-title":"Application of Wireless Sensor Networks in Flood Detection and River Pollution Monitoring","volume":"XVII","year":"2018","journal-title":"Forum Geogr."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wahyono, I.D., Jong, G.J., Asfani, K., Afandi, A.N., and Fadlika, I. (2019, January 3\u20134). New Algorithm to Determine Prediction Accuracy on Wireless Sensor Networks. Proceedings of the 2019 International Conference on Electrical, Electronics and Information Engineering (ICEEIE), Denpasar, Indonesia.","DOI":"10.1109\/ICEEIE47180.2019.8981459"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1007\/978-981-16-3767-4_46","article-title":"Validation and Implementation of a Smart Flood Surveillance System Based on Wireless Sensor Network","volume":"Volume 781","author":"Saharia","year":"2022","journal-title":"The Micro and Nanoelectronics Devices, Circuits and Systems Conference 2021"},{"key":"ref_16","unstructured":"(2022, July 21). Oracle What Is the Internet of Things (IoT)?. Available online: https:\/\/bit.ly\/orcliot."},{"key":"ref_17","first-page":"166","article-title":"Riverbank Monitoring using Image Processing for Early Flood Warning System via IoT","volume":"14","author":"Hamzah","year":"2022","journal-title":"Int. J. Integr. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"012011","DOI":"10.1088\/1755-1315\/955\/1\/012011","article-title":"Analysis of Ciliwung river flood debit and city flood anticipation using floods early detection system (FEDS)","volume":"955","author":"Biantoro","year":"2022","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_19","unstructured":"(2022, July 21). Blynk IoT Platform: For Businesses and Developers. Available online: https:\/\/blynk.io\/."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1504\/IJSNET.2022.122574","article-title":"An IoT-based scalable river level monitoring platform","volume":"38","author":"Acosta","year":"2022","journal-title":"Int. J. Sens. Netw."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ganesh, R.S., Sasipriya, S., Gowtham Balaji, M., Ashok Karthi, G., and Gokul Dharan, S. (2022, January 9\u201311). An IoT-based Dam Water Level Monitoring and Alerting System. Proceedings of the 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India.","DOI":"10.1109\/ICAAIC53929.2022.9792675"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chaudhari, B.S., Zennaro, M., and Borkar, S. (2020). LPWAN technologies: Emerging application characteristics, requirements, and design considerations. Future Internet, 12.","DOI":"10.3390\/fi12030046"},{"key":"ref_23","unstructured":"Shea, S. (2022, July 24). What is LPWAN (Low-Power Wide Area Network)?\u2014Definition from WhatIs.com. Available online: https:\/\/bit.ly\/LPWANss."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"57596","DOI":"10.1109\/ACCESS.2022.3178437","article-title":"A Novel LoRa LPWAN-Based Communication Architecture for Search & Rescue Missions","volume":"10","author":"Manuel","year":"2022","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ibarreche, J., Aquino, R., Edwards, R.M., Rangel, V., P\u00e9rez, I., Mart\u00ednez, M., Castellanos, E., \u00c1lvarez, E., Jimenez, S., and Renter\u00eda, R. (2020). Flash Flood Early Warning System in Colima, Mexico. Sensors, 20.","DOI":"10.3390\/s20185231"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s13638-022-02096-5","article-title":"Early warning of impending flash flood based on AIoT","volume":"2022","author":"Sung","year":"2022","journal-title":"EURASIP J. Wirel. Commun. Netw."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ragnoli, M., Barile, G., Leoni, A., Ferri, G., and Stornelli, V. (2020). An Autonomous Low-Power LoRa-Based Flood-Monitoring System. J. Low Power Electron. Appl., 10.","DOI":"10.3390\/jlpea10020015"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"569","DOI":"10.5194\/isprs-archives-XLII-3-W4-569-2018","article-title":"A study on real-time flood monitoring system based on sensors using flood damage insurance map","volume":"XLII-3\/W4","author":"Yeon","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e74","DOI":"10.1002\/itl2.74","article-title":"WaterS: A Sigfox-compliant prototype for water monitoring","volume":"2","author":"Vitanio","year":"2019","journal-title":"Internet Technol. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1057\/s41288-020-00201-7","article-title":"The impact of artificial intelligence along the insurance value chain and on the insurability of risks","volume":"47","author":"Eling","year":"2022","journal-title":"Geneva Pap. Risk Insur.-Issues Pract."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Shi, Y., Yang, K., Yang, Z., and Zhou, Y. (2021). Mobile Edge Artificial Intelligence: Opportunities and Challenges, Elsevier.","DOI":"10.1016\/B978-0-12-823817-2.00013-9"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Qaisar, S.M., Mihoub, A., Krichen, M., and Nisar, H. (2021). Multirate Processing with Selective Subbands and Machine Learning for Efficient Arrhythmia Classification. Sensors, 21.","DOI":"10.3390\/s21041511"},{"key":"ref_33","first-page":"6961343","article-title":"A Deep Learning-Based Framework for Human Activity Recognition in Smart Homes","volume":"2021","author":"Mihoub","year":"2021","journal-title":"Mob. Inf. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zidi, S., Mihoub, A., Mian Qaisar, S., Krichen, M., and Abu Al-Haija, Q. (2022). Theft detection dataset for benchmarking and machine learning based classification in a smart grid environment. J. King Saud Univ. \u2014Comput. Inf. Sci., in press.","DOI":"10.1016\/j.jksuci.2022.05.007"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Mihoub, A., Snoun, H., Krichen, M., Salah, R.B.H., and Kahia, M. (2020, January 3\u20135). Predicting COVID-19 Spread Level using Socio- Economic Indicators and Machine Learning Techniques. Proceedings of the 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH), Riyadh, Saudi Arabia.","DOI":"10.1109\/SMART-TECH49988.2020.00041"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"107716","DOI":"10.1016\/j.compeleceng.2022.107716","article-title":"Denial of service attack detection and mitigation for internet of things using looking-back-enabled machine learning techniques","volume":"98","author":"Mihoub","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"109422","DOI":"10.1016\/j.ymssp.2022.109422","article-title":"Data-driven simultaneous identification of the 6DOF dynamic model and wave load for a ship in waves","volume":"184","author":"Ren","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1960","DOI":"10.3934\/mbe.2023090","article-title":"Optimal search mapping among sensors in heterogeneous smart homes","volume":"20","author":"Yu","year":"2022","journal-title":"Math. Biosci. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2781","DOI":"10.1109\/JSTARS.2021.3059451","article-title":"A Hyperspectral Image Classification Method Using Multifeature Vectors and Optimized KELM","volume":"14","author":"Chen","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"114629","DOI":"10.1016\/j.eswa.2021.114629","article-title":"An improved quantum-inspired cooperative co-evolution algorithm with muli-strategy and its application","volume":"171","author":"Cai","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"103198","DOI":"10.1016\/j.pce.2022.103198","article-title":"A comparison of performance measures of three machine learning algorithms for flood susceptibility mapping of river Silabati (tropical river, India)","volume":"127","author":"Hasanuzzaman","year":"2022","journal-title":"Phys. Chem. Earth Parts A\/B\/C"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"151885","DOI":"10.1016\/j.scitotenv.2021.151885","article-title":"Deep learning models to predict flood events in fast-flowing watersheds","volume":"813","author":"Luppichini","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Atashi, V., Gorji, H.T., Shahabi, S.M., Kardan, R., and Lim, Y.H. (2022). Water Level Forecasting Using Deep Learning Time-Series Analysis: A Case Study of Red River of the North. Water, 14.","DOI":"10.3390\/w14121971"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Tanim, A.H., McRae, C.B., Tavakol-Davani, H., and Goharian, E. (2022). Flood Detection in Urban Areas Using Satellite Imagery and Machine Learning. Water, 14.","DOI":"10.3390\/w14071140"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Boulouard, Z., Ouaissa, M., Ouaissa, M., and El Himer, S. (2022). AI and IoT for Sustainable Development in Emerging Countries, Springer International Publishing. Lecture Notes on Data Engineering and Communications Technologies.","DOI":"10.1007\/978-3-030-90618-4"},{"key":"ref_46","first-page":"715","article-title":"Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network","volume":"70","author":"Goudarzi","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Fernandes Junior, F.E., Nonato, L.G., Ranieri, C.M., and Ueyama, J. (2021). Memory-Based Pruning of Deep Neural Networks for IoT Devices Applied to Flood Detection. Sensors, 21.","DOI":"10.3390\/s21227506"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1007\/s11069-022-05347-2","article-title":"Flood risk mapping for the lower Narmada basin in India: A machine learning and IoT-based framework","volume":"113","author":"Mangukiya","year":"2022","journal-title":"Nat. Hazards"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Anuradha, B., Abinaya, C., Bharathi, M., Janani, A., and Khan, A. (2022, January 25\u201326). IoT Based Natural Disaster Monitoring and Prediction Analysis for Hills Area Using LSTM Network. Proceedings of the 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India.","DOI":"10.1109\/ICACCS54159.2022.9785121"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Dudak, J., Kebisek, M., Gaspar, G., and Fabo, P. (2020, January 2\u20134). Implementation of machine learning algorithm in embedded devices. Proceedings of the 2020 19th International Conference on Mechatronics\u2014Mechatronika (ME), Prague, Czech Republic.","DOI":"10.1109\/ME49197.2020.9286705"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Gokul, H., Suresh, P., Hari Vignesh, B., Pravin Kumaar, R., and Vijayaraghavan, V. (2020, January 24). Gait Recovery System for Parkinson\u2019s Disease using Machine Learning on Embedded Platforms. Proceedings of the 2020 IEEE International Systems Conference (SysCon), Montreal, QC, Canada.","DOI":"10.1109\/SysCon47679.2020.9275930"},{"key":"ref_52","unstructured":"Gupta, C., Suggala, A.S., Goyal, A., Simhadri, H.V., Paranjape, B., Kumar, A., Goya, S., Udupa, R., Varma, M., and Jain, P. (2017, January 6\u201311). ProtoNN: Compressed and accurate kNN for resource-scarce devices. Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, Australia."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Peruzzi, G., Galli, A., and Pozzebon, A. (2022, January 18\u201320). A Novel Methodology to Remotely and Early Diagnose Sleep Bruxism by Leveraging on Audio Signals and Embedded Machine Learning. Proceedings of the 2022 IEEE International Symposium on Measurements & Networking (M&N), Padua, Italy.","DOI":"10.1109\/MN55117.2022.9887782"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Ali, S.A., Ashfaq, F., Nisar, E., Azmat, U., and Zeb, J. (2020, January 14\u201318). A Prototype for Flood Warning and Management System using Mobile Networks. Proceedings of the 2020 17th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, Pakistan.","DOI":"10.1109\/IBCAST47879.2020.9044531"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Covenas, F.E.M., Palomares, R., Milla, M.A., Verastegui, J., and Cornejo, J. (2021, January 24\u201326). Design and Development of a Low-Cost Wireless Network Using IoT Technologies for a Mudslides Monitoring System. Proceedings of the 2021 IEEE URUCON, Montevideo, Uruguay.","DOI":"10.1109\/URUCON53396.2021.9647379"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Yang, S.-N., and Chang, L.-C. (2020). Regional Inundation Forecasting Using Machine Learning Techniques with the Internet of Things. Water, 12.","DOI":"10.3390\/w12061578"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"70375","DOI":"10.1109\/ACCESS.2020.2986090","article-title":"IoT-Enabled Flood Severity Prediction via Ensemble Machine Learning Models","volume":"8","author":"Khalaf","year":"2020","journal-title":"IEEE Access"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Adefemi Alimi, K.O., Ouahada, K., Abu-Mahfouz, A.M., and Rimer, S. (2020). A Survey on the Security of Low Power Wide Area Networks: Threats, Challenges, and Potential Solutions. Sensors, 20.","DOI":"10.3390\/s20205800"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1016\/j.procs.2019.08.090","article-title":"A comparative survey study on LPWA IoT technologies: Design, considerations, challenges and solutions","volume":"155","author":"Muteba","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_60","unstructured":"(2022, November 22). LoRa Alliance What is LoRaWAN\u00ae Specification?. Available online: https:\/\/bit.ly\/lorawan1."},{"key":"ref_61","unstructured":"(2022, November 23). Amazon Web Services What is LoRaWAN?\u2014AWS IoT Core. Available online: https:\/\/bit.ly\/lorawanaws."},{"key":"ref_62","first-page":"43","article-title":"Towards Low-Cost IoT and LPWAN-Based Flood Forecast and Monitoring System","volume":"17","author":"Tadrist","year":"2022","journal-title":"J. Ubiquitous Syst. Pervasive Netw."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"e07353","DOI":"10.1016\/j.heliyon.2021.e07353","article-title":"Communication protocols evaluation for a wireless rainfall monitoring network in an urban area","volume":"7","year":"2021","journal-title":"Heliyon"},{"key":"ref_64","unstructured":"(2022, November 23). Advantech WISE-4610. Available online: https:\/\/bit.ly\/wise4610p."},{"key":"ref_65","unstructured":"(2022, November 23). Advantech WISE-6610. Available online: https:\/\/bit.ly\/wise6610w."},{"key":"ref_66","unstructured":"(2022, November 23). Advantech Co-Creating the Future of the IoT World. Available online: https:\/\/www.advantech.com\/en."},{"key":"ref_67","unstructured":"(2022, July 27). UK-DEFRA Catchment Based Approach: Improving the Quality of Our Water Environment A Policy Framework to Encourage the Wider Adoption of an Integrated Catchment Based Approach to Improving the Quality of Our Water Environment. Available online: https:\/\/bit.ly\/catchmentpaper."},{"key":"ref_68","unstructured":"UK-DEFRA (2022, July 28). How to Use Catchment Data Explorer. Available online: https:\/\/bit.ly\/catchdataexp."},{"key":"ref_69","unstructured":"Kobia (2022, July 29). Multi-Class Classification Metrics. Available online: https:\/\/bit.ly\/multicm."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1002\/sam.11583","article-title":"Optimal ratio for data splitting","volume":"15","author":"Joseph","year":"2022","journal-title":"Stat. Anal. Data Min. ASA Data Sci. J."},{"key":"ref_71","unstructured":"Hashmi, F. (2022, November 22). How to Tune Hyperparameters Using Random Search CV in Python\u2014Thinking Neuron. Available online: https:\/\/bit.ly\/RScv2."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9485\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:33:58Z","timestamp":1760146438000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9485"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,5]]},"references-count":71,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22239485"],"URL":"https:\/\/doi.org\/10.3390\/s22239485","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,5]]}}}