IoT-Enabled Flood Severity Prediction via Ensemble Machine Learning Models

River flooding is a natural phenomenon that can have a devastating effect on human life and economic losses. There have been various approaches in studying river flooding; however, insufficient understanding and limited knowledge about flooding conditions hinder the development of prevention and control measures for this natural phenomenon. This paper entails a new approach for the prediction of water level in association with flood severity using the ensemble model. Our approach leverages the latest developments in the Internet of Things (IoT) and machine learning for the automated analysis of flood data that might be useful to prevent natural disasters. Research outcomes indicate that ensemble learning provides a more reliable tool to predict flood severity levels. The experimental results indicate that the ensemble learning using the Long-Short Term memory model and random forest outperformed individual models with a sensitivity, specificity and accuracy of 71.4%, 85.9%, 81.13%, respectively.

[1]  Okan Fistikoglu,et al.  Statistical Downscaling of Monthly Precipitation Using NCEP/NCAR Reanalysis Data for Tahtali River Basin in Turkey , 2011 .

[2]  I. Sumaiya Thaseen,et al.  Integrated Intrusion Detection Model Using Chi-Square Feature Selection and Ensemble of Classifiers , 2018, Arabian Journal for Science and Engineering.

[3]  Quan Z. Sheng,et al.  Storytelling Integration of the Internet of Things into Business Processes , 2018, BPM.

[4]  Wasiq Khan,et al.  An Intelligent System for Spoken Term Detection That Uses Belief Combination , 2017, IEEE Intelligent Systems.

[5]  Andrew Blake,et al.  Random Forest Classification for Automatic Delineation of Myocardium in Real-Time 3D Echocardiography , 2009, FIMH.

[6]  Xiaochun Cheng,et al.  Vulnerabilities and Limitations of MQTT Protocol Used between IoT Devices , 2019, Applied Sciences.

[7]  Cesare Stefanelli,et al.  Exploiting smart city IoT for disaster recovery operations , 2018, 2018 IEEE 4th World Forum on Internet of Things (WF-IoT).

[8]  S. Appavu alias Balamurugan,et al.  Prediction of warning level in aircraft accidents using data mining techniques , 2014, The Aeronautical Journal (1968).

[9]  John Scott Bridle,et al.  Probabilistic Interpretation of Feedforward Classification Network Outputs, with Relationships to Statistical Pattern Recognition , 1989, NATO Neurocomputing.

[10]  Anders Krogh,et al.  A Simple Weight Decay Can Improve Generalization , 1991, NIPS.

[11]  Thar Baker,et al.  Cloud-Based Multi-Agent Cooperation for IoT Devices Using Workflow-Nets , 2019, Journal of Grid Computing.

[12]  N. Altman.  An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[13]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[14]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[15]  Tin Kam Ho,et al.  A Data Complexity Analysis of Comparative Advantages of Decision Forest Constructors , 2002, Pattern Analysis & Applications.

[16]  Thar Baker,et al.  A Profitable and Energy-Efficient Cooperative Fog Solution for IoT Services , 2020, IEEE Transactions on Industrial Informatics.

[17]  Mark A. Musen,et al.  Medical applications of artificial neural networks : connectionist models of survival , 1996 .

[18]  C. Lee Giles,et al.  Overfitting and neural networks: conjugate gradient and backpropagation , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[19]  T.,et al.  Training Feedforward Networks with the Marquardt Algorithm , 2004 .

[20]  Ali G. Hessami.  Perspectives on Risk, Assessment and Management Paradigms , 2019 .

[21]  Dingguo Zhang,et al.  A discriminant bispectrum feature for surface electromyogram signal classification. , 2010, Medical engineering & physics.

[22]  Richard Werner Nelem Pazzi,et al.  How to Improve Fault Tolerance in Disaster Predictions: A Case Study about Flash Floods Using IoT, ML and Real Data , 2018, Sensors.

[23]  Tasos Dagiuklas,et al.  RESCUE: A Resilient Cloud Based IoT System for Emergency and Disaster Recovery , 2018, 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS).

[24]  Yogesh Kumar Dwivedi,et al.  Impact of internet of things (IoT) in disaster management: a task-technology fit perspective , 2019, Ann. Oper. Res..

[25]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Mohsen Guizani,et al.  Deep Learning for IoT Big Data and Streaming Analytics: A Survey , 2017, IEEE Communications Surveys & Tutorials.

[27]  Robert A. Jacobs,et al.  Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.

[28]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[29]  Mohsen Guizani,et al.  Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications , 2015, IEEE Communications Surveys & Tutorials.

[30]  Thar Baker,et al.  Improving fog computing performance via Fog-2-Fog collaboration , 2019, Future Gener. Comput. Syst..

[31]  Saad Y. Sait,et al.  Flooding Level Classification by Gait Analysis of Smartphone Sensor Data , 2019, IEEE Access.

[32]  Wasiq Khan,et al.  Novel hybrid object-based non-parametric clustering approach for grouping similar objects in specific visual domains , 2018, Appl. Soft Comput..

[33]  Jürgen Schmidhuber,et al.  Recurrent nets that time and count , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[34]  Zhonghao Sun,et al.  Finding Sands in the Eyes: Vulnerabilities Discovery in IoT With EUFuzzer on Human Machine Interface , 2019, IEEE Access.

[35]  Sotiris B. Kotsiantis,et al.  Data preprocessing in predictive data mining , 2019, The Knowledge Engineering Review.

[36]  Alexander Y. Sun,et al.  How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions , 2019, Environmental Research Letters.

[37]  Jalel Ben-Othman,et al.  QoS enhancement with deep learning-based interference prediction in mobile IoT , 2019, Comput. Commun..

[38]  Dunja Mladenic,et al.  Using machine learning on sensor data , 2010, Proceedings of the ITI 2010, 32nd International Conference on Information Technology Interfaces.

[39]  Korin Richmond,et al.  Estimating articulatory parameters from the acoustic speech signal , 2002 .

[40]  Ozgur Kisi,et al.  Streamflow Forecasting Using Different Artificial Neural Network Algorithms , 2007 .

[41]  Eleonora Borgia,et al.  The Internet of Things vision: Key features, applications and open issues , 2014, Comput. Commun..

[42]  K.R Kavitha,et al.  PCA-based gene selection for cancer classification , 2018, 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC).

[43]  Riccardo Beltramo,et al.  The Internet of Things for Natural Risk Management (Inte.Ri.M.) , 2018, Perspectives on Risk, Assessment and Management Paradigms.

[44]  Hongmei He,et al.  Information gain directed genetic algorithm wrapper feature selection for credit rating , 2018, Appl. Soft Comput..

[45]  Thar Baker,et al.  An Edge Computing Based Smart Healthcare Framework for Resource Management , 2018, Sensors.

[46]  Keeley A. Crockett,et al.  Intelligent Deception Detection through Machine Based Interviewing , 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

[47]  Abdullah Gani,et al.  A Study on Detection and Monitoring of Water Quality and Flow , 2018, 2018 12th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS).

[48]  Dhiya Al-Jumeily,et al.  Recurrent Neural Network Architectures for Analysing Biomedical Data Sets , 2017, 2017 10th International Conference on Developments in eSystems Engineering (DeSE).

[49]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[50]  Beat Fasel,et al.  Robust face analysis using convolutional neural networks , 2002, Object recognition supported by user interaction for service robots.

[51]  Abhishek Bhardwaj,et al.  A review on integrating intrusion detection model using classifiers and chi-square feature selection , 2019 .

[52]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.