Proposal of a Computational Intelligence Prediction Model Based on Internet of Things Technologies

The new challenges of the humanity imply new challenges that require a research with novel proposals and oriented to problems that imply cyber-physical systems (CPS). Another aspect is the climate changes and the low economic resources in some regions of the world require new models of data collection through new technologies such as IoT and its processing for the use of different actors from ordinary citizens, state organizations to researchers in natural disasters. This proposal is oriented to the use of data based on IoT network to provide information to the prediction model using computational intelligence (IC) techniques to perform flood prediction. This preliminary experiment uses data from different sources using the cellular telephone network with meteorology and hydrology stations. The conditioning of data variables such as level (m), precipitation (mm of H2O), temperature (C) among other variables to the model implies a preliminary adjustment as the same development of the IC model. The number of variables to be used in the preliminary model IC are three variables as a basis for the proposed experiment and will be extended to the final model. The results obtained will be part of other projects oriented to the implementation the applications in agriculture and data processing. This project is being developed by the Universidad Distrital Francisco José de Caldas and the Uniagraria Foundation of Colombia.

[1]  Dennis J. Parker,et al.  Understanding and enhancing the public's behavioural response to flood warning information , 2009 .

[2]  Jay Lee,et al.  A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems , 2015 .

[3]  G. Delzanno,et al.  An “Internet of Things” Vision of the Flood Monitoring Problem , 2015 .

[4]  Paulo Alonso Gaona-García,et al.  Proposal of an Offline Data-Processing Network Model for Flood Analysis , 2019 .

[5]  Ramez Elmasri,et al.  Flood Prediction and Mining Influential Spatial Features on Future Flood with Causal Discovery , 2015, 2015 IEEE International Conference on Data Mining Workshop (ICDMW).

[6]  K. Kasiviswanathan,et al.  Flood frequency analysis using multi-objective optimization based interval estimation approach , 2017 .

[7]  Florian Pappenberger,et al.  Continental and global scale flood forecasting systems , 2016 .

[8]  Jim E Freer,et al.  Satellite-supported flood forecasting in river networks: A real case study , 2015 .

[9]  J. Yazdi,et al.  Identifying low impact development strategies for flood mitigation using a fuzzy-probabilistic approach , 2014, Environ. Model. Softw..

[10]  Christina Gloeckner Earth And Space Science , 2016 .

[11]  Wei Liu,et al.  A location-aided flooding mechanism in community-based IoT networks , 2017, 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC).

[12]  Jing Zhao,et al.  Gap analysis on open data interconnectivity for disaster risk research , 2019, Geo spatial Inf. Sci..

[13]  Gökçen Uysal,et al.  Streamflow Forecasting Using Different Neural Network Models with Satellite Data for a Snow Dominated Region in Turkey , 2016 .

[14]  T. Ross Fuzzy Logic with Engineering Applications , 1994 .

[15]  Mosaad Khadr,et al.  Forecasting of meteorological drought using Hidden Markov Model (case study: The upper Blue Nile river basin, Ethiopia) , 2016 .

[16]  Arnab Raha,et al.  A Simple Flood Forecasting Scheme Using Wireless Sensor Networks , 2012, ArXiv.

[17]  Walter J. Gutjahr,et al.  Multicriteria optimization in humanitarian aid , 2016, Eur. J. Oper. Res..