Prior investigation for flash floods and hurricanes, concise capsulization of hydrological technologies and instrumentation: A survey

Intense and severe torrents, tornadoes and thunderstorm causes numerous casualties in fraction of second and extreme devastation of infrastructure in many countries. Flash floods are considered one of the most hazardous natural disasters. Several approaches have been made for an authentic and robust early warning system to forecast the flash floods vigorously. An intelligent and vibrant model for the recognition of floods includes the estimation of water level, Global Positioning System-Precipitable Water Vapor (GPS PWV), precipitation velocity, wind speed, direction, upstream levels of river, soil moisture, oceanic bottom pressure and color of the water with accurate and reliable cognition algorithms. UGS (unattended ground sensors) and langrangian micro transducers are deployed on the ground and spread on the sea surface respectively to investigate the hydrological and meteorological differences on real time basis. By the utilization of fuzzy logic, Kalman filtering, Adaptive neuro fuzzy interference system (ANFIS), Particle Swarm Optimization (PSO) and Neural network autoregressive model with exogenous input (NNARX) based structure. Reduction of complexities in data collection, high false alarm rates, communication issues, low WSN battery backup and all related hindrances have been the focal point of this research paper.

[1]  John L. Largier,et al.  Japan Tsunami Current Flows Observed by HF Radars on Two Continents , 2011, Remote. Sens..

[2]  G. Evensen.  The ensemble Kalman filter for combined state and parameter estimation , 2009, IEEE Control Systems.

[3]  Chung Shue Chen,et al.  Real-time QoS support in wireless sensor networks: a survey , 2007 .

[4]  Asok Ray,et al.  Real-time activity recognition from seismic signature via multi-scale symbolic time series analysis (MSTSA) , 2015, 2015 American Control Conference (ACC).

[5]  Don Barrick,et al.  Simulator to evaluate tsunami warning performance for coastal HF radars , 2016, OCEANS 2016 MTS/IEEE Monterey.

[6]  Billie F. Spencer,et al.  SENSOR DEVELOPMENT USING BERKELEY MOTE PLATFORM , 2006 .

[7]  M.M. Rashid,et al.  Development of Particle Swarm Optimization Based Rainfall-Runoff Prediction Model for Pahang River, Pekan , 2016, 2016 International Conference on Computer and Communication Engineering (ICCCE).

[8]  V. Chandrasekar,et al.  Short wavelength technology and the potential for distributed networks of small radar systems , 2009, 2009 IEEE Radar Conference.

[9]  A. Round,et al.  The VENUS Cabled Observatory: Engineering Meets Science on the Seafloor , 2007, OCEANS 2007.

[10]  Yu Hen Hu,et al.  Detection, classification, and tracking of targets , 2002, IEEE Signal Process. Mag..

[11]  Ramli Adnan,et al.  3 Hours ahead of time flood water level prediction using NNARX structure: Case study pahang , 2016, 2016 7th IEEE Control and System Graduate Research Colloquium (ICSGRC).

[12]  C. Meinig,et al.  Technology developments in real-time tsunami measuring, monitoring and forecasting , 2005, Proceedings of OCEANS 2005 MTS/IEEE.

[13]  Mark B. Yeary,et al.  A brief overview of weather radar technologies and instrumentation , 2014, IEEE Instrumentation & Measurement Magazine.

[14]  Sebastiano B. Serpico,et al.  The OPERA project: EO-based flood risk management in Italy , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[15]  G. L. Goodman.  Detection and classification for unattended ground sensors , 1999, 1999 Information, Decision and Control. Data and Information Fusion Symposium, Signal Processing and Communications Symposium and Decision and Control Symposium. Proceedings (Cat. No.99EX251).

[16]  Yan Zhang,et al.  The Atmospheric Imaging Radar: Simultaneous Volumetric Observations Using a Phased Array Weather Radar , 2013 .

[17]  Ricardo Gonzalez,et al.  QoS on Wireless Sensor Network applied in post-seismic assessment , 2016, 2016 IEEE Colombian Conference on Communications and Computing (COLCOM).

[18]  S. Ghosh.  Flood control and drainage engineering , 1997 .

[19]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[20]  Quan J. Wang,et al.  A review of advances in flash flood forecasting , 2011 .

[21]  Alexandre M. Bayen,et al.  Ensemble Kalman Filter based state estimation in 2D shallow water equations using Lagrangian sensing and state augmentation , 2008, 2008 47th IEEE Conference on Decision and Control.

[22]  P. C. Nayak,et al.  Rainfall-runoff modeling using conceptual, data driven, and wavelet based computing approach , 2013 .

[23]  S. Glenn,et al.  High-frequency radar observations of the June 2013 US East Coast meteotsunami , 2014, Natural Hazards.

[24]  V. Chandrasekar,et al.  The Dallas Fort Worth urban remote sensing network , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[25]  Yves Goussard.  3D Reconstruction in X‐Ray Tomography: Approach Example for Clinical Data Processing , 2015 .

[26]  Lizhen Lu,et al.  Short-term water level prediction using different artificial intelligent models , 2016, 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics).

[27]  Daniela Rus,et al.  Model-based monitoring for early warning flood detection , 2008, SenSys '08.

[28]  Benoit Pirenne,et al.  The web-enabled awareness research network (WARN) project early earthquake and tsunami warning at Ocean Networks Canada , 2014, 2014 Oceans - St. John's.

[29]  Ramli Adnan,et al.  5 hours flood prediction modeling using NNARX structure: case study Kuala Lumpur , 2014, 2014 IEEE 4th International Conference on System Engineering and Technology (ICSET).

[30]  Scott McLean,et al.  Cabled observing stations for remote locations , 2013, 2013 OCEANS - San Diego.

[31]  Ramli Adnan,et al.  Flood water level modeling and prediction using NARX neural network: Case study at Kelang river , 2014, 2014 IEEE 10th International Colloquium on Signal Processing and its Applications.

[32]  Wail Gueaieb,et al.  A UAV based system for real time flash flood monitoring in desert environments using Lagrangian microsensors , 2013, 2013 International Conference on Unmanned Aircraft Systems (ICUAS).

[33]  Mark B. Yeary,et al.  PX-1000: A Solid-State Polarimetric X-Band Weather Radar and Time–Frequency Multiplexed Waveform for Blind Range Mitigation , 2013, IEEE Transactions on Instrumentation and Measurement.

[34]  B. Pirenne,et al.  The NEPTUNE Project - a cabled ocean observatory in the NE Pacific: Overview, challenges and scientific objectives for the installation and operation of Stage I in Canadian waters , 2007, 2007 Symposium on Underwater Technology and Workshop on Scientific Use of Submarine Cables and Related Technologies.

[35]  A. Macrander,et al.  PACT — a bottom pressure based, compact deep-ocean Tsunameter with acoustic surface coupling , 2009, OCEANS 2009-EUROPE.

[36]  S Ghosh.  Flood Control and Drainage Engineering, Fourth Edition , 2014 .

[37]  Donald E. Barrick,et al.  A coastal radar system for tsunami warning , 1979 .