Abstract
Flash floods can demolish infrastructure and property within seconds as they are very sudden. Flash floods are the main cause of the casualties and loss of properties. Existing natural disaster prediction algorithms contains false alarms. Indefinite techniques have been applied to overcome this leading issue in many countries. A competent flood management system must have the potential and tendency to identify the flash floods and atmospheric and climatic changes on early basis with less false alarm rate. Techniques which have been designed for the flash flood investigation may be categorized into following types a. Sensors based direct measurement b. Radar images c. Satellite based X-band images. The proposed research consisted of Artificial intelligence-based decision making for multi-modal sensing (direct measurement from multi-resolution sensors). A combination of sensors like Passive infrared (PIR), water level sensor, ultrasonic sensor, temperature sensor, pressure and altimeter sensors have been integrated on a single device to investigate the flash floods. The use of most suitable pair of measurement sensors can substantially enhance the advantage of more accuracy and reliability compared to a single sensor. In recent trends Particle swarm optimization is very popular for solving stochastic global optimization problems. The data was trained and processed by modified multi-layer feed forward neural network optimized by particle swarm optimization algorithm. Hybrid Modified Particle swarm optimization has been combined with feed forward neural network for the vigorous investigation of flash floods with less false alarm rate. Simulated results showed that the proposed research algorithm Modified multi-layer feed forward neural network optimized by Particle swarm optimization for multi-modal sensing performed very well in terms of evaluation parameters compared to other existing strategies with minimum false alarm ratio. Moreover, modified multi-layer feed forward neural network optimized by article swarm optimization algorithm results have been compared with the cuckoo search, modified cuckoo search, particle swarm optimization and Multi-layer perceptron neural network configurations for the validation purpose.
Highlights
Many countries like Pakistan, Malaysia, Indonesia, Japan, Bangladesh, France etc. are badly affected annually due to the flash floods
The analysis proved that root mean square (RMSE), best fit and accuracy of modified MFNPSO was found to be 0.0037, 97.3 and 98.89% respectively
Simulated results proved that proposed algorithm worked as a better classifier and forecasting tool for the flash floods
Summary
Many countries like Pakistan, Malaysia, Indonesia, Japan, Bangladesh, France etc. are badly affected annually due to the flash floods. Warning predictive analysis systems have been implemented in many countries and running successfully but forecasting the exact actual timings with much detailed information is very complex as false alarms may be detected due to the inadequate processing algorithms. Sensors and Instrumentation always introduce some kind of errors and false alarm which may lead towards incorrect measurement and observations. High ratio of false alarm rate in forecasting disaster events leads towards the high number of casualties and infrastructural loss. Disaster management authorities cannot predict natural hazards accurately and precisely like flash floods, tsunami and earthquake. A competent early warning system was strongly needed for forecasting any type of natural hazards like tsunami, flash floods and seismic events. Intense floods can be regarded as the basic cause of infrastructural losses and casualties in various countries like Malaysia, Pakistan, Southern France, India, VOLUME XX, 2017
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