Controls and intelligence behind &amp;#x201C;NISTARA-2&amp;#x201D; &amp;#x2014; A disaster management machine (DMM)

Bhardwaj, Nishant; Aggarwal, Nitish; Ahlawat, Nishant; Rana, Charu

doi:10.1109/cipech.2014.7019069

Cited by 8 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The research included various sensors, master computer placed in a safest zone and in case of disaster there must be backup of master computer with others various functions like camera and voice recognition. The multipurpose device for disaster management can be installed in malls, schools and hospitals as well [18]. Cloud server was built to gather data on cumulative basis and to investigate huge data of long time for the decision making.…”

Section: A Sensors and Gauges Based Measurementmentioning

confidence: 99%

Investigation of Flash Floods on Early Basis: A Factual Comprehensive Review

et al. 2020

View full text Add to dashboard Cite

show abstract

Section: A Sensors and Gauges Based Measurementmentioning

confidence: 99%

Investigation of Flash Floods on Early Basis: A Factual Comprehensive Review

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Kalman filtering, fuzzy logic, clustering, Neural network autoregressive model with exogenous input (NNARX), Particle swarm optimization (PSO) and Support vector machine have been applied for the prediction and estimation of flash floods [10]. Fuzzy logic based a disaster management device has been designed for the announcement of exit routes during the hazard [11]. Ensemble learning model has also been designed for the better generalization model of classification [12].…”

Section: Introductionmentioning

confidence: 99%

A Comparison Review based on Classifiers and Regression Models for the Investigation of Flash Floods

Khan¹,

Alam²,

Kadir³

et al. 2019

IJACSA

View full text Add to dashboard Cite

Several regions of the world have been affected by one of the natural disasters named as flash floods. Many villagers who live near stream or dam, they suffer a lot in terms of property, cattle and human lives loss. Conventional early warning systems are not up to the mark for the early warning announcements. Diversified approaches have been carried out for the identification of flash floods with less false alarm rate. Forecasting approaches includes some errors and ambiguity due to the incompetent processing algorithms and measurement readings. Process variables like stream flow, water level, water color, precipitation velocity, wind speed, wave's pattern and cloud to ground (CG) flashes have been measured for the robust identification of flash floods. A vibrant competent algorithm would be required for the investigation of flash floods with less false alarm rate. In this research paper classifiers have been applied on the collected data set so that any researcher could easily know that which classifier is competent and can be further enhanced by combining it with other algorithms. A novel comprehensive parametric comparison has been performed to investigate the classification accuracy for the robust classification of false alarms. For the better accuracy more than one process variables have been measured but still contained some false alarm. Appropriate combination of sensor was integrated to increase the accuracy in results as multi-modal sensing device has been designed to collect the data. Linear discriminant analysis, logistic regression, quadratic support vector machine, knearest neighbor and Ensemble bagged tree have been applied to the collected data set for the data classification. Results have been obtained in the MATLAB and discussed in detail in the research paper. The worst accuracy of the classification (62%) has been achieved by the coarse k-NN classifier that means coarse k-NN produced 38% false negative rate that is not acceptable in the case of forecasting. Ensemble bagged trees produced best classification results as it achieved 99 % accuracy and 1% error rate. Furthermore, according to the comprehensive parametric comparison of regression models Quadratic SVM found to be the worst with mean square error of 0.5551 and time elapsed 13.159 seconds. On the other hand, Exponential Gaussian process regression performed better than the other existing approaches with the minimum root mean squared error of 0.0002 and prediction speed of 35000 observations per second.

show abstract

Flash Floods Prediction using Real Time data: An Implementation of ANN-PSO with less False Alarm

Khan

Alam

Shaikh

et al. 2019

2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

View full text Add to dashboard Cite

Flash floods and hurricanes are caused by the release of energy inside the oceans. Hurricanes are very sudden and may lead to heavy infrastructural damage with loss revenues associated human and animal's fatalities. Diversified techniques have been utilized to properly investigate the flash floods and hurricanes before the event. A hydro atmospheric and climatic change due to the hurricanes leads towards the high death toll. Approaches for the early prediction of flash floods and hurricanes may be categorized as (a) Modeling of the system (bathymetry), (b) Sensors and gauges-based measurement, (c) Radar-based images, (d) Satellite images and data, and (e) AI-based prediction. Comparative analysis of direct real-time data from the sensors and gauges, is more reliable compared to other techniques but it may contain some errors and missing information which leads towards the false alarms. Therefore, in this paper, a novel predictive hybrid algorithm (ANN PSO) has been applied to estimate the flash floods and hurricanes more precisely. A suitable combination of the sensors will give the benefit of better precision and improved accuracy when compare to the use of a single sensor. The combination of six process variables utilized in this paper for the measurement and investigation of the flash flood has been discussed. Real-time data of over forty eight (48) hours has been collected from PIR, Ultrasonic sensor, Temperature sensor, CO2 sensor, Rainfall module, Pressure, and temperature sensor. ANN feed-forward propagation is trained by using sample collected data from the multi-modal sensing device and applied for the classification of events while neurons are optimized by the particle swarm optimization (PSO), taking less processing time without requiring advanced complex computational resources. Results have proved that proposed AI based technique for the early identification of flash floods and hurricanes have worked more accurate and performance-wise better than the ongoing techniques. The results include flood probabilities and prediction analysis using proposed algorithm.

show abstract

Controls and intelligence behind “NISTARA-2” — A disaster management machine (DMM)

Cited by 8 publications

References 0 publications

Investigation of Flash Floods on Early Basis: A Factual Comprehensive Review

Investigation of Flash Floods on Early Basis: A Factual Comprehensive Review

A Comparison Review based on Classifiers and Regression Models for the Investigation of Flash Floods

Flash Floods Prediction using Real Time data: An Implementation of ANN-PSO with less False Alarm

Contact Info

Product

Resources

About