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Title |
Early Identification of Flash Floods: Artificial Intelligence based Forecasting using Modified Cuckoo Search |
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Author |
Talha Ahmed Khan, Muhammad Alam, Kushsairy Kadir, Zeeshan Shahid, M.S. Mazliham |
| Citation |
Vol. 25 No. 6 pp. 121-127 |
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Abstract |
Flash floods are one of the most dangerous natural disaster which can devastate number of buildings, lands, cattle and human lives within seconds. The early and sure identification of flash flood can be regarded as the most complex task. Heavy precipitation and chocked streams may also cause flash floods. Cattles have to suffer due to the extreme floods. Generally intensified floods damage all the objects that comes around in the affected area of flood including vehicles, roads, building and bridges as well. Diversified approaches have been developed and designed to predict the flash floods accurately and precisely. Construction on the basis of modeling of dams and reservoirs to prevent the flash floods have been suggested by researchers. Many Artificial intelligence-based methods like NNARX, PSO, MLP, Cuckoo search, Bayesian classifier, dempster Shafer and ANFIS have been designed to forecast the flash floods with less false alarm. Direct measurement from instruments and gauges were also considered important for the data collection. Several parameters have been used in the past to detect the flash floods like precipitation velocity, wind velocity, wind direction, temperature, humidity, soil moisture, pressure, water color and cloud to ground (CG) flashes. In our proposed research the multi-sensor data has been collected and artificial intelligence based a unique algorithm modified cuckoo search has been designed to reduce the false alarm. Results were successfully achieved in the MATLAB. Results have also been bench marked by comparing the MLP-PSO. Graphical analysis proved that our proposed algorithm worked better than the other existing approaches. |
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Keywords |
Modified cuckoo search, flash floods, PSO, Artificial Intelligence, Predictive analysis |
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