Flood Prediction and Warning System using Dam Data Monitoring
Meenu Thomas1, Shuhaib Ummer2, Sruthi Vijayan T K3, Vivek T4, Ashok S Kumar5
1Meenu Thomas, Electronics and Communication, NSS College of Engineering, Palakkad, India.
2Shuhaib Ummer, Electronics and Communication, NSS College of Engineering, Palakkad, India.
3Sruthi Vijayan T K, Electronics and Communication, NSS College of Engineering, Palakkad, India.
4Vivek T, Electronics and Communication, NSS College of Engineering, Palakkad, India.
5Ashok S Kumar, Electronics and Communication, NSS College of Engineering, Palakkad, India.
Manuscript received on June 16, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 10, 2020. | PP: 294-298 | Volume-9 Issue-9, July 2020 | Retrieval Number: 100.1/ijitee.I7152079920 | DOI: 10.35940/ijitee.I7152.079920
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Flood is one of the most devastating natural calamities affecting parts of the state from past few years. The recurring calamity necessitates an efficient early warning system since anticipation and preparedness play a key role in mitigating the impact. Though heavy and erratic rainfall has been marked as one of the main reasons for flood in several places, flood witnessed by various regions of Kerala was the result of sudden opening of reservoirs indicating poor dam management. The unforeseen flow of water often provided less time for evacuation. Prediction thus plays key role in avoiding loss of life and property, followed by such calamities. The vast benefits and potentials offered by Machine Learning makes it the most promising approach. The developed system is a model by taking Malampuzha Dam as reference. Support Vector Machine (SVM) is used as machine learning method for prediction and is programmed in python. The idea has been to create early flood prediction and warning system by monitoring different weather parameters and dam-related data. The feature vectors include current live storage, current reservoir level, rainfall and relative humidity from the period 2016-2019. Based on the analysis of these parameters, the open/closure of shutters of the dam is predicted. Release of shutters has varied impacts in the nearby regions and is measured by succeeding prediction, by mapping regions on grounds of level warning to be issued. Warning is issued through Flask-based server, by identifying vulnerable areas based on flood hazard reference for regions. The dam status prediction model delivered highest prediction accuracy of 99.14% and associated levels of warning has been generated in the development server, thus preventing unexpected release.
Keywords: Flask, Gridsearch CV, inundation, SVM.
Scope of the Article: Data Analytics