Abstract

Floods are among the most horrendous, complex catastrophic events to imitate. Throughout recent years, neural network approaches have contributed to creating prediction frameworks that give better execution and financially intelligent solutions to emulate the complex factual indications of normal flood processes. Research on the advancement of flood prediction models has added to take a chance with reduction, a strategy proposition, a decrease of human life, and relief of flood-related property harm. To overcome this issue, predict the event of floods or not with a rainfall data set by researching neural network-based procedures. The Multi-facet Perceptron Classifier (MLP) will do data set examination to catch subtleties like unique, recognizable proof, shortage treatment, information approval, and information cleaning/planning across the given information base. To apply flood prediction for or without precise computation in the class division report, track down the confusion matrix, and the outcome shows the effectiveness of the python.

References

1. Pengzhan Cui, Yeqing Guan, Ying Zhu. 'Flood Loss Prediction of Coastal City Based on GM-ANN' International Conference on Grey Systems and Intelligent Services (GSIS):2017
2. Swapnil Bande, Virendra V. Shete. 'Smart flood disaster prediction system using IOT & Neural Networks' International Conference On Smart Technologies For Smart Nation (SmartTechCon):2017
3. Febus Reidj G. Cruz, Matthew G. Binag, Marlou Ryan G, Francis Aldrine A. 'Flood Prediction Using Multi-Layer Artificial Neural Network in Monitoring System with Rain Gauge, Water Level, Soil Moisture Sensors' TENCON 2018 - 2018 IEEE Region 10 Conference:2018
4. Indrastanti R. Widiasari, Lukito Edi Nugoho, Widyawan, Rissal Efendi. 'Context-based Hydrology Time Series Data for A Flood Prediction Model Using LSTM' 5th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE):2018
5. Haniyeh Seyed Alinezhad, Jun Shang and Tongwen Chen. ”Early Classification of Industrial Alarm Floods Based on Semi-Supervised Learning” IEEE :2021
6. T Gurleen Kaur, Anju Bala. ” An Efficient Automated Hybrid Algorithm to Predict Floods in Cloud Environment” IEEE Xplore :2019
7. Manomy K V, Meghna C S, Renuka Jayan, Raghi R Menon. “Flood Prediction and Tracking Trapped” IJERT Vol 9,Issue 6 :2020
8. J. E. Reynolds, S. Halldin, J. Seibert, C.Y. Xu & T. Grabs .”Flood prediction using parameters calibrated on limited discharge data and uncertain rainfall scenarios” Hydrological Sciences Journal, 65:9 :2020
9. Xiaodong Ming, Qiuhua Liang, Xilin Xia, Dingmin Li, and Hayley J. Fowler . ”Real‐Time Flood Forecasting Based on a High‐ Performance 2‐D Hydrodynamic Model and Numerical Weather Predictions” AGU Water Resources Research Vol 56 Issue 7 :2019
10. Sharad Kumar Jain, Pankaj Mani, Sanjay K. Jain, Pavithra Prakash, Vijay P. Singh, Desiree Tullos, Sanjay Kumar, S. P. Agarwal & A. P. Dimri. ”A Brief review of flood forecasting techniques and their applications.” DOI:10.1080/15715124.2017.1411920 :2017

Download PDF
Back