Predictive Analysis of Flood Forecasting through Machine Learning Algorithms: Study on Cauvery Basin, India

Main Article Content

Article Sidebar

Published Sep 8, 2021
Shobhit Shukla

Abstract

This paper tries to explore several machine learning techniques namely, Nonlinear Autoregressive Network with Exogenous Inputs (NARX) [1], Artificial Neural Networks (ANN) [2], Tree Bagger [3], Support Vector Machine (SVM) [4], Gaussian Process Regression (GPR) and Adaptive Neuro Fuzzy Inference System (ANFIS) [4] for the purpose of foretelling floods by envisaging river flow in Cauvery river basin of southern India. The techniques were applied on various models constructed from combinations of various antecedent river flow values from two gauging stations and the results were compared for each technique. This paper utilizes three standard performance assessment measures viz. Mean Squared Error (MSE), coefficient of correlation (R) and Nash-Sutcliffe coefficient (NS) [5] for the purpose of assessing the efficacy of models developed. A comprehensive evaluation of the performance parameters for each model established that the Support Vector Machine model achieved best performance in comparison with other models for the purpose of flood forecasting.

How to Cite

Shukla, S. (2021). Predictive Analysis of Flood Forecasting through Machine Learning Algorithms: Study on Cauvery Basin, India. SPAST Abstracts, 1(01). Retrieved from https://spast.org/techrep/article/view/200
Abstract 5 |

Article Details

Keywords

Artificial Neural Networks (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Machine (SVM), Nonlinear Autoregressive Network with Exogenous Inputs (NARX), Tree Bagger, Gaussian Process Regression (GPR)

References
[1]. Firat, M., & Gngr, M. (2008). Hydrological time-series modelling using an adaptive neuro-fuzzy inference system. Hydrological processes, 22(13), 2122-2132.
[2]. Siddiquee, M. S. A., & Hossain, M. M. A. (2015). Development of a sequential Artificial Neural Network for predicting river water levels based on Brahmaputra and Ganges water levels. Neural Computing and Applications, 26(8), 1979-1990.
[3]. Yajima, H., & Derot, J. (2018). Application of the Random Forest model for chlorophyll-a forecasts in fresh and brackish water bodies in Japan, using multivariate long-term databases. Journal of Hydroinformatics, 20(1), 206-220.
[4]. Okkan, U., Serbes, Z. A., & Samui, P. (2014). Relevance vector machines approach for long-term flow prediction. Neural Computing and Applications, 25(6), 1393-1405.
[5]. Keong, K. C., Mustafa, M., Mohammad, A. J., Sulaiman, M. H., & Abdullah, N. R. H. (2016, October). Artificial neural network flood prediction for sungai isap residence. In Automatic Control and Intelligent Systems (I2CACIS), IEEE International Conference on (pp. 236-241) IEEE.
Section
GE3- Computers & Information Technology