Generating Spatial Distribution and Forecasting the Rainfall by Suitable ML Models-A Case Study of Aiyar River Basin, Tiruchirappalli District

• Published in: MATEC Web of Conferences Vol. 400; p. 2011
• Main Authors: Natarajan, Surendar, Jegan Bharath Kumar, A.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 2024
• Subjects: Accuracy; aiyar basin; Algorithms; arc-gis; Comparative studies; Deep learning; Flood forecasting; Flood management; Flood predictions; Machine learning; ml techniques; Natural disasters; Rainfall; rainfall prediction; River basins; Spatial distribution; Support vector machines; svr; Water resources; Water storage
• ISSN: 2261-236X; 2274-7214; 2261-236X
• DOI: 10.1051/matecconf/202440002011

Rainfall plays a prominent role in managing of water resources. The accurate prediction of rainfall is the greatest challenge in the field of hydrologic studies. The prediction of rainfall is necessary to overcome natural disasters like flood and drought. The inaccurate prediction of rainfall causes either dry or overflow in water storage structures. In this study different types of Machine Learning (ML) and deep learning techniques are adopted to predict rainfall pattern of Aiyar river basin, in Tiruchirappalli district. The comparative study of these ML models is done to identify the best ML model for the study area. The comparison was done for different scenarios and time intervals. The rainfall data from years 1987 to 2023 is used for predicting the daily rainfall in the basin. The rainfall data from years 1987 to 2007 is used for testing and the remaining years data is used for training the data set. The Theisen polygon method is used to average and weighted the rainfall data in the basin. The ML models and deep learning techniques used in this study are Linear model, Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) models. The rainfall was predicted for different time scenario by using different ML algorithms like Autocorrelation method. The accuracy of the predicted model results was tested with RMSE, MASE and R square values. The result shows coefficient between 0.5 to 0.9 within the limit from the daily rainfall values. From the overall model comparison, it is observed that the SVM model accuracy is high compared to the other models involved in this study. It is concluded that two different methods ML and deep learning methods have been applied with same data in which SVM ML techniques gives better results in this study area. In future the predicted rainfall data of this study can be used for accurate flood forecasting and modelling of Aiyar basin.