Abstract
Precipitation plays an important role in various applications like agriculture, health care, disaster management, forest health and others. These applications are directly or indirectly affected by precipitation. In order to predict precipitation, it becomes important to analyze the pattern followed by the event. The generated pattern should be accurate to obtain the prediction at par. Presence of outliers in any data significantly affects the generated pattern, time required and accuracy in prediction. Treating missing values is not sufficient to get higher accuracy in prediction. In this paper, we present the performance evaluation of Support vector regression (SVR), Seasonal auto regressive integrated moving average (SARIMA), and Bi-directional LSTM (Bi-LSTM) for prediction of hydro estimator values (HEM), in domain of machine learning, statistical and deep learning approaches respectively. We have made a comparison between the generated pattern and prediction with and without outliers. We have identified a suitable outlier detection technique by comparing the performance of Cluster Based Local Outlier Factor (CBLOF), Histogram based outlier Detection (HBOS), K nearest neighbour (KNN), Isolation Forest for detecting outliers. The performance on prediction techniques are compared using RMSE and MAE measures. The implementation is done over a big data architecture, since the data is, Big data’.
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Sisodiya, N., Vyas, K., Dube, N., Thakkar, P. (2023). Analyzing Hydro-Estimator INSAT-3D Time Series with Outlier Detection. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1776. Springer, Cham. https://doi.org/10.1007/978-3-031-31407-0_37
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