Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Intermittent Reservoir Daily Inflow Prediction Using Stochastic and Model Tree Techniques

Intermittent Reservoir Daily Inflow Prediction Using Stochastic and Model Tree Techniques The present study depicts the applicability of model tree (MT) technique to a large data set having large number of zero values. It is also aimed to develop a model that results in simple equations as that of stochastic models. The performance of MT is compared with conventional autoregressive integrated moving average (ARIMA) models. Forty-nine years of daily inflow data from Koyna Reservoir located in Maharashtra, India, are used for developing and testing the models. In this case study of developed MT models, the number of inputs is selected by trial and error and is varied from one lag to eight lags. Numerous MT models were developed by considering the model formulations of pruning and smoothing, whereas in ARIMA model, the number of inputs required for proper modeling is selected from autocorrelation function and partial autocorrelation function plots as well as through trial-and-error procedure. The performances of the developed models were evaluated using various statistical measures. On comparing the daily time step MT and ARIMA models, it is found that un-pruned and un-smoothed MT models performed better than ARIMA models. Even though the number of leaves (local linear equations with nonlinear way of finding them) is slightly larger, the low and peak values of the reservoir inflow are predicted better by MT model. From the results, it is concluded that for better modeling and to have a set of linear applicable equations for smaller time step reservoir inflow, MT technique can be a better choice than ARIMA model. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of The Institution of Engineers (India): Series A Springer Journals

Intermittent Reservoir Daily Inflow Prediction Using Stochastic and Model Tree Techniques

Loading next page...
 
/lp/springer-journals/intermittent-reservoir-daily-inflow-prediction-using-stochastic-and-m4NZBT0w5x
Publisher
Springer Journals
Copyright
Copyright © 2019 by The Institution of Engineers (India)
Subject
Engineering; Civil Engineering
ISSN
2250-2149
eISSN
2250-2157
DOI
10.1007/s40030-019-00368-w
Publisher site
See Article on Publisher Site

Abstract

The present study depicts the applicability of model tree (MT) technique to a large data set having large number of zero values. It is also aimed to develop a model that results in simple equations as that of stochastic models. The performance of MT is compared with conventional autoregressive integrated moving average (ARIMA) models. Forty-nine years of daily inflow data from Koyna Reservoir located in Maharashtra, India, are used for developing and testing the models. In this case study of developed MT models, the number of inputs is selected by trial and error and is varied from one lag to eight lags. Numerous MT models were developed by considering the model formulations of pruning and smoothing, whereas in ARIMA model, the number of inputs required for proper modeling is selected from autocorrelation function and partial autocorrelation function plots as well as through trial-and-error procedure. The performances of the developed models were evaluated using various statistical measures. On comparing the daily time step MT and ARIMA models, it is found that un-pruned and un-smoothed MT models performed better than ARIMA models. Even though the number of leaves (local linear equations with nonlinear way of finding them) is slightly larger, the low and peak values of the reservoir inflow are predicted better by MT model. From the results, it is concluded that for better modeling and to have a set of linear applicable equations for smaller time step reservoir inflow, MT technique can be a better choice than ARIMA model.

Journal

Journal of The Institution of Engineers (India): Series ASpringer Journals

Published: Mar 9, 2019

References