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

Download PDF
8 pages

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 A – Springer Journals

Published: Mar 9, 2019

References

  • Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis
    Adamowski, J
  • A new look at the statistical model identification
    Akaike, H
  • Optimal reservoir operation for hydropower generation using non-linear programming model
    Arunkumar, R; Jothipraksh, V
  • Neural networks and M5 model trees in modelling water level-discharge relationship
    Bhattacharya, B; Solomatine, DP
  • A machine learning approach to modelling sediment transport
    Bhattacharya, B; Price, RK; Solomatine, DP
  • Effect of pruning and smoothing while using M5 model tree technique for reservoir inflow prediction
    Jothiprakash, V; Kote, A
  • Improving the performance of data driven techniques through data pre-processing while modeling the daily reservoir inflow
    Jothiprakash, V; Kote, SA
  • Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent technique using lumped and distributed data
    Jothiprakash, V; Magar, RB
  • Stochastic and artificial neural network models for reservoir inflow prediction
    Kote, AS; Jothiprakash, V
  • Intermittent reservoir daily-inflow prediction using lumped and distributed data multi-linear regression models
    Magar, RB; Jothiprakash, V
  • Short term rainfall prediction using ANN and MT techniques
    Mandal, T; Jothiprakash, V
  • Time series model for rainfall data in Jordan: case study for using time series analysis
    Momani, M; Naill, PE
  • Forecasting monthly river flow time series
    Nokes, DJ; McLeod, I; Hipel, KW
  • Application of MLP neural network and M5P model tree in predicting streamflow: a case study of Luvuvhu Catchment, South Africa
    Onyari, EK; Ilunga, FM
  • M5 model trees based modeling of reference evapotranspiration
    Pal, M; Deswal, S
  • Use of model tree and gene expression programming to predict the suspended sediment load in rivers
    Reddy, MJ; Ghimire, BNS
  • Modeling of short data description
    Rissanen, J
  • M5 model trees and neural networks based modelling of ET0 in Ankara, Turkey
    Sattari, MT; Pal, M; Yurekli, K; Ünlukara, A
  • Advances in Data-Based Approaches for Hydrologic Modeling and Forecasting
    Sivakumar, B; Berndtsson, R
  • Model trees as an alternative to neural networks in rainfall-runoff modeling
    Solomatine, DP; Dulal, KN
  • M5 model tree and neural networks: application to flood forecasting in the upper reach of the Huai River in China
    Solomatine, DP; Xue, Y
  • Development of a low-flow forecasting model using the M5 machine learning method
    Štravs, L; Brilly, M
  • Data Mining: Practical Machine Learning Tools and Techniques with Java Implementation
    Witten, H; Frank, E
  • Prediction of daily maximum streamflow based on stochastic approaches
    Yurekli, K; Kurunc, A; Simsek, H