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Improving the performance of data-driven techniques through data pre-processing for modelling daily reservoir inflow

DSpace at IIT Bombay

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Title Improving the performance of data-driven techniques through data pre-processing for modelling daily reservoir inflow
 
Creator JOTHIPRAKASH, V
KOTE, AS
 
Subject ARTIFICIAL NEURAL-NETWORK
RAINFALL-RUNOFF MODELS
RIVER FLOW MODELS
TIME-SERIES MODELS
PREDICTION
TREES
STREAMFLOWS
BASIN
ANN
data pre-processing
time-lagged recurrent network
model tree
linear genetic programming
moving average
transformation
full-year inflow data
seasonal inflow
Pawana Reservoir, India
 
Description The effect of data pre-processing while developing artificial intelligence (AI) -based data-driven techniques, such as artificial neural networks (ANN), model trees (MT) and linear genetic programming (LGP), is studied for Pawana Reservoir in Maharashtra, India. The daily one-step-ahead inflow forecasts are compared with flows generated from a univariate autoregressive integrated moving average (ARIMA) model. For the full-year data series, a large error is found mainly due to the occurrence of zero values, since the reservoir is located in an intermittent river. Hence, all the techniques are evaluated using two data series: 18 years of daily full-year inflow data (from 1 January to 31 December); and 18 years of daily monsoon season inflow data (from 1 June to 31 October) to take into account the intermittent nature of the data. The relevant range of inputs for each category is selected based on autocorrelation and partial autocorrelation analyses of the inflow series. Conventional preprocessing methods, such as transformation and/or normalization of data, do not perform well because of the large variation in magnitudes, as well as the many zero values (65% of the full-year data series). Therefore, the input data are pre-processed into un-weighted moving average (MA) series of 3 days, 5 days and 7 days. The 3-day MA series performs better, maintaining the peak inflow pattern as in the actual data series, while the coarser-scale (5-day and 7-day) MA series reduce the peak inflow pattern, leading to more errors in peak inflow prediction. The results indicate that AI methods are powerful tools for modelling the daily flow time series with appropriate data preprocessing, in spite of the presence of many zero values. The time-lagged recurrent network (TLRN) ANN modelling technique applied in this study maps the inflow forecasting in a better way than the standard multilayer perceptron (MLP) neural networks, especially in the case of the seasonal data series. The MT technique performs equally well for low and medium inflows, but fails to predict the peak inflows. However, LGP outperforms the other AI models, and also the ARIMA model, for all inflow magnitudes. In the LGP model, the daily full-year data series with more zero inflow values performs better than the daily seasonal models.
 
Publisher TAYLOR & FRANCIS LTD
 
Date 2012-06-26T09:56:26Z
2012-06-26T09:56:26Z
2011
 
Type Article
 
Identifier HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES,56(1)168-186
0262-6667
http://dx.doi.org/10.1080/02626667.2010.546358
http://dspace.library.iitb.ac.in/jspui/handle/100/14343
 
Language English