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Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent technique using lumped and distributed data

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Title Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent technique using lumped and distributed data
 
Creator MAGAR R.B
JOTHIPRAKASH, V
 
Subject Time-series models; Cause-effect models; Combined models; Daily and hourly; Lumped and distributed data; Artificial intelligent techniques
 
Description In this study, artificial intelligent (AI) techniques such as artificial neural network (ANN), Adaptive neuro-fuzzy inference system (ANFIS) and Linear genetic programming (LGP) are used to predict daily and hourly multi-time-step ahead intermittent reservoir inflow. To illustrate the applicability of AI techniques, intermittent Koyna river watershed in Maharashtra, India is chosen as a case study. Based on the observed daily and hourly rainfall and reservoir inflow various types of time-series, cause-effect and combined models are developed with lumped and distributed input data. Further, the model performance was evaluated using various performance criteria. From the results, it is found that the performances of LGP models are found to be superior to ANN and ANFIS models especially in predicting the peak inflows for both daily and hourly time-step. A detailed comparison of the overall performance indicated that the combined input model (combination of rainfall and inflow) performed better in both lumped and distributed input data modelling. It was observed that the lumped input data models performed slightly better because; apart from reducing the noise in the data, the better techniques and their training approach, appropriate selection of network architecture, required inputs, and also training–testing ratios of the data set. The slight poor performance of distributed data is due to large variations and lesser number of observed values.
 
Publisher Elsevier
 
Date 2012-07-17T10:26:07Z
2012-07-17T10:26:07Z
2012
 
Type Article
 
Identifier Journal of Hydrology,450-451()293-307
0022-1694
http://dspace.library.iitb.ac.in/jspui/handle/100/14390
http://dx.doi.org/10.1016/j.jhydrol.2012.04.045
 
Language English