1. How to submit my research paper? What’s the process of publication of my paper?
The journal receives submitted manuscripts via email only. Please submit your research paper in .doc or.pdf format to the submission email: ijlbpr@ejournal.net.
You’ll be given a paper number if your submission is successful. Your paper then will undergo peer review process, which may take approximately one and a half months under normal circumstances, three tops.
After blind peer review, you will receive the notification letter with the review result of your paper...
2. Can I submit an abstract?
The journal publishes full research papers. So only full paper submission should be [Read More]

Comparative Study of Stream Flow Prediction Models

S R Asati and S S Rathore
Department of Civil Engineering, MIET, Gondia 441614, Maharashtra State.
Abstract—Stream flow prediction is required to provide the information of various problems related to the design and effective operation of river balancing system. The evaluation of natural and technical science over the past centuries has been closely related to experimental studies and modeling of natural resources. Methods to continuously predict water levels at a site along a river are generally its model based. Hydrologist has relied on individual techniques such as determinates, stochastic, conceptual or black box type to model the complex, uncertain rainfall and consecutive water levels. These techniques provide reasonable accuracy in modeling and prediction of stream flow. River Wainganga has been subjected to water level rise during 2004-2005 and, consequently, the low-laying areas along the bank are in undated, giving problems to local inhabitants, irrigation activities and people properly. Another river Bagh has been connecting to the said river the problem of flood arises more. Therefore predicting water levels has started to attract the attention of the researchers. How this local problem get solved or minimized? An attempt has been made to use the conventional method such as Autoregressive model, more deterministic approach through multi-Linear Regression model and Artificial Neural Network which is capable of identifying complex non-linear relationship between input and output data without attempting to reach understanding into the nature of the process. The performances of these approaches were compared and the best possible result amongst them is the key point of this study.

Index Terms—Artificial neural network, Runoff, River, Auto-regressive, Multi-linear regression

Cite: S R Asati and S S Rathore, "Comparative Study of Stream Flow Prediction Models," International Journal of Life Sciences Biotechnology and Pharma Research, Vol. 1, No. 2, pp. 139-151, April 2012.
Copyright © 2012-2015 International Journal of Life Sciences Biotechnology and Pharma Research, All Rights Reserved