NDLI: A comparison of Artificial Neural Networks (ANN) and Local Linear Regression (LLR) techniques for predicting monthly reservoir levels

Content Provider	Springer Nature Link
Author	Shamim, Muhammad Ali Hassan, Muhammad Ahmad, Sameel Zeeshan, Muhammad
Copyright Year	2016
Abstract	Storage dams play a very important role in irrigation especially during lean periods. For proper regulation one should make sure the availability of water according to needs and requirements. Normally regression techniques are used for the estimation of a reservoir level but this study was aimed to account for a non-linear change and variability of natural data by using Gamma Test, for input combination and data length selection, in conjunction with Artificial Neural Networking (ANN) and Local Linear Regression (LLR) based models for monthly reservoir level prediction. Results from both training and validation phase clearly indicate the usefulness of both ANN and LLR based prediction techniques for Water Management in general and reservoir level forecasting in particular, with LLR outperforming the ANN based model with relatively higher values of Nash-Sutcliffe model efficiency coefficnet (R$^{2}$) and lower values of Root Mean Squared Error (RMSE) and Mean Biased Error (MBE). The study also demonstrates how Gamma test can be effectively used to determine the ideal input combination for data driven model development.
Starting Page	971
Ending Page	977
Page Count	7
File Format	PDF
ISSN	12267988
Journal	KSCE Journal of Civil Engineering
Volume Number	20
Issue Number	2
e-ISSN	19763808
Language	English
Publisher	Korean Society of Civil Engineers
Publisher Date	2015-06-19
Publisher Place	Seoul
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	reservoir level Artificial Neural Network ANN Local Linear Regression LLR Gamma Test (GT) Civil Engineering Industrial Pollution Prevention Geotechnical Engineering & Applied Earth Sciences
Content Type	Text
Resource Type	Article
Subject	Civil and Structural Engineering

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