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A Comparison of Artificial Neural Networks (ANN) and Local Linear Regression (LLR) Techniques for Predicting Monthly Reservoir Levels

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Abstract:
Storage dams play a very important role in irrigation especially during lean periods. For proper regulation one should make sure theavailability of water according to needs and requirements. Normally regression techniques are used for the estimation of a reservoirlevel but this study was aimed to account for a non-linear change and variability of natural data by using Gamma Test, for inputcombination 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 ofboth ANN and LLR based prediction techniques for Water Management in general and reservoir level forecasting in particular, withLLR outperforming the ANN based model with relatively higher values of Nash-Sutcliffe model efficiency coefficnet (R2) and lowervalues of Root Mean Squared Error (RMSE) and Mean Biased Error (MBE). The study also demonstrates how Gamma test can beeffectively used to determine the ideal input combination for data driven model development.

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Ketersediaan

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Informasi Detail

Judul Seri

Civil Engineering

No. Panggil

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Penerbit

: Springer Science., 2016

Deskripsi Fisik

Journal of Civil Engineering (2016) 20(2):971-977

Bahasa

English

ISBN/ISSN

DOI 10.1007/s12205-0

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

(2016) 20(2):

Subjek

Civil Engineering

Info Detail Spesifik

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Pernyataan Tanggungjawab

deliza

Versi lain/terkait

Lampiran Berkas

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