American Journal of Innovative Research & Applied Sciences
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| ARTICLES | Am. J. innov. res. appl. sci. Volume 7, Issue 2, Pages 109-117 (August 2018)
American Journal of innovative
Research & Applied Sciences
ISSN 2429-5396 (Online)
OCLC Number: 920041286
| AUGUST VOLUME 7 | N° 2 | 2018 |
*Correspondant author and authors Copyright © 2018:
| Ghatfan, Abdalkareem Ammar 1 | Amer, Qousai Al Darwish 2 | and | Alaa, Ali Slieman 3 |
1. Tishreen University | department of water engineering and irrigation | Lattakia | Syria |
2. AL Baath University | Department of Water Resources Engineering and Management | Homs | Syria |
3.AL Baath University | Department of Water Resources Engineering and Management | Homs | Syria |
This article is made freely available as part of this journal's Open Access: ID | Ghatfan-ManuscriptRef.2-ajira180818 |
Background: Water resource planning and management requires long time series of hydrological data (e.g. precipitation, river flow). However, sometimes hydrological time series happen to be incomplete or to have missing values. Objectives: This paper focuses on the possibility of using artificial neural network (ANN) in the treatment of the missing precipitation data in AL Rasafeh station (Hama, Syria) as a target station, using the available data of neighboring stations (Ein Hlaqeem, Wade Alaewn, Messiaf), which were used as reference stations during the period from (1/1/1994) to (31/12/2001). Methods: This paper presents a technique for replacing missing spatial data using a feed forward neural network with Levenberg-Marquardt algorithm applied to concurrent data from nearby gauges of the three reference stations depending on their correlation coefficients with the target station. Results: On evaluating the performance of FFNN with LM algorithm models, it turned out that these models have the ability to estimate, accurately and reliably, the missing amounts of the target station, which allows for a flexible, easy-to-use system that can be developed later by increasing the number of the neighboring stations and selecting the most accurate model to predict the missing precipitation values in a certain period (Mean-Root Square Error, RMSE) in conformity with a case of missing values of one or more neighboring stations for the same period. Conclusions: Access to accurate and reliable precipitation data is essential to water resources assessment, management and planning activities. Results show good ability of the proposed models to replace missing rain gauge data in the target station using a Feedforward neural network that calculates its estimates on precipitation amounts from nearby rain gauges depending on available patterns of inputs.
Keywords: Precipitation, Prediction, Artificial neural network, data estimation, missing data.
INFILLING DAILY PRECIPITATION DATA USING FEEDFORWARD BACK-PROPAGATION ARTIFICIAL NEURAL NETWORKS (ANN), HAMA, SYRIA
| Ghatfan, Abdalkareem Ammar 1 | Amer, Qousai Al Darwish 2 | and | Alaa, Ali Slieman 3 |. Am. J. innov. res. appl. sci. 2018; 7(2):109-117.
| PDF FULL TEXT | | XML FILE | | EPUB FILE | | Received | 12 August 2018 | | Published | 27 August 2018 | | Published | 28 August 2018 |