Next Article in Journal
Forecasting Low Stream Flow Rate Using Monte—Carlo Simulation of Perigiali Stream, Kavala City, NE Greece
Previous Article in Journal
Additive Manufactured Metallic Smart Structures to Monitor the Mechanical Behavior In Situ
Open AccessProceedings

Artificial Neural Network for Daily Low Stream Flow Rate Prediction of Perigiali Stream, Kavala City, NE Greece

1
Department of Civil Engineering, Democritus University of Thrace, Kimmeria Campus, 67100 Xanthi, Greece
2
Department of Mechanical Engineering, Eastern Macedonia & Thrace Institute of Technology, 65404 Kavala, Greece
*
Author to whom correspondence should be addressed.
Presented at the 3rd EWaS International Conference on “Insights on the Water-Energy-Food Nexus”, Lefkada Island, Greece, 27–30 June 2018.
Proceedings 2018, 2(11), 578; https://doi.org/10.3390/proceedings2110578
Published: 20 August 2018
(This article belongs to the Proceedings of EWaS3 2018)
Only a few scientific research studies with reference to extremely low stream flow conditions, have been conducted in Greece, so far. Forecasting future low stream flow rate values is a crucial and desicive task when conducting drought and watershed management plans, designing water reservoirs and general hydraulic works capacity, calculating hydrological and drought low flow indices, separating groundwater base flow and storm flow of storm hydrographs etc. Artificial Neural Network modeling simulation method generates artificial time series of simulated values of a random (hydrological in this specific case) variable. The present study produces artificial low stream flow time series of both a part of the past year (2016) as well as the present year (2017) considering the stream flow data observed during two different respecting interval period of the years 2016 and 2017. We compiled an Artificial Neural Network to simulate low stream flow rate data, acquired at a certain location of the partly regulated semi-urban stream which runs through the eastern exit of Kavala city, NE Greece, using a 3-inches U.S.G.S. modified portable Parshall flume, a 3-inches conventional portable Parshall flume, a 3-inches portable Montana (short Parshall) flume and a 90° V-notched triangular shaped sharp crested portable weir plate. The observed data were plotted against the predicted one and the results were demonstrated through interactive tables providing us the ability to effectively evaluate the ANN model simulation procedure performance. Finally, we plot the recorded against the simulated low stream flow rate data, compiling a log-log scale chart which provides a better visualization of the discrepancy ratio statistical performance metrics and calculate the derived model statistics featuring the comparison between the recorded and the forecasted low stream flow rate data.
Keywords: artificial neural network; discrepancy ratio; drought; low flow data; Parshall flume artificial neural network; discrepancy ratio; drought; low flow data; Parshall flume
MDPI and ACS Style

Papalaskaris, T.; Panagiotidis, T. Artificial Neural Network for Daily Low Stream Flow Rate Prediction of Perigiali Stream, Kavala City, NE Greece. Proceedings 2018, 2, 578. https://doi.org/10.3390/proceedings2110578

AMA Style

Papalaskaris T, Panagiotidis T. Artificial Neural Network for Daily Low Stream Flow Rate Prediction of Perigiali Stream, Kavala City, NE Greece. Proceedings. 2018; 2(11):578. https://doi.org/10.3390/proceedings2110578

Chicago/Turabian Style

Papalaskaris, Thomas; Panagiotidis, Theologos. 2018. "Artificial Neural Network for Daily Low Stream Flow Rate Prediction of Perigiali Stream, Kavala City, NE Greece" Proceedings 2, no. 11: 578. https://doi.org/10.3390/proceedings2110578

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop