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

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.

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

Environmental Sciences Proceedings ◽

10.3390/environsciproc2020002070 ◽

2020 ◽

Vol 2 (1) ◽

pp. 70

Author(s):

Thomas Papalaskaris

Keyword(s):

Neural Network ◽

Time Series ◽

Artificial Neural Network ◽

Flow Rate ◽

Stream Flow ◽

Low Flow ◽

Urban Stream ◽

Neural Network Modeling ◽

Rate Data ◽

Artificial Neural

Only a few scientific research studies referencing extremely low flow conditions have been conducted in Greece so far. Forecasting future low stream flow rate values is a crucial and decisive task when conducting drought and watershed management plans by designing construction plans dealing with water reservoirs and general hydraulic works capacity, by calculating hydrological and drought low flow indices, and by separating groundwater base flow and storm flow of storm hydrographs, etc. The 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 part of 2015. We compiled an Artificial Neural Network to simulate low stream flow rate data, acquired at a certain location of the entirely regulated, urban stream, which crosses the roads junction formed by Iokastis road and an Chrisostomou Smirnis road, Agios Loukas residential area, Kavala city, Eastern Macedonia & Thrace Prefecture, NE Greece, during part of July, August, and part of September 2015, until 12 September 2015, using a 3-inches conventional portable Parshall flume. The observed data were plotted against the predicted one and the results were demonstrated through interactive tables by providing us the ability to effectively evaluate the ANN model simulation procedure performance. Finally, we plotted the recorded against the simulated low stream flow rate data by compiling a log-log scale chart, which provides a better visualization of the discrepancy ratio statistical performance metrics and calculated further statistic values featuring the comparison between the recorded and the forecasted low stream flow rate data.

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Investigation of the Efficiency of Small-Scale NF/RO Seawater Desalination by Using Artificial Neural Network Modeling

International Journal of Design & Nature and Ecodynamics ◽

10.18280/ijdne.160307 ◽

2021 ◽

Vol 16 (3) ◽

pp. 239-299

Author(s):

Asma Adda ◽

Salah Bezari ◽

Mohamed Salmi ◽

Giulio Lorenzini ◽

Maamar Laidi ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Flow Rate ◽

Coefficient Of Determination ◽

Neural Network Modeling ◽

Small Scale ◽

Water Recovery ◽

Seawater Desalination ◽

Feed Forward Back Propagation ◽

Artificial Neural

An attempt is conducted in this paper to develop an artificial neural network (ANN) model for predicting the efficiency of small-scale NF/RO seawater desalination, then applied to the simulation of permeate flow rate and water recovery. A feed-forward back-propagation neural network with the Levenberg-Marquardt learning algorithm is considered. The performance of ANN compared to the multiple linear regression (MLR) is based on the calculated value of the coefficient of determination (R2). For ANN, R2 permeate flow rate was 0.997, and R2 permeate water recovery was 0.999, and for MLR, R2 permeate flow rate was 0.508, and R2 permeate water recovery was 0.713. It was observed that ANN performed better than the MLR.

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Cluster Forecasting of Corruption Using Nonlinear Autoregressive Models with Exogenous Variables (NARX)—An Artificial Neural Network Analysis

Sustainability ◽

10.3390/su132011366 ◽

2021 ◽

Vol 13 (20) ◽

pp. 11366

Author(s):

SeyedAli Ghahari ◽

Cesar Queiroz ◽

Samuel Labi ◽

Sue McNeil

Keyword(s):

Neural Network ◽

Time Series ◽

Artificial Neural Network ◽

Global Perspective ◽

Fine Tuning ◽

Neural Network Modeling ◽

Monolithic Group ◽

Artificial Neural ◽

Mean Square Errors ◽

Nonlinear Autoregressive

Any effort to combat corruption can benefit from an examination of past and projected worldwide trends. In this paper, we forecast the level of corruption in countries by integrating artificial neural network modeling and time series analysis. The data were obtained from 113 countries from 2007 to 2017. The study is carried out at two levels: (a) the global level, where all countries are considered as a monolithic group; and (b) the cluster level, where countries are placed into groups based on their development-related attributes. For each cluster, we use the findings from our previous study on the cluster analysis of global corruption using machine learning methods that identified the four most influential corruption factors, and we use those as independent variables. Then, using the identified influential factors, we forecast the level of corruption in each cluster using nonlinear autoregressive recurrent neural network models with exogenous inputs (NARX), an artificial neural network technique. The NARX models were developed for each cluster, with an objective function in terms of the Corruption Perceptions Index (CPI). For each model, the optimal neural network is determined by fine-tuning the hyperparameters. The analysis was repeated for all countries as a single group. The accuracy of the models is assessed by comparing the mean square errors (MSEs) of the time series models. The results suggest that the NARX artificial neural network technique yields reliable future values of CPI globally or for each cluster of countries. This can assist policymakers and organizations in assessing the expected efficacies of their current or future corruption control policies from a global perspective as well as for groups of countries.

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Cluster Forecasting of Corruption Using Nonlinear Autoregressive with Exogenous Variables (NARX) – An Artificial Neural Network Analysis

10.20944/preprints202108.0247.v1 ◽

2021 ◽

Author(s):

SeyedAli Ghahari ◽

Cesar Quieroz ◽

Samuel Labi ◽

Sue McNeil

Keyword(s):

Neural Network ◽

Time Series ◽

Artificial Neural Network ◽

Influential Factors ◽

Global Perspective ◽

Neural Network Modeling ◽

Monolithic Group ◽

Artificial Neural ◽

Mean Square Errors ◽

Nonlinear Autoregressive

Any effort to combat corruption can benefit from an examination of past and projected worldwide trends. In this paper, we forecast the level of corruption in countries by integrating an artificial neural network modeling and time series analysis. The data were obtained from 113 countries from 2007 to 2017. The study is carried out at two levels: (a) global level where all countries are considered as a monolithic group; and (b) cluster level, where countries are placed into groups based on their development-related attributes. For each cluster, we use the findings from our previous study on the cluster analysis of global corruption using machine learning methods that identified the four most influential corruption factors, and we use those as independent variables. Then, using the identified influential factors, we forecast the level of corruption in each cluster using a nonlinear autoregressive recurrent neural network with exogenous inputs (NARX), an artificial neural network technique. The NARX models were developed for each cluster, with the objective function in terms of the Corruption Perceptions Index (CPI). For each model, the optimal neural network is determined by finetuning the hyperparameters. The analysis was repeated for all countries as a single group. The accuracy of the models is assessed by comparing the mean square errors (MSE) of the time series models. The results suggest that the NARX artificial neural network technique yields reliable future values of CPI globally or for each cluster of countries. This can assist policymakers and organizations in assessing the expected efficacies of their current or future corruption control policies from a global perspective as well as for groups of countries.

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