scholarly journals Employing Artificial Neural Networks to Predict the Performance of Domestic Sewage Treatment Terminals in the Rural Region

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Qiang Lin ◽  
Ancheng Luo ◽  
Yan Zhang ◽  
Yunlong Wang ◽  
Zhiwei Liang ◽  
...  
Keyword(s):  
Prediction Models ◽  
Domestic Sewage ◽  
Practical Significance ◽  
Small Scale ◽  
High Dispersion ◽  
Local Optima ◽  
Training Stage ◽  
Ann Models ◽  
Artificial Neural ◽  
Validation Stage

Domestic sewage in rural regions is mainly treated by small-scale treatment terminals in China. The large quantities and high dispersion of these terminals render the chemical measurement of effluent to be a time and energy intensive work and further hinder the efficient surveillance of terminals’ performance. After a thorough investigation of 136 operating terminals, this study successfully employs two artificial neural network (ANN) models to predict effluent total nitrogen (TN) and COD (R2 both higher than 0.8) by setting some easily detectable parameters, e.g., pH and conductivity, as inputs. To prevent ANN models getting stuck on local optima and enhance the model performance, genetic algorithm (GA) and particle swarm optimization (PSO) are introduced into ANN, respectively. By comparison, ANN-PSO excels in modelling both TN and COD. The root mean square error (RMSE) and R2 of ANN-PSO in modelling TN are 9.14 and 0.90, respectively, in the training stage, and 11.54 and 0.90, respectively, in the validation stage. The RMSE and R2 of ANN-PSO in modelling COD are 22.10 and 0.90, respectively, in the training stage, and 26.57 and 0.85, respectively, in the validation stage. This is the first study to provide performance prediction models that are available for different terminals. Two established ANN-PSO models show great practical significance in monitoring huge amounts of terminals despite the slight sacrifice of models’ accuracy caused by the great heterogeneity of different terminals.

scholarly journals Application of artificial neural networks to predict the COVID-19 outbreak

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Hamid Reza Niazkar ◽  
Majid Niazkar
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Incubation Period ◽  
Prediction Models ◽  
Time Interval ◽  
Policy Makers ◽  
Ann Models ◽  
Artificial Neural ◽  
Relative Errors ◽  
Mean Square Errors

Abstract Background Millions of people have been infected worldwide in the COVID-19 pandemic. In this study, we aim to propose fourteen prediction models based on artificial neural networks (ANN) to predict the COVID-19 outbreak for policy makers. Methods The ANN-based models were utilized to estimate the confirmed cases of COVID-19 in China, Japan, Singapore, Iran, Italy, South Africa and United States of America. These models exploit historical records of confirmed cases, while their main difference is the number of days that they assume to have impact on the estimation process. The COVID-19 data were divided into a train part and a test part. The former was used to train the ANN models, while the latter was utilized to compare the purposes. The data analysis shows not only significant fluctuations in the daily confirmed cases but also different ranges of total confirmed cases observed in the time interval considered. Results Based on the obtained results, the ANN-based model that takes into account the previous 14 days outperforms the other ones. This comparison reveals the importance of considering the maximum incubation period in predicting the COVID-19 outbreak. Comparing the ranges of determination coefficients indicates that the estimated results for Italy are the best one. Moreover, the predicted results for Iran achieved the ranges of [0.09, 0.15] and [0.21, 0.36] for the mean absolute relative errors and normalized root mean square errors, respectively, which were the best ranges obtained for these criteria among different countries. Conclusion Based on the achieved results, the ANN-based model that takes into account the previous fourteen days for prediction is suggested to predict daily confirmed cases, particularly in countries that have experienced the first peak of the COVID-19 outbreak. This study has not only proved the applicability of ANN-based model for prediction of the COVID-19 outbreak, but also showed that considering incubation period of SARS-COV-2 in prediction models may generate more accurate estimations.

Prediction of stress responses in goats: comparison of artificial neural network and multiple regression models

2020 ◽  
Vol 100 (1) ◽  
pp. 102-110 ◽  
Author(s):  
G. Kannan ◽  
R. Gosukonda ◽  
A.K. Mahapatra
Keyword(s):  
Stress Responses ◽  
Regression Models ◽  
Prediction Models ◽  
Physiological Stress ◽  
Stocking Density ◽  
Statistical Regression ◽  
Nonparametric Models ◽  
Learning Rules ◽  
Ann Models ◽  
Artificial Neural

This study was conducted to determine if artificial neural networks (ANN) can be used to more accurately predict physiological stress responses in goats compared with statistical regression. Prediction models were developed for plasma cortisol and glucose concentrations, creatine kinase (CK) activity, neutrophil (N) and lymphocyte (L) counts, and N:L ratio as a function of time (0, 1, 2, 3, and 4 h; n = 16 goats per time) after a 2.5 h transportation (input 1) and stocking density (25 vs. 50 goats; input 2). However, input 2 was not included in the final models because density did not have a significant effect. The NeuralWorks Predict® software and SAS were used to develop ANN and regression models, respectively. Backpropagation (BP) and Kalman filter (KF) learning rules were used to develop nonparametric models. Correlations between predicted and observed values were better with ANN-BP (R values = 0.87, 0.67, 0.56, 0.27, 0.42, and 0.53) and ANN-KF (R values = 0.84, 0.67, 0.58, 0.27, 0.42, and 0.50) models for cortisol, glucose, CK, N, L, and N:L ratio, respectively, than with regression models (R values =0.85, 0.52, 0.27, 0.13, 0.31, and 0.12). The results showed that the ANN models can predict responses more robustly compared with statistical regression.

scholarly journals Effects of input/output parameters on artificial neural network model efficiency for breakthrough contaminant prediction

2021 ◽  
Author(s):  
Jayashree Pal ◽  
Dibakar Chakrabarty
Keyword(s):  
Temporal Variation ◽  
Prediction Models ◽  
Numerical Models ◽  
Spatial And Temporal Variation ◽  
Input Output ◽  
Model Efficiency ◽  
Groundwater Systems ◽  
Ann Models ◽  
Artificial Neural ◽  
Observation Wells

Abstract Groundwater quality assessment is characterized by pollution injection rates, pollution injection locations and duration of pollution injection for identifying spatial and temporal variation. In this study, the spatial variations are obtained by placing observation wells in the downstream zone. And, the temporal variation of contaminant concentration has been simulated during the study period. Generally, simulations are carried out using various numerical models, which are subject to the availability of all required input parameters and are necessary for the proper management of contaminated aquifers. In literature, artificial neural networks (ANNs) are prescribed in such situations as these modeling methods focuses on available input-output datasets, thus resolving the concern of obtaining all inputs that numerical simulator usually demands. Past researches have predicted groundwater breakthrough contaminants. But the effects of input-output variations need to be discussed. This study is to quantify the effects of a few input-output datasets in the performance of ANN models to simulate pollutant transport in groundwater systems. The combinations of input/output scenarios have rendered these ANN models sensitive to variations, thus affecting model efficiency. These outcomes can reliably be employed for contaminant estimation and provide a paradigm in data collection, which will help hydrogeologists develop more efficient prediction models.

Simultaneous UV-Vis spectrophotometric quantification of ternary basic dye mixtures by partial least squares and artificial neural networks

2016 ◽  
Vol 74 (10) ◽  
pp. 2497-2504 ◽  
Author(s):  
Seyed Karim Hassaninejad-Darzi ◽  
Mohammad Torkamanzadeh
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Prediction Models ◽  
Correlation Coefficients ◽  
Analyte Signal ◽  
Ann Models ◽  
Artificial Neural ◽  
Seawater Samples ◽  
Artificial Neural Network Ann

One of the main difficulties in quantification of dyes in industrial wastewaters is the fact that dyes are usually in complex mixtures rather than being pure. Here we report the development of two rapid and powerful methods, partial least squares (PLS-1) and artificial neural network (ANN), for spectral resolution of a highly overlapping ternary dye system in the presence of interferences. To this end, Crystal Violet (CV), Malachite Green (MG) and Methylene Blue (MB) were selected as three model dyes whose UV-Vis absorption spectra highly overlap each other. After calibration, both prediction models were validated through testing with an independent spectra-concentration dataset, in which high correlation coefficients (R2) of 0.998, 0.999 and 0.999 were obtained by PLS-1 and 0.997, 0.999 and 0.999 were obtained by ANN for CV, MG and MB, respectively. Having shown a relative error of prediction of less than 3% for all the dyes tested, both PLS-1 and ANN models were found to be highly accurate in simultaneous determination of dyes in pure aqueous samples. Using net-analyte signal concept, the quantitative determination of dyes spiked in seawater samples was carried out successfully by PLS-1 with satisfactory recoveries (90–101%).

scholarly journals Managing and Control of Nanofiltration / Reverse Osmosis Desalination System: Application of Artificial Neural Network

2020 ◽  
Vol 15 (6) ◽  
pp. 843-853
Author(s):  
Asma Adda ◽  
Salah Hanini ◽  
Salah Bezari ◽  
Houari Ameur ◽  
Rachid Maouedj
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reverse Osmosis ◽  
Statistical Tests ◽  
Small Scale ◽  
Feed Water ◽  
Temperature Conductivity ◽  
Ann Models ◽  
Artificial Neural ◽  
Reverse Osmosis Desalination

Some models of the artificial neural network (ANN) are introduced in the control system of a Nanofiltration / Reverse Osmosis desalination in order to manage the operation and to improve the overall efficiency. This study is carried out on a small-scale prototype of NF/RO seawater desalinate++on plant installed in Saudi Arabia and allowing it to operate with input power. The ANN models are developed to generate the permeate flow rate and recovery after taking into account the temperature, conductivity and pressure of the feed water and the available electrical power. The utilized ANN models after training proved their ability to control the operating of the unit with success. In addition, the statistical tests revealed minimum values of RMSE and MAE. A dimensioning of a photovoltaic system to power the plant is also carried out.

scholarly journals Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs)

2021 ◽  
Author(s):  
Stephen Arhin ◽  
Babin Manandhar ◽  
Hamdiat Baba Adam ◽  
Adam Gatiba
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Washington Dc ◽  
Travel Times ◽  
Ann Models ◽  
Artificial Neural ◽  
Bus Stops ◽  
Quasi Newton

Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) data for six Washington Metropolitan Area Transit Authority (WMATA) bus routes operating in Washington, DC. We developed regression models and Artificial Neural Network (ANN) models for predicting travel times of buses for different peak periods (AM, Mid-Day and PM). Our analysis included variables such as number of served bus stops, length of route between bus stops, average number of passengers in the bus, average dwell time of buses, and number of intersections between bus stops. We obtained ANN models for travel times by using approximation technique incorporating two separate algorithms: Quasi-Newton and Levenberg-Marquardt. The training strategy for neural network models involved feed forward and errorback processes that minimized the generated errors. We also evaluated the models with a Comparison of the Normalized Squared Errors (NSE). From the results, we observed that the travel times of buses and the dwell times at bus stops generally increased over time of the day. We gathered travel time equations for buses for the AM, Mid-Day and PM Peaks. The lowest NSE for the AM, Mid-Day and PM Peak periods corresponded to training processes using Quasi-Newton algorithm, which had 3, 2 and 5 perceptron layers, respectively. These prediction models could be adapted by transit agencies to provide the patrons with accurate travel time information at bus stops or online.

Bankruptcy Prediction Models: A Bet on Artificial Neural Networks

2012 ◽  
Vol 3 (2) ◽  
pp. 48-50
Author(s):  
Ana Isabel Velasco Fernández ◽  
◽  
Ricardo José Rejas Muslera ◽  
Juan Padilla Fernández-Vega ◽  
María Isabel Cepeda González
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Artificial Neural

Forecasting of Tornadoes and Downbursts

2020 ◽  
Author(s):  
Djordje Romanic
Keyword(s):  
Chaos Theory ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Weather Forecasts ◽  
Wind Speeds ◽  
The Past ◽  
Extreme Wind ◽  
Extreme Wind Speeds ◽  
Numerical Weather Prediction Models

Tornadoes and downbursts cause extreme wind speeds that often present a threat to human safety, structures, and the environment. While the accuracy of weather forecasts has increased manifold over the past several decades, the current numerical weather prediction models are still not capable of explicitly resolving tornadoes and small-scale downbursts in their operational applications. This chapter describes some of the physical (e.g., tornadogenesis and downburst formation), mathematical (e.g., chaos theory), and computational (e.g., grid resolution) challenges that meteorologists currently face in tornado and downburst forecasting.

Treatment of Rural Domestic Sewage by Solar Ozonic Ecological Sewage Treatment Plant

2014 ◽  
Vol 955-959 ◽  
pp. 3393-3399 ◽  
Author(s):  
Wei Zheng ◽  
Yan Ming Yang ◽  
Yun Long Li ◽  
Jian Qiu Zheng
Keyword(s):  
Municipal Wastewater ◽  
Operating Cost ◽  
Sewage Treatment ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Domestic Sewage ◽  
Design Parameters ◽  
Small Scale ◽  
Municipal Wastewater Treatment ◽  
Average Percentage

The process technique and design parameters of project of Solar Ozonic Ecological Sewage Treatment Plant (short for SOESTP) which consists of anaerobic reactor, horizontal subsurface flow (HSSF) constructed wetlands(CWs) and the combination of solar power and ozone disinfection are described, the paper further examines the removal efficiency for treating rural domestic sewage, running expense and recycling ability of product water. The results show that the average percentage removal values of CODcr，BOD5，SS，TN，NH3-N，TP range from 95.6% to 98.0%, 96.0% to 98.7%, 93.1% to 96.1%, 97.0% to 98.9%, 96.9% to 99.5%, 98.2% to 99.6%, respectively, the reduction of fecal coliform (FC) reaches 99.9%, the effluent quality meets the first level A criteria specified in Discharge Standard of Pollutants for Municipal Wastewater Treatment Plant（GB18918-2002）. The running cost of SOESTP is 0.063yuan/ m3, saves much more than traditional sewage treatment, and the ozone water obtained from the reservoir will be an ideal choice for disinfection .The system has characteristics of easy manipulation, low operating cost, achieving advanced water, energy conservation and environment protection, is thought to be very suitable for use as the promotion of rural small - scale sewage treatment.

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