scholarly journals Modelling the effects of meteorological parameters on water temperature using artificial neural networks

2018 ◽  
Vol 77 (6) ◽  
pp. 1724-1733 ◽  
Author(s):  
Merve Temizyurek ◽  
Filiz Dadaser-Celik
Keyword(s):  
Water Temperature ◽  
Meteorological Parameters ◽  
Meteorological Data ◽  
Activation Function ◽  
Quality Parameter ◽  
Gradient Algorithm ◽  
Water Quality Parameter ◽  
Ann Model ◽  
Water Temperature Data ◽  
Artificial Neural

Abstract Water temperature affects all biological and chemical processes in water; therefore, it is an extremely important water quality parameter. Meteorological factors are among the most important factors that affect water temperatures. The aim of this study is to develop an artificial neural network (ANN) model to investigate the effects of meteorological parameters on water temperatures at Kızılırmak River in Turkey. Water temperature data were collected from gauging stations on Kızılırmak River, and meteorological data were acquired from the nearest meteorological stations. Air temperature, wind speed, relative humidity, and previous water temperatures were formed the input parameters. The model output included water temperatures. All data were available for the 1995–2007 period, with occasional missing records. The activation functions of the ANN model and the number of neurons in the hidden layer were selected by trial-and-error method to find the best results. The root mean square error and the correlation coefficient between observed and simulated water temperatures were used to assess the model success. The best results were obtained by using sigmoid activation function and scaled conjugate gradient algorithm. This study showed that meteorological data can be used to simulate water temperature with ANN model for Kızılırmak River.

scholarly journals Application of Artificial Neural Network for Forecasting Nitrate Concentration as a Water Quality Parameter: A Case Study of Feitsui Reservoir, Taiwan

2020 ◽  
Vol 15 (5) ◽  
pp. 647-652
Author(s):  
Sarmad Dashti Latif ◽  
Muhammad Shukri Bin Nor Azmi ◽  
Ali Najah Ahmed ◽  
Chow Ming Fai ◽  
Ahmed El-Shafie
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Quality Parameters ◽  
Quality Parameter ◽  
Water Quality Parameters ◽  
Water Quality Parameter ◽  
Ann Model ◽  
Artificial Neural ◽  
Feitsui Reservoir

Water resources play a vital role in various economies such as agriculture, forestry, cattle farming, hydropower generation, fisheries, industrial activity, and other creative activities, as well as the need for drinking water. Monitoring the water quality parameters in rivers is becoming increasingly relevant as freshwater is increasingly being used. In this study, the artificial neural network (ANN) model was developed and applied to predict nitrate (NO3) as a water quality parameter (WQP) in the Feitsui reservoir, Taiwan. For the input of the model, five water quality parameters were monitored and used namely, ammonium (NH3), nitrogen dioxide (NO2), dissolved oxygen (DO), nitrate (NO3) and phosphate (PO4) as input parameters. As a statistical measurement, the correlation coefficient (R) is used to evaluate the performance of the model. The result shows that ANN is an accurate model for predicting nitrate as a water quality parameter in the Feitsui reservoir. The regression value for the training, testing, validation, and overall are 0.92, 0.93, 0.99, and 0.94, respectively.

scholarly journals Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 626
Author(s):  
Svajone Bekesiene ◽  
Rasa Smaliukiene ◽  
Ramute Vaicaitiene
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Back Propagation ◽  
Armed Forces ◽  
Activation Function ◽  
Optimal Number ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Classification Rate ◽  
Artificial Neural

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.

scholarly journals Experimental and artificial neural network approach for forecasting of traffic air pollution in urban areas: The case of Subotica

2010 ◽  
Vol 14 (suppl.) ◽  
pp. 79-87 ◽  
Author(s):  
Bogdana Vujic ◽  
Srdjan Vukmirovic ◽  
Goran Vujic ◽  
Nebojsa Jovicic ◽  
Gordana Jovicic ◽  
...  
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Meteorological Data ◽  
Model Development ◽  
Ambient Air ◽  
Ann Model ◽  
Statistical Parameters ◽  
Neural Network Approach ◽  
Starting Point ◽  
Artificial Neural

In the recent years, artificial neural networks (ANNs) have been used to predict the concentrations of various gaseous pollutants in ambient air, mainly to forecast mean daily particle concentrations. The data on traffic air pollution, irrespective of whether they are obtained by measuring or modelling, represent an important starting point for planning effective measures to improve air quality in urban areas. The aim of this study was to develop a mathematical model for predicting daily concentrations of air pollution caused by the traffic in urban areas. For the model development, experimental data have been collected for 10 months, covering all four seasons. The data about hourly concentration levels of suspended particles with aerodynamic diameter less than 10 ?m (PM10) and meteorological data (temperature, air humidity, speed and direction of wind), measured at the measuring station in the town of Subotica from June 2008 to March 2009, served as the basis for developing an ANN-based model for forecasting mean daily concentrations of PM10. The quality of the ANN model was assessed on the basis of the statistical parameters, such as RMSE, MAE, MAPE, and r.

Power Forecasting from Solar Panels Using Artificial Neural Network in UTHM Parit Raja

2021 ◽  
Vol 02 (01) ◽  
Author(s):  
Natasha Munirah Mohd Fahmi ◽  
◽  
Nor Aira Zambri ◽  
Norhafiz Salim ◽  
Sim Sy Yi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Climatic Factors ◽  
Activation Function ◽  
Solar Panels ◽  
Ann Model ◽  
Power Characteristics ◽  
Step Procedure ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper presents a step-by-step procedure for the simulation of photovoltaic modules with numerical values, using MALTAB/Simulink software. The proposed model is developed based on the mathematical model of PV module, which based on PV solar cell employing one-diode equivalent circuit. The output current and power characteristics curves highly depend on some climatic factors such as radiation and temperature, are obtained by simulation of the selected module. The collected data are used in developing Artificial Neural Network (ANN) model. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) are the techniques used to forecast the outputs of the PV. Various types of activation function will be applied such as Linear, Logistic Sigmoid, Hyperbolic Tangent Sigmoid and Gaussian. The simulation results show that the Logistic Sigmoid is the best technique which produce minimal root mean square error for the system.

scholarly journals Modeling of hourly river water temperatures using artificial neural networks

2014 ◽  
Vol 49 (2) ◽  
pp. 144-162 ◽  
Author(s):  
Cindie Hebert ◽  
Daniel Caissie ◽  
Mysore G. Satish ◽  
Nassir El-Jabi
Keyword(s):  
Water Temperature ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Air Temperatures ◽  
Level Data ◽  
Water Level Data ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Many

Water temperature is an important component for water quality and biotic conditions in rivers. A good knowledge of river thermal regime is critical for the management of aquatic resources and environmental impact studies. The objective of the present study was to develop a water temperature model as a function of air temperatures, water temperatures and water level data using artificial neural network (ANN) techniques for two thermally different streams. This model was applied on an hourly basis. The results showed that ANN models are an effective modeling tool with overall root-mean-square-error of 0.94 and 1.23 °C, coefficient of determination (R2) of 0.967 and 0.962 and bias of −0.13 and 0.02 °C, for Catamaran Brook and the Little Southwest Miramichi River, respectively. The ANN model performed best in summer and autumn and showed a poorer performance in spring. Results of the present study showed similar or better results to those of deterministic and stochastic models. The present study shows that the predicted hourly water temperatures can also be used to estimate the mean and maximum daily water temperatures. The many advantages of ANN models are their simplicity, low data requirements, their capability of modeling long-term time series as well as having an overall good performance.

scholarly journals Dengue Outbreak Prediction Based on Artificial Neural Networking Model Using Climatic Parameters

2020 ◽  
Author(s):  
Biplab Ghosh ◽  
Monika Soni
Keyword(s):  
Dengue Fever ◽  
Meteorological Parameters ◽  
Ann Model ◽  
Data Set ◽  
Long Run ◽  
Modelling Techniques ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
And Control ◽  
The Relationship

Abstract Background: Dengue fever is a vector-borne tropical disease radically amplified by 30 times in occurrence between 1960 and 2010. The upsurge is considered to be because of urbanization, population growth and climate change. Therefore, Meteorological parameters (temperature, precipitation and relative humidity) have impact on the occurrence and outbreaks of dengue fever. There are not many studies that enumerate the relationship between the dengue cases in a particular locality and the meteorological parameters. This study explores the relationship between the dengue cases and the meteorological parameters. In prevalent localities, it is essential to alleviate the outbreaks using modelling techniques for better disease control.Methods: An artificial neural network (ANN) model was developed for predicting the number of dengue cases by knowing the meteorological parameters. The model was trained with 7 years of dengue fever data of Kamrup and Lakhimpur district of Assam, India. The practicality of the model was corroborated using independent data set with satisfactory outcomes. Findings: It was apparent from the sensitivity analysis that precipitation is more sensitive to the number of dengue cases than other meteorological parameters. Conclusion: This model would assist dengue fever alleviation and control in the long run.

scholarly journals Neural networks in analysing 137Cs behaviour in the air in the Belgrade area

2014 ◽  
Vol 29 (3) ◽  
pp. 226-232
Author(s):  
Aleksandra Samolov ◽  
Snezana Dragovic ◽  
Marko Dakovic ◽  
Goran Bacic
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Meteorological Parameters ◽  
Gamma Ray ◽  
Specific Activity ◽  
Meteorological Data ◽  
Principal Component ◽  
Spectrometric Analysis ◽  
Specific Activities ◽  
Artificial Neural

The application of the principal component analysis and artificial neural network method in forecasting 137Cs behaviour in the air as the function of meteorological parameters is presented. The model was optimized and tested using 137Cs specific activities obtained by standard gamma-ray spectrometric analysis of air samples collected in Belgrade (Serbia) during 2009-2011 and meteorological data for the same period. Low correlation (r = 0.20) between experimental values of 137Cs specific activities and those predicted by artificial neural network was obtained. This suggests that artificial neural network in the case of prediction of 137Cs specific activity, using temperature, insolation, and global Sun warming does not perform well, which can be explained by the relative independence of 137Cs specific activity of particular meteorological parameters and not by the ineffectiveness of artificial neural network in relating these parameters in general.

scholarly journals Urban Ozone Concentration Forecasting with Artificial Neural Network in Corsica

2014 ◽  
Vol 10 (1) ◽  
pp. 29-37
Author(s):  
Wani Tamas ◽  
Gilles Notton ◽  
Christophe Paoli ◽  
Cyril Voyant ◽  
Marie-Laure Nivet ◽  
...  
Keyword(s):  
Air Quality ◽  
Ozone Concentration ◽  
Meteorological Data ◽  
Atmospheric Pollutants ◽  
Saharan Dust ◽  
Ann Model ◽  
Deterministic Models ◽  
Artificial Neural ◽  
Dust Events ◽  
Short Term Prediction

Abstract Atmospheric pollutants concentration forecasting is an important issue in air quality monitoring. Qualitair Corse, the organization responsible for monitoring air quality in Corsica (France), needs to develop a short-term prediction model to lead its mission of information towards the public. Various deterministic models exist for local forecasting, but need important computing resources, a good knowledge of atmospheric processes and can be inaccurate because of local climatical or geographical particularities, as observed in Corsica, a mountainous island located in the Mediterranean Sea. As a result, we focus in this study on statistical models, and particularly Artificial Neural Networks (ANNs) that have shown good results in the prediction of ozone concentration one hour ahead with data measured locally. The purpose of this study is to build a predictor realizing predictions of ozone 24 hours ahead in Corsica in order to be able to anticipate pollution peaks formation and to take appropriate preventive measures. Specific meteorological conditions are known to lead to particular pollution event in Corsica (e.g. Saharan dust events). Therefore, an ANN model will be used with pollutant and meteorological data for operational forecasting. Index of agreement of this model was calculated with a one year test dataset and reached 0.88.

scholarly journals KUALITAS AIR PADA KOLAM LOBSTER AIR TAWAR (Cherax quadricarinatus) DI BBAT TATELU

2011 ◽  
Vol 7 (3) ◽  
pp. 128
Author(s):  
Sipriana S. Tumembouw
Keyword(s):  
Water Quality ◽  
Water Temperature ◽  
Pond Water ◽  
Quality Standard ◽  
Quality Parameter ◽  
Water Quality Parameter ◽  
Freshwater Aquaculture ◽  
Carbon Dioxide Co2 ◽  
National Water ◽  
Culture Site

ABSTRACTThe study was done at the culture site and in the laboratory. The former covered water temperature and pH in the spawning, nursery and rearing ponds, in the morning, 06:00-07:00, at noon,12:00-13.00, and in the afternoon 17:00 to 18:00. Measurements were taken for 2 weeks. The latter included Dissolved Oxygen (DO), Ammonia, Nitrite, Carbon Dioxide (CO2) and turbidity. Water samples were analyzed in the Laboratory of Freshwater Aquaculture Center (BBAT) Tatelu, North Minahasa. Results showed that water temperature, pH, DO, CO2, turbidity, ammonia and nitrite were in the suitable range for the freshwater lobster culture, except that DO, CO2, turbidity, ammonia in the spawning and rearing ponds on March 14, 2011 were either lower or higher than the National Water Quality Standard for aquaculture.Keywords: Water Quality Parameter, Laboratory, Pond, Water Quality Standard.ABSTRAKPenelitian dilakukan di tempat budidaya lobster dan di laboratorium. Pengukur-an lapangan meliputi suhu air dan pH di kolam pemijahan, pendederan, dan pembesar-an, pada pagi hari (06:00-07:00), siang hari (12:00-13.00), dan sore hari (17:00-18:00). Pengukuran dilakukan selama dua minggu. Pengukuran laboratorium meliputi Oksigen terlarut (DO), Amonia, Nitrit, Karbondioksida (CO2) and kekeruhan. Sampel air dianali-sa di laboratorium Balai Budidaya Air Tawar, (BBAT) Tatelu, Minahasa Utara. Hasil menunjukkan bahwa suhu air, pH, DO, CO2, kekeruhan, amonia dan nitrit berada da-lam kisaran yang sesuai untuk budidaya lobster air tawar, kecuali bahwa DO, CO2, ke-keruhan, amonia di kolam pemijahan dan pembesaran pada 14 Maret 2011 berada le-bih rendah maupun lebih tinggi daripada standar bakumutu air nasional untuk budidaya.Kata kunci: Parameter kualitas air, laboratorium, air kolam, bakumutu.

scholarly journals Eutrophication forecasting and management by artificial neural network: a case study at Yuqiao Reservoir in North China

2015 ◽  
Vol 17 (4) ◽  
pp. 679-695 ◽  
Author(s):  
Ya Zhang ◽  
Jinhui Jeanne Huang ◽  
Liang Chen ◽  
Lan Qi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Temperature ◽  
Chlorophyll A ◽  
Total Phosphorus ◽  
Ann Model ◽  
Validation Data ◽  
Yuqiao Reservoir ◽  
Artificial Neural ◽  
Sound Management

Yuqiao Reservoir is the potable water supply source for a city with a population of more than 14 million. Eutrophication has threatened the reliability of drinking water supplies and, therefore, the forecasting systems for eutrophication and sound management become urgent needs. Water temperature and total phosphorus have long been considered as the major influencing factors to eutrophication. This study used the artificial neural network (ANN) model to forecast three water quality variables including water temperature, total phosphorus, and chlorophyll-a in Yuqiao Reservoir. Two weeks in advance for forecasting was chosen to ensure a sufficient preparation response time for algae outbreak. The Nash–Sutcliffe coefficient of efficiency (R2) was between 0.84 and 0.99 for the training and over-fitting test data sets, while it was between 0.59 and 0.99 for the validation data set. To better respond to the algae outbreak, a number of management scenarios formed by orthogonal experimental design were modeled to assess the responses of chlorophyll-a and an optimal management scenario was identified, which can reduce chlorophyll-a by 23.8%. This study demonstrates that ANN model is potentially useful for forecasting eutrophication up to 2 weeks in advance. It also provides valuable information for the sound management of nutrient loads to reservoirs.

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