scholarly journals Prediction on the Seasonal Behavior of Hydrogen Sulfide Using a Neural Network Model

2011 ◽  
Vol 11 ◽  
pp. 992-1004 ◽  
Author(s):  
Byungwhan Kim ◽  
Joogong Lee ◽  
Jungyoung Jang ◽  
Dongil Han ◽  
Ki-Hyun Kim
Keyword(s):  
Neural Networks ◽  
Hydrogen Sulfide ◽  
Wind Speed ◽  
Meteorological Parameters ◽  
Dew Point ◽  
Industrial Complex ◽  
Seasonal Behavior ◽  
Generalized Regression Neural Networks ◽  
Main Effect ◽  
Optimized Model

Models to predict seasonal hydrogen sulfide (H2S) concentrations were constructed using neural networks. To this end, two types of generalized regression neural networks and radial basis function networks are considered and optimized. The input data for H2S were collected from August 2005 to Fall 2006 from a huge industrial complex located in Ansan City, Korea. Three types of seasonal groupings were prepared and one optimized model is built for each dataset. These optimized models were then used for the analysis of the sensitivity and main effect of the parameters. H2S was noted to be very sensitive to rainfall during the spring and summer. In the autumn, its sensitivity showed a strong dependency on wind speed and pressure. Pressure was identified as the most influential parameter during the spring and summer. In the autumn, relative humidity overwhelmingly affected H2S. It was noted that H2S maintained an inverse relationship with a number of parameters (e.g., radiation, wind speed, or dew-point temperature). In contrast, it exhibited a declining trend with a decrease in pressure. An increase in radiation was likely to decrease during spring and summer, but the opposite trend was predicted for the autumn. The overall results of this study thus suggest that the behavior of H2S can be accounted for by a diverse combination of meteorological parameters across seasons.

scholarly journals Identification allergen Cladosporium spores concentration in the ambient of Ahvaz wastewater treatment plant and their relationship with meteorological variables, Ahvaz, Iran

2020 ◽  
Author(s):  
Nastaran Talepour ◽  
Mohammad Sadegh Hassanvand ◽  
Effat Abbasi-Montazeri ◽  
Seyed Mahmoud Latifi ◽  
Neamat Jaafarzadeh Haghighi Fard
Keyword(s):  
Wastewater Treatment ◽  
Wind Speed ◽  
Meteorological Parameters ◽  
Treatment Plant ◽  
Allergic Diseases ◽  
Airborne Fungi ◽  
Air Pressure ◽  
Dew Point ◽  
Uv Index ◽  
Ultraviolet Index

Introduction: Airborne Cladosporium spores in different regions of the world are known as the main cause of allergic diseases. This study aimed to identify the Cladosporium species airborne fungi in Ahvaz wastewater treat- ment plant area and its adjacent places and check the effect of some meteoro- logical parameters on their emissions. Materials and methods: Cladosporium spores were cultured on Sabouraud`s dextrose agar (SDA) medium in both cold and warm seasons. The passive sampling method was performed and after incubation, colonies were counted as CFU/Plate/h. Then, according to the macroscopic and microscopic charac- teristics of the genus, the fungal was studied. The meteorological parameters including temperature, humidity, air pressure, dew point, wind speed, and ultraviolet index were measured. Results: At least, 3358 colonies were counted. 1433 colonies were related  to the Cladosporium species. The amount of Cladosporium in indoor air was 46% of the total Cladosporium. The average of meteorological parameters includes temperature, humidity, air pressure, dew point, wind speed and UV index during the study were 27.8 °C, 32.9%, 548.7 °Kpa, 3.6°, 9.1 km / h and 3.9 respectively. 42.6% of the total number of colonies was related to the Cladosporium species. Cladospiromes had a direct correlation with the dew point, temperature, humidity, air pressure, wind speed, and ultraviolet index (Pvalue<0.05). Primary sludge dewatering has the greatest role in the Cladospo- rium spores emission. Conclusion: Considering the importance of Cladosporium spores in the ap- pearance of allergic diseases, and given that wastewater treatment workers spend most of their time outside, observing health and preventive measures is necessary in this regard.

An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

2017 ◽  
Vol 7 (9) ◽  
pp. 13
Author(s):  
Bhargavi Munnaluri ◽  
K. Ganesh Reddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Support Vector ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed.  In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

scholarly journals Predicting culturable enterococci exceedances at Escambron Beach, San Juan, Puerto Rico using satellite remote sensing and artificial neural networks

2018 ◽  
Vol 17 (1) ◽  
pp. 137-148
Author(s):  
Abdiel E. Laureano-Rosario ◽  
Andrew P. Duncan ◽  
Erin M. Symonds ◽  
Dragan A. Savic ◽  
Frank E. Muller-Karger
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Puerto Rico ◽  
Artificial Neural Networks ◽  
Dew Point ◽  
San Juan ◽  
Recreational Water ◽  
Recreational Water Quality ◽  
Artificial Neural ◽  
The U.S

Abstract Predicting recreational water quality is key to protecting public health from exposure to wastewater-associated pathogens. It is not feasible to monitor recreational waters for all pathogens; therefore, monitoring programs use fecal indicator bacteria (FIB), such as enterococci, to identify wastewater pollution. Artificial neural networks (ANNs) were used to predict when culturable enterococci concentrations exceeded the U.S. Environmental Protection Agency (U.S. EPA) Recreational Water Quality Criteria (RWQC) at Escambron Beach, San Juan, Puerto Rico. Ten years of culturable enterococci data were analyzed together with satellite-derived sea surface temperature (SST), direct normal irradiance (DNI), turbidity, and dew point, along with local observations of precipitation and mean sea level (MSL). The factors identified as the most relevant for enterococci exceedance predictions based on the U.S. EPA RWQC were DNI, turbidity, cumulative 48 h precipitation, MSL, and SST; they predicted culturable enterococci exceedances with an accuracy of 75% and power greater than 60% based on the Receiving Operating Characteristic curve and F-Measure metrics. Results show the applicability of satellite-derived data and ANNs to predict recreational water quality at Escambron Beach. Future work should incorporate local sanitary survey data to predict risky recreational water conditions and protect human health.

scholarly journals Understanding the drivers of marine liquid-water cloud occurrence and properties with global observations using neural networks

2017 ◽  
Author(s):  
Hendrik Andersen ◽  
Jan Cermak ◽  
Julia Fuchs ◽  
Reto Knutti ◽  
Ulrike Lohmann
Keyword(s):  
Neural Networks ◽  
Liquid Water ◽  
Meteorological Parameters ◽  
Reanalysis Data ◽  
Main Determinant ◽  
Boundary Layer Height ◽  
Water Cloud ◽  
Aerosol Cloud Interactions ◽  
Water Amount

Abstract. The role of aerosols, clouds and their interactions with radiation remain among the largest unknowns in the climate system. Even though the processes involved are complex, aerosol-cloud interactions are often analyzed by means of bivariate relationships. In this study, 15 years (2001–2015) of monthly satellite-retrieved nearly-global aerosol products are combined with reanalysis data of various meteorological parameters to predict satellite-derived marine liquid-water cloud occurrence and properties by means of regionally-specific artificial neural networks. The statistical models used are shown to be capable of predicting clouds, especially in regions of high cloud variability. At this monthly scale, lower tropospheric stability is shown to be the main determinant of cloud fraction and droplet size, especially in stratocumulus regions, while boundary layer height controls the liquid-water amount and thus the optical thickness of clouds. While aerosols show the expected impact on clouds, at this scale they are less relevant than some meteorological factors. Global patterns of the derived sensitivities point to regional characteristics of aerosol and cloud processes.

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