Search results for: atmospheric pollutant prediction

Authors: Angeliki Peponi, Paulo Morgado, Jorge Trindade

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Artificial Neural Networks (ANNs) and Geographic Information Systems (GIS) are applied as a robust tool for modeling and forecasting the erosion changes in Costa Caparica, Lisbon, Portugal, for 2021. ANNs present noteworthy advantages compared with other methods used for prediction and decision making in urban coastal areas. Multilayer perceptron type of ANNs was used. Sensitivity analysis was conducted on natural and social forces and dynamic relations in the dune-beach system of the study area. Variations in network’s parameters were performed in order to select the optimum topology of the network. The developed methodology appears fitted to reality; however further steps would make it better suited.

Keywords: artificial neural networks, backpropagation, coastal urban zones, erosion prediction

Procedia PDF Downloads 365

Authors: Himali N. Balasooriya, Kithsiri B. Dassanayake, Saman Seneweera, Said Ajlouni

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Increase in atmospheric CO₂ concentration [CO₂] and ambient temperature associated with changing climatic conditions will have significant impacts on agriculture crop productivity and quality. Independent effects of the above two environmental variables on the growth, yield and quality of strawberry were well documented. Higher temperatures over the optimum range (20-25ºC) lead to crop failures, while elevated [CO₂] stimulated plant growth and yield but compromised the physical quality of fruits. However, there is very limited understanding of the interaction between these variables on the plant growth, yield and quality. Therefore, this study was designed to investigate the interactive effect of high temperature and elevated [CO₂] on growth, yield and quality of strawberries. Strawberry cultivars ‘Albion’ and ‘San Andreas’ were grown under six different combinations of two temperatures (25 and 30ºC) and three [CO₂] (400, 650 and 950 µmol mol^-1) in controlled-environmental growth chambers. Plant growth measurements such as plant height, canopy area, number of flowers, and fruit yield were measured during phonological development. Photosynthesis and transpiration, the ratio of intercellular to atmospheric [CO₂] (Ci/Ca) were measured to estimate the physiological adjustment to climate stress. The impact of temperature and [CO₂] interaction on growth and yield of strawberry was significant (p < 0.05). Across both cultivars, highest fruit yields were observed at 650 µmol mol^-1 [CO₂], which was particularly clear at 25°C. The fruit yield gradually decreased at 30°C under all the treatment combinations. However, photosynthesis rates were highest at 650 µmol mol^-1 [CO₂] but no increment was found at 900 µmol mol^-1 [CO₂]. Interestingly, Ci/Ca ratio increased with increasing atmospheric [CO₂] which was predominant at high temperature. Similarly, fruit yield was substantially reduced at high [CO₂] under high temperature. Our findings suggest that increased Ci/Ca ratio at high temperature is likely reduces the photosynthesis and thus yield response to elevated [CO₂].

Keywords: atmospheric CO₂ concentration, fruit yield, strawberry, temperature

Procedia PDF Downloads 207

Authors: I. Nitoi, P. Oancea, M. Raileanu, M. Crisan, L. Constantin, I. Cristea

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Hazardous organic compounds like nitroaromatics are frequently found in chemical and petroleum industries discharged effluents. Due to their bio-refractory character and high chemical stability cannot be efficiently removed by classical biological or physical-chemical treatment processes. In the past decades, semiconductor photocatalysis has been frequently applied for the advanced degradation of toxic pollutants. Among various semiconductors titania was a widely studied photocatalyst, due to its chemical inertness, low cost, photostability and nontoxicity. In order to improve optical absorption and photocatalytic activity of TiO2 many attempts have been made, one feasible approach consists of doping oxide semiconductor with metal. The degradation of dinitrobenzene (DNB) and dinitrotoluene (DNT) from aqueous solution under UVA-VIS irradiation using heavy metal (0.5% Fe, 1%Co, 1%Ni ) doped titania was investigated. The photodegradation experiments were carried out using a Heraeus laboratory scale UV-VIS reactor equipped with a medium-pressure mercury lamp which emits in the range: 320-500 nm. Solutions with (0.34-3.14) x 10-4 M pollutant content were photo-oxidized in the following working conditions: pH = 5-9; photocatalyst dose = 200 mg/L; irradiation time = 30 – 240 minutes. Prior to irradiation, the photocatalyst powder was added to the samples, and solutions were bubbled with air (50 L/hour), in the dark, for 30 min. Dopant type, pH, structure and initial pollutant concentration influence on the degradation efficiency were evaluated in order to set up the optimal working conditions which assure substrate advanced degradation. The kinetics of nitroaromatics degradation and organic nitrogen mineralization was assessed and pseudo-first order rate constants were calculated. Fe doped photocatalyst with lowest metal content (0.5 wt.%) showed a considerable better behaviour in respect to pollutant degradation than Co and Ni (1wt.%) doped titania catalysts. For the same working conditions, degradation efficiency was higher for DNT than DNB in accordance with their calculated adsobance constants (Kad), taking into account that degradation process occurs on catalyst surface following a Langmuir-Hinshalwood model. The presence of methyl group in the structure of DNT allows its degradation by oxidative and reductive pathways, while DNB is converted only by reductive route, which also explain the highest DNT degradation efficiency. For highest pollutant concentration tested (3 x 10-4 M), optimum working conditions (0.5 wt.% Fe doped –TiO2 loading of 200 mg/L, pH=7 and 240 min. irradiation time) assures advanced nitroaromatics degradation (ηDNB=89%, ηDNT=94%) and organic nitrogen mineralization (ηDNB=44%, ηDNT=47%).

Keywords: hazardous organic compounds, irradiation, nitroaromatics, photocatalysis

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Authors: Sumit Sen, Sohan Khedekar, Umang Shinde, Shivam Bhargava

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This study has been undertaken to investigate whether the deep learning models are able to predict the future stock prices by training the model with the historical stock price data. Since this work required time series analysis, various models are present today to perform time series analysis such as Recurrent Neural Network LSTM, ARIMA and Facebook Prophet. Applying these models the movement of stock price of stocks are predicted and also tried to provide the future prediction of the stock price of a stock. Final product will be a stock price prediction web application that is developed for providing the user the ease of analysis of the stocks and will also provide the predicted stock price for the next seven days.

Keywords: Autoregressive Integrated Moving Average, Deep Learning, Long Short Term Memory, Time-series

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Authors: Salahaeddine Sbai

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Air quality analysis using machine learning models is a method employed to assess and predict air pollution levels. This approach leverages the capabilities of machine learning algorithms to analyze vast amounts of air quality data and extract valuable insights. By training these models on historical air quality data, they can learn patterns and relationships between various factors such as weather conditions, pollutant emissions, and geographical features. The trained models can then be used to predict air quality levels in real-time or forecast future pollution levels. This application of machine learning in air quality analysis enables policymakers, environmental agencies, and the general public to make informed decisions regarding health, environmental impact, and mitigation strategies. By understanding the factors influencing air quality, interventions can be implemented to reduce pollution levels, mitigate health risks, and enhance overall air quality management. Climate change is having significant impacts on Morocco, affecting various aspects of the country's environment, economy, and society. In this study, we use some machine learning models under python environment to predict and analysis air quality change over North of Morocco to evaluate the climate change impact on agriculture.

Keywords: air quality, machine learning models, pollution, pollutant emissions

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Authors: Salman Mohamadi, Seyed Mohammad Ali Tayaranian Hosseini, Hamidreza Amindavar

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In this paper, we provide a procedure to analyze and model EEG (electroencephalogram) signal as a time series using ARIMA-GARCH to predict an epileptic attack. The heteroskedasticity of EEG signal is examined through the ARCH or GARCH, (Autore- gressive conditional heteroskedasticity, Generalized autoregressive conditional heteroskedasticity) test. The best ARIMA-GARCH model in AIC sense is utilized to measure the volatility of the EEG from epileptic canine subjects, to forecast the future values of EEG. ARIMA-only model can perform prediction, but the ARCH or GARCH model acting on the residuals of ARIMA attains a con- siderable improved forecast horizon. First, we estimate the best ARIMA model, then different orders of ARCH and GARCH modelings are surveyed to determine the best heteroskedastic model of the residuals of the mentioned ARIMA. Using the simulated conditional variance of selected ARCH or GARCH model, we suggest the procedure to predict the oncoming seizures. The results indicate that GARCH modeling determines the dynamic changes of variance well before the onset of seizure. It can be inferred that the prediction capability comes from the ability of the combined ARIMA-GARCH modeling to cover the heteroskedastic nature of EEG signal changes.

Keywords: epileptic seizure prediction , ARIMA, ARCH and GARCH modeling, heteroskedasticity, EEG

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Authors: Benedictus Asriparusa, Lathifah Al Hakimi, Aulia Husada

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A large amount of energy is being wasted by the release of natural gas associated with the oil industry. This release interrupts the environment particularly atmosphere layer condition globally which contributes to global warming impact. This research presents an overview of the methods employed by researchers in PT. Chevron Pacific Indonesia in the Minas area to determine a new prediction method of measuring and reducing gas flaring and its emission. The method emphasizes advanced research which involved analytical studies, numerical studies, modeling, and computer simulations, amongst other techniques. A flaring system is the controlled burning of natural gas in the course of routine oil and gas production operations. This burning occurs at the end of a flare stack or boom. The combustion process releases emissions of greenhouse gases such as NO2, CO2, SO2, etc. This condition will affect the chemical composition of air and environment around the boundary layer mainly during transition season. Transition season in Indonesia is absolutely very difficult condition to predict its pattern caused by the difference of two air mass conditions. This paper research focused on transition season in 2013. A simulation to create the new pattern of the pollutants distribution is needed. This paper has outlines trends in gas flaring modeling and current developments to predict the dominant variables in the pollutants distribution. A Fluent model is used to simulate the distribution of pollutants gas coming out of the stack, whereas WRF model output is used to overcome the limitations of the analysis of meteorological data and atmospheric conditions in the study area. Based on the running model, the most influence factor was wind speed. The goal of the simulation is to predict the new pattern based on the time of fastest wind and slowest wind occurs for pollutants distribution. According to the simulation results, it can be seen that the fastest wind (last of March) moves pollutants in a horizontal direction and the slowest wind (middle of May) moves pollutants vertically. Besides, the design of flare stack in compliance according to EPA Oil and Gas Facility Stack Parameters likely shows pollutants concentration remains on the under threshold NAAQS (National Ambient Air Quality Standards).

Keywords: flare motion, new prediction, pollutants distribution, transition season, WRF model

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Authors: Supriya, J. K. Basu, S. Sengupta

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Silver nanoparticles have caught a lot of attention because of its unique physical and chemical properties. Silver nanoparticles embedded in polyvinyl alcohol (PVA/Ag) free-standing film have been prepared by microwave irradiation in few minutes. PVA performed as a reducing agent, stabilizing agents as well as support for silver nanoparticles. UV-Vis spectrometry, scanning transmission electron (SEM) and transmission electron microscopy (TEM) techniques affirmed the reduction of silver ion to silver nanoparticles in the polymer matrix. Effect of irradiation time, the concentration of PVA and concentration of silver precursor on the synthesis of silver nanoparticle has been studied. Particles size of silver nanoparticles decreases with increase in irradiation time. Concentration of silver nanoparticles increases with increase in concentration of silver precursor. Good dispersion of silver nanoparticles in the film has been confirmed by TEM analysis. Particle size of silver nanoparticle has been found to be in the range of 2-10nm. Catalytic property of prepared silver nanoparticles as a heterogeneous catalyst has been studied in the reduction of p-Nitrophenol (a water pollutant) with >98% conversion. From the experimental results, it can be concluded that PVA encapsulated Ag nanoparticles film as a catalyst shows better efficiency and reusability in the reduction of p-Nitrophenol.

Keywords: biopolymer, microwave irradiation, silver nanoparticles, water pollutant

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Authors: Elison Eduardo Jardim Bierhals, Claudineia Brazil, Deivid Pires, Rafael Haag, Elton Gimenez Rossini

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This study evaluated the uncertainties involved in the solar radiation projections generated by the Brazilian Earth System Model (BESM) of the Weather and Climate Prediction Center (CPTEC) belonging to Coupled Model Intercomparison Phase 5 (CMIP5), with the aim of identifying efficiency in the projections for solar radiation of said model and in this way establish the viability of its use. Two different scenarios elaborated by Intergovernmental Panel on Climate Change (IPCC) were evaluated: RCP 4.5 (with more optimistic contour conditions) and 8.5 (with more pessimistic initial conditions). The method used to verify the accuracy of the present model was the Nash coefficient and the Statistical bias, as it better represents these atmospheric patterns. The BESM showed a tendency to overestimate the data ​​of solar radiation projections in most regions of the state of Rio Grande do Sul and through the validation methods adopted by this study, BESM did not present a satisfactory accuracy.

Keywords: climate changes, projections, solar radiation, uncertainty

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Authors: Kisan Sarda, Bhavika Shingote

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This paper aims to evaluate regression modelling for prediction of Energy storage of solar photovoltaic (PV) system using Semi parametric regression techniques because there are some parameters which are known while there are some unknown parameters like humidity, dust etc. Here irradiation of solar energy is different for different places on the basis of Latitudes, so by finding out areas which give more storage we can implement PV systems at those places and our need of energy will be fulfilled. This regression modelling is done for daily, monthly and seasonal prediction of solar energy storage. In this, we have used R modules for designing the algorithm. This algorithm will give the best comparative results than other regression models for the solar PV cell energy storage.

Keywords: semi parametric regression, photovoltaic (PV) system, regression modelling, irradiation

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Authors: Jingling Li, Yi Zhang, Wei Liang, Tao Cui, Jun Li

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Compared with terrestrial network, the traffic of spatial information network has both self-similarity and short correlation characteristics. By studying its traffic prediction method, the resource utilization of spatial information network can be improved, and the method can provide an important basis for traffic planning of a spatial information network. In this paper, considering the accuracy and complexity of the algorithm, the spatial information network traffic is decomposed into approximate component with long correlation and detail component with short correlation, and a time series hybrid prediction model based on wavelet decomposition is proposed to predict the spatial network traffic. Firstly, the original traffic data are decomposed to approximate components and detail components by using wavelet decomposition algorithm. According to the autocorrelation and partial correlation smearing and truncation characteristics of each component, the corresponding model (AR/MA/ARMA) of each detail component can be directly established, while the type of approximate component modeling can be established by ARIMA model after smoothing. Finally, the prediction results of the multiple models are fitted to obtain the prediction results of the original data. The method not only considers the self-similarity of a spatial information network, but also takes into account the short correlation caused by network burst information, which is verified by using the measured data of a certain back bone network released by the MAWI working group in 2018. Compared with the typical time series model, the predicted data of hybrid model is closer to the real traffic data and has a smaller relative root means square error, which is more suitable for a spatial information network.

Keywords: spatial information network, traffic prediction, wavelet decomposition, time series model

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Authors: Kuo-Chun Chien, Chia-Hui Chang, Ren-Der Sun

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Legal Judgment Prediction (LJP) is a subtask for legal AI. Its main purpose is to use the facts of a case to predict the judgment result. In Taiwan's criminal procedure, when prosecutors complete the investigation of the case, they will decide whether to prosecute the suspect and which article of criminal law should be used based on the facts and evidence of the case. In this study, we collected 305,240 indictments from the public inquiry system of the procuratorate of the Ministry of Justice, which included 169 charges and 317 articles from 21 laws. We take the crime facts in the indictments as the main input to jointly learn the prediction model for law source, article, and charge simultaneously based on the pre-trained Bert model. For single article cases where the frequency of the charge and article are greater than 50, the prediction performance of law sources, articles, and charges reach 97.66, 92.22, and 60.52 macro-f1, respectively. To understand the big performance gap between articles and charges, we used a bipartite graph to visualize the relationship between the articles and charges, and found that the reason for the poor prediction performance was actually due to the wording precision. Some charges use the simplest words, while others may include the perpetrator or the result to make the charges more specific. For example, Article 284 of the Criminal Law may be indicted as “negligent injury”, "negligent death”, "business injury", "driving business injury", or "non-driving business injury". As another example, Article 10 of the Drug Hazard Control Regulations can be charged as “Drug Control Regulations” or “Drug Hazard Control Regulations”. In order to solve the above problems and more accurately predict the article and charge, we plan to include the article content or charge names in the input, and use the sentence-pair classification method for question-answer problems in the BERT model to improve the performance. We will also consider a sequence-to-sequence approach to charge prediction.

Keywords: legal judgment prediction, deep learning, natural language processing, BERT, data visualization

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Authors: Raqiqa Tur Rasool, Ghulam Abbas Ashraf

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This study presents the synthesis and application of Carbon@NiCoFeS nanoparticles as a photocatalyst for the degradation of organic pollutants through peroxymonosulfate (PMS) activation. The Carbon@NiCoFeS nanoparticles, synthesized via a hydrothermal method, exhibit a highly crystalline and uniformly distributed nanostructure, as confirmed by XRD, SEM, TEM, and FTIR analyses. The photocatalytic performance was tested using ibuprofen (IBU) as a model pollutant under visible light, demonstrating remarkable efficiency across various conditions, including different concentrations of photocatalyst and PMS and a range of pH values. The enhanced activity is attributed to the synergistic effects of Ni, Co, and Fe, promoting effective electron-hole separation and reactive radical generation, primarily SO4•− and •OH. Quenching experiments highlighted sulfate radicals' predominant role in the degradation process. The Carbon@NiCoFeS photocatalyst also showed excellent reusability and stability over multiple cycles, and its versatility in degrading various organic pollutants underscores its potential for practical wastewater treatment applications. This research offers significant insights into multi-metal sulfide photocatalyst design, showcasing Carbon@NiCoFeS nanoparticles' promising role in environmental remediation via efficient PMS activation.

Keywords: NiCoFeS nanoparticles, photocatalytic degradation, peroxymonosulfate activation, organic pollutant removal, wastewater treatment

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Authors: Mehdi Mirzaei Alavijeh, Farzad Jalilian

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Background: Marijuana is the most widely used illicit drug worldwide, especially among adolescents and young adults, which can cause numerous complications. The aim of this study was to determine the pattern, motivation use, and factors related to marijuana use among Iranian youths based on the integrative model of behavioral prediction Methods: A cross-sectional study was conducted among 174 youths marijuana user in Kermanshah County and Isfahan County, during summer 2014 which was selected with the convenience sampling for participation in this study. A self-reporting questionnaire was applied for collecting data. Data were analyzed by SPSS version 21 using bivariate correlations and linear regression statistical tests. Results: The mean marijuana use of respondents was 4.60 times at during week [95% CI: 4.06, 5.15]. Linear regression statistical showed, the structures of integrative model of behavioral prediction accounted for 36% of the variation in the outcome measure of the marijuana use at during week (R2 = 36% & P < 0.001); and among them attitude, marijuana refuse, and subjective norms were a stronger predictors. Conclusion: Comprehensive health education and prevention programs need to emphasize on cognitive factors that predict youth’s health-related behaviors. Based on our findings it seems, designing educational and behavioral intervention for reducing positive belief about marijuana, marijuana self-efficacy refuse promotion and reduce subjective norms encourage marijuana use has an effective potential to protect youths marijuana use.

Keywords: marijuana, youth, integrative model of behavioral prediction, Iran

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Authors: Lee P. Leon, Raymond Charles

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This paper presents a method of evaluating the effect of aggregate angularity on hot mix asphalt (HMA) properties and its relationship to the Permanent Deformation resistance. The research concluded that aggregate particle angularity had a significant effect on the Permanent Deformation performance, and also that with an increase in coarse aggregate angularity there was an increase in the resistance of mixes to Permanent Deformation. A comparison between the measured data and predictive data of permanent deformation predictive models showed the limits of existing prediction models. The numerical analysis described the permanent deformation zones and concluded that angularity has an effect of the onset of these zones. Prediction of permanent deformation help road agencies and by extension economists and engineers determine the best approach for maintenance, rehabilitation, and new construction works of the road infrastructure.

Keywords: aggregate angularity, asphalt concrete, permanent deformation, rutting prediction

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Authors: Kassem Saab, Fritzen Bart, Yves-Marie Seveque

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The deployment of Free Space Optical Communications (FSOC) systems requires the development of robust and reliable Optical Ground Stations (OGS) that can be easily installed and operated. To this end, the Engineering Department of Airbus Defence and Space is actively working on the development of innovative and compact OGS solutions that can be deployed in various environments and provide high-quality connectivity under different atmospheric conditions. This article presents an overview of our recent developments in this field, including an evaluation study of different use cases of the FSOC with respect to different atmospheric conditions. The goal is to provide OGS solutions that are both simple and highly effective, allowing for the deployment of high-speed communication networks in a wide range of scenarios.

Keywords: end to end optical communication, laser propagation, optical ground station, turbulence

Procedia PDF Downloads 74

Authors: W. S. Hsu, S. W. Chen, Y. T. Ku, Y. Chiang, J. R. Wang , J. H. Yang, C. Shih

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ALOHA code was used to calculate the concentration under the CO₂ storage burst condition for Maanshan nuclear power plant (NPP) in this study. Five main data are input into ALOHA code including location, building, chemical, atmospheric, and source data. The data from Final Safety Analysis Report (FSAR) and some reports were used in this study. The ALOHA results are compared with the failure criteria of R.G. 1.78 to confirm the habitability of control room. The result of comparison presents that the ALOHA result is below the R.G. 1.78 criteria. This implies that the habitability of control room can be maintained in this case. The sensitivity study for atmospheric parameters was performed in this study. The results show that the wind speed has the larger effect in the concentration calculation.

Keywords: PWR, ALOHA, habitability, Maanshan

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Authors: Denys Osipenko, Jonathan Crook

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Because of the variety of the card holders’ behaviour types and income sources each consumer account can be transferred to a variety of states. Each consumer account can be inactive, transactor, revolver, delinquent, defaulted and requires an individual model for the income prediction. The estimation of transition probabilities between statuses at the account level helps to avoid the memorylessness of the Markov Chains approach. This paper investigates the transition probabilities estimation approaches to credit cards income prediction at the account level. The key question of empirical research is which approach gives more accurate results: multinomial logistic regression or multistage conditional logistic regression with binary target. Both models have shown moderate predictive power. Prediction accuracy for conditional logistic regression depends on the order of stages for the conditional binary logistic regression. On the other hand, multinomial logistic regression is easier for usage and gives integrate estimations for all states without priorities. Thus further investigations can be concentrated on alternative modeling approaches such as discrete choice models.

Keywords: multinomial regression, conditional logistic regression, credit account state, transition probability

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Authors: Zeeshan Muzammal, Usama Latif, Fouzia Younas, Syed Muhammad Hassan, Samia Razaq

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Unexpected rainfall has always been an issue in the lifetime of crops and brings destruction for the farmers who harvest them. Unfortunately, Pakistan is one of the countries in which untimely rain impacts badly on crops like wash out of seeds and pesticides etc. Pakistan’s GDP is related to agriculture, especially in rural areas farmers sometimes quit farming because leverage of huge loss to their crops. Through our surveys and research, we came to know that farmers in the rural areas of Pakistan need rain information to avoid damages to their crops from rain. We developed a prototype using ICTs to inform the farmers about rain one week in advance. Our proposed solution has two ways of informing the farmers. In first we send daily messages about weekly prediction and also designed a helpline where they can call us to ask about possibility of rain.

Keywords: ICTD, farmers, mobile based, Pakistan, rural areas, weather prediction

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Authors: Paula Lopez-Arce, Hector Altamirano, Dimitrios Rovas, James Berry, Bryan Hindle, Steven Hodgson

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Efficient mould remediation and accurate moisture diagnostics leading to condensation and mould growth in dwellings are largely untapped. Number of factors are contributing to the rising trend of excessive moisture in homes mainly linked with modern living, increased levels of occupation and rising fuel costs, as well as making homes more energy efficient. Environmental monitoring by means of data collection though loggers sensors and survey forms has been performed in a range of buildings from different UK regions. Air and surface temperature and relative humidity values of residential areas affected by condensation and/or mould issues were recorded. Additional measurements were taken through different trials changing type, location, and position of loggers. In some instances, IR thermal images and ventilation rates have also been acquired. Results have been interpreted together with environmental key parameters by processing and connecting data from loggers and survey questionnaires, both in buildings with and without moisture issues. Monitoring exercises carried out during Winter and Spring time show the importance of developing and following accurate protocols for guidance to obtain consistent, repeatable and comparable results and to improve the performance of environmental monitoring. A model and a protocol are being developed to build a diagnostic tool with the goal of performing a simple but precise residential atmospheric moisture diagnostics to distinguish the cause entailing condensation and mould generation, i.e., ventilation, insulation or heating systems issue. This research shows the relevance of monitoring and processing environmental data to assign moisture risk levels and determine the origin of condensation or mould when dealing with a building atmospheric moisture excess.

Keywords: environmental monitoring, atmospheric moisture, protocols, mould

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Authors: Sakura Yoshii, Masakazu Abe, Akihiro Iijima

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Nitrogen saturation has become one of the serious issues in the field of forest environment. The watershed protection forests located in the downwind hinterland of Tokyo Metropolitan Area are believed to be facing nitrogen saturation. In this study, we carefully focus on the balance of nitrogen between load and runoff. Annual nitrogen load via atmospheric deposition was estimated to 461.1 t-N/year in the upper reaches of the Kanna River. Annual nitrogen runoff to the forested headwater stream of the Kanna River was determined to 184.9 t-N/year, corresponding to 40.1% of the total nitrogen load. Clear seasonal change in NO3-N concentration was still observed. Therefore, watershed protection forest of the Kanna River is most likely to be in Stage-1 on the status of nitrogen saturation.

Keywords: atmospheric deposition, nitrogen accumulation, denitrification, forest ecosystems

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Authors: Sokkhey Phauk, Takeo Okazaki

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The challenging task in educational institutions is to maximize the high performance of students and minimize the failure rate of poor-performing students. An effective method to leverage this task is to know student learning patterns with highly influencing factors and get an early prediction of student learning outcomes at the timely stage for setting up policies for improvement. Educational data mining (EDM) is an emerging disciplinary field of data mining, statistics, and machine learning concerned with extracting useful knowledge and information for the sake of improvement and development in the education environment. The study is of this work is to propose techniques in EDM and integrate it into a web-based system for predicting poor-performing students. A comparative study of prediction models is conducted. Subsequently, high performing models are developed to get higher performance. The hybrid random forest (Hybrid RF) produces the most successful classification. For the context of intervention and improving the learning outcomes, a feature selection method MICHI, which is the combination of mutual information (MI) and chi-square (CHI) algorithms based on the ranked feature scores, is introduced to select a dominant feature set that improves the performance of prediction and uses the obtained dominant set as information for intervention. By using the proposed techniques of EDM, an academic performance prediction system (APPS) is subsequently developed for educational stockholders to get an early prediction of student learning outcomes for timely intervention. Experimental outcomes and evaluation surveys report the effectiveness and usefulness of the developed system. The system is used to help educational stakeholders and related individuals for intervening and improving student performance.

Keywords: academic performance prediction system, educational data mining, dominant factors, feature selection method, prediction model, student performance

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Authors: Joel F. Campbell, Bing Lin, Michael Obland, Susan Kooi, Tai-Fang Fan, Byron Meadows, Edward Browell, Wayne Erxleben, Doug McGregor, Jeremy Dobler, Sandip Pal, Christopher O'Dell, Ken Davis

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The Active Sensing of CO₂ Emissions over Nights, Days, and Seasons (ASCENDS) CarbonHawk Experiment Simulator (ACES) is a NASA Langley Research Center instrument funded by NASA’s Science Mission Directorate that seeks to advance technologies critical to measuring atmospheric column carbon dioxide (CO₂ ) mixing ratios in support of the NASA ASCENDS mission. The ACES instrument, an Intensity-Modulated Continuous-Wave (IM-CW) lidar, was designed for high-altitude aircraft operations and can be directly applied to space instrumentation to meet the ASCENDS mission requirements. The ACES design demonstrates advanced technologies critical for developing an airborne simulator and spaceborne instrument with lower platform consumption of size, mass, and power, and with improved performance. The Atmospheric Carbon and Transport – America (ACT-America) is an Earth Venture Suborbital -2 (EVS-2) mission sponsored by the Earth Science Division of NASA’s Science Mission Directorate. A major objective is to enhance knowledge of the sources/sinks and transport of atmospheric CO₂ through the application of remote and in situ airborne measurements of CO₂ and other atmospheric properties on spatial and temporal scales. ACT-America consists of five campaigns to measure regional carbon and evaluate transport under various meteorological conditions in three regional areas of the Continental United States. Regional CO₂ distributions of the lower atmosphere were observed from the C-130 aircraft by the Harris Corp. Multi-Frequency Fiber Laser Lidar (MFLL) and the ACES lidar. The airborne lidars provide unique data that complement the more traditional in situ sensors. This presentation shows the applications of CO₂ lidars in support of these science needs.

Keywords: CO₂ measurement, IMCW, CW lidar, laser spectroscopy

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Authors: Xiao Zhou, Jianlin Cheng

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A single amino acid mutation can have a significant impact on the stability of protein structure. Thus, the prediction of protein stability change induced by single site mutations is critical and useful for studying protein function and structure. Here, we presented a deep learning network with the dropout technique for predicting protein stability changes upon single amino acid substitution. While using only protein sequence as input, the overall prediction accuracy of the method on a standard benchmark is >85%, which is higher than existing sequence-based methods and is comparable to the methods that use not only protein sequence but also tertiary structure, pH value and temperature. The results demonstrate that deep learning is a promising technique for protein stability prediction. The good performance of this sequence-based method makes it a valuable tool for predicting the impact of mutations on most proteins whose experimental structures are not available. Both the downloadable software package and the user-friendly web server (DNpro) that implement the method for predicting protein stability changes induced by amino acid mutations are freely available for the community to use.

Keywords: bioinformatics, deep learning, protein stability prediction, biological data mining

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Authors: Jayanta Kumar Ghosh, Swastik Sunil Goriwale, Himangshu Sarkar

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Groundwater is one of the most valuable natural resources that society consumes for its domestic, industrial, and agricultural water supply. Its bulk and indiscriminate consumption affects the groundwater resource. Often, it has been found that the groundwater recharge rate is much lower than its demand. Thus, to maintain water and food security, it is necessary to monitor and management of groundwater storage. However, it is challenging to estimate groundwater storage (GWS) by making use of existing hydrological models. To overcome the difficulties, machine learning (ML) models are being introduced for the evaluation of groundwater level (GWL). Thus, the objective of this research work is to develop an ML-based model for the prediction of GWL. This objective has been realized through the development of an artificial neural network (ANN) model based on hydro-gravimetry. The model has been developed using training samples from field observations spread over 8 months. The developed model has been tested for the prediction of GWL in an observation well. The root means square error (RMSE) for the test samples has been found to be 0.390 meters. Thus, it can be concluded that the hydro-gravimetric-based ANN model can be used for the prediction of GWL. However, to improve the accuracy, more hydro-gravimetric parameter/s may be considered and tested in future.

Keywords: machine learning, hydro-gravimetry, ground water level, predictive model

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Authors: K. Roushanger, A. Soleymanzadeh

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Weirs are structures often used in irrigation techniques, sewer networks and flood protection. However, the hydraulic behavior of this type of weir is complex and difficult to predict accurately. An accurate flow prediction over a weir mainly depends on the proper estimation of discharge coefficient. In this study, the Genetic Expression Programming (GEP) approach was used for predicting trapezoidal and rectangular sharp-crested side weirs discharge coefficient. Three different performance indexes are used as comparing criteria for the evaluation of the model’s performances. The obtained results approved capability of GEP in prediction of trapezoidal and rectangular side weirs discharge coefficient. The results also revealed the influence of downstream Froude number for trapezoidal weir and upstream Froude number for rectangular weir in prediction of the discharge coefficient for both of side weirs.

Keywords: discharge coefficient, genetic expression programming, trapezoidal weir

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Authors: Mishell Vaca, Gema Gonzales, Francisco Quiroz

Abstract:

Heavy metals present in wastewater constitute a danger for living beings in general. In Ecuador, one of the sources of contamination is artisanal mining whose liquid effluents, in many of the cases without prior treatment, are discharged to the surrounding rivers. Lead is a pollutant that accumulated in the body causes severe health effects. Nowadays, there are several treatment methods to reduce this pollutant. The aim of this study is to reduce the concentration of lead II through the use of a porous material formed by a matrix of chitosan, in which hydroxyapatite and moringa particles smaller than 53 um are suspended. These materials are not toxic to the environment, and each one adsorbs metals independently, so the synergic effect between them will be evaluated. The synthesized material has a cylindrical design that allows increasing the surface area, which is expected to have greater capacity of adsorption. It has been determined that the best conditions for its preparation are to dissolve the chitosan in 1% v/v acetic acid with a pH = 5, then the hydroxyapatite and moringa are added to the mixture with magnetic stirring. This suspension is frozen, lyophilized and finally dried. In order to evaluate the performance of the synthesized material, synthetic solutions of lead are prepared at different concentrations, and the percentage of removal is evaluated. It is expected to have an effluent whose lead content is less than 0.2 mg/L which is the limit maximum allowable according to established environmental standards.

Keywords: adsorption, chitosan, hydroxyapatite, lead, moringa, water treatment

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Authors: Etienne Gaborit, Dorothy Durnford, Daniel Deacu, Marco Carrera, Nathalie Gauthier, Camille Garnaud, Vincent Fortin

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A new experimental streamflow forecasting system was recently implemented at the Environment and Climate Change Canada’s (ECCC) Canadian Centre for Meteorological and Environmental Prediction (CCMEP). It relies on CaLDAS (Canadian Land Data Assimilation System) for the assimilation of surface variables, and on a surface prediction system that feeds a routing component. The surface energy and water budgets are simulated with the SVS (Soil, Vegetation, and Snow) Land-Surface Scheme (LSS) at 2.5-km grid spacing over Canada. The routing component is based on the Watroute routing scheme at 1-km grid spacing for the Great Lakes and Nelson River watersheds. The system is run in two distinct phases: an analysis part and a forecast part. During the analysis part, CaLDAS outputs are used to force the routing system, which performs streamflow assimilation. In forecast mode, the surface component is forced with the Canadian GEM atmospheric forecasts and is initialized with a CaLDAS analysis. Streamflow performances of this new system are presented over 2019. Performances are compared to the current ECCC’s operational streamflow forecasting system, which is different from the new experimental system in many aspects. These new streamflow forecasts are also compared to persistence. Overall, the new streamflow forecasting system presents promising results, highlighting the need for an elaborated assimilation phase before performing the forecasts. However, the system is still experimental and is continuously being improved. Some major recent improvements are presented here and include, for example, the assimilation of snow cover data from remote sensing, a backward propagation of assimilated flow observations, a new numerical scheme for the routing component, and a new reservoir model.

Keywords: assimilation system, distributed physical model, offline hydro-meteorological chain, short-term streamflow forecasts

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Authors: M. Hadi Bordbar, Matthew England, Alex Sen Gupta, Agus Santoso, Andrea Taschetto, Thomas Martin, Wonsun Park, Mojib Latif

Abstract:

Climate models generally simulate long-term reductions in the Pacific Walker Circulation with increasing atmospheric greenhouse gases. However, over two recent decades (1992-2011) there was a strong intensification of the Pacific Trade Winds that is linked with a slowdown in global surface warming. Using large ensembles of multiple climate models forced by increasing atmospheric greenhouse gas concentrations and starting from different ocean and/or atmospheric initial conditions, we reveal very diverse 20-year trends in the tropical Pacific climate associated with a considerable uncertainty in the globally averaged surface air temperature (SAT) in each model ensemble. This result suggests low confidence in our ability to accurately predict SAT trends over 20-year timescale only from external forcing. We show, however, that the uncertainty can be reduced when the initial oceanic state is adequately known and well represented in the model. Our analyses suggest that internal variability in the Pacific trade winds can mask the anthropogenic signal over a 20-year time frame, and drive transitions between periods of accelerated global warming and temporary slowdown periods.

Keywords: trade winds, walker circulation, hiatus in the global surface warming, internal climate variability

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Authors: Baeza S. Roberto

Abstract:

The knitted fabric suffers a deformation in its dimensions due to stretching and tension factors, transverse and longitudinal respectively, during the process in rectilinear knitting machines so it performs a dry relaxation shrinkage procedure and thermal action of prefixed to obtain stable conditions in the knitting. This paper presents a dry relaxation shrinkage prediction of Bordeaux fiber using a feed forward neural network and linear regression models. Six operational alternatives of shrinkage were predicted. A comparison of the results was performed finding neural network models with higher levels of explanation of the variability and prediction. The presence of different reposes are included. The models were obtained through a neural toolbox of Matlab and Minitab software with real data in a knitting company of Southern Guanajuato. The results allow predicting dry relaxation shrinkage of each alternative operation.

Keywords: neural network, dry relaxation, knitting, linear regression

Procedia PDF Downloads 557