89 research outputs found
TEHRAN AIR POLLUTION MODELING USING LONG-SHORT TERM MEMORY ALGORITHM: AN UNCERTAINTY ANALYSIS
Air pollution is a major environmental issue in urban areas, and accurate forecasting of particles 10 μm or smaller (PM10) level is essential for smart public health policies and environmental management in Tehran, Iran. In this study, we evaluated the performance and uncertainty of long short-term memory (LSTM) model, along with two spatial interpolation methods including ordinary kriging (OK) and inverse distance weighting (IDW) for mapping the forecasted daily air pollution in Tehran. We used root mean square error (RMSE) and mean square error (MSE) to evaluate the prediction power of the LSTM model. In addition, prediction intervals (PIs), and Mean and standard deviation (STD) were employed to assess the uncertainty of the process. For this research, the air pollution data in 19 Tehran air pollution monitoring stations and temperature, humidity, wind speed and direction as influential factors were taken into account. The results showed that the OK had better RMSE and STD in the test (32.48 ± 9.8 μg/m3) and predicted data (56.6 ± 13.3 μg/m3) compared with those of the IDW in the test (47.7 ± 22.43 μg/m3) and predicted set (62.18 ± 26.1 μg/m3). However, in PIs, IDW ([0, 0.7] μg/m3) compared with the OK ([0, 0.5] μg/m3) had better performance. The LSTM model achieved in the predicted values an RMSE of 8.6 μg/m3 and a standard deviation of 9.8 μg/m3 and PIs between [2.7 ± 4.8, 14.9 ± 15] μg/m3
GROUNDWATER LEVEL PREDICTION USING DEEP RECURRENT NEURAL NETWORKS AND UNCERTAINTY ASSESSMENT
Groundwater is one of the most important sources of regional water supply for humans. In recent years, several factors have contributed to a significant decline in groundwater levels (GWL) in certain regions. As a result of climate change, such as temperature increase, rainfall decrease, and changes in relative humidity, it is necessary to investigate and model the effects of these factors on GWL. Although a number of researches have been conducted on GWL modeling with machine learning (ML) and deep learning (DL) algorithms, only a limited number of studies have reported model uncertainty. In this paper, GWL modeling of some piezometric wells has been conducted by considering the effects of the meteorological parameters with Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. The models were trained on one piezometric well data and predictions were executed on six other wells. To perform an uncertainty assessment, the models were run 10 times and their means were calculated. Subsequently, their standard deviations were considered to evaluate the outcomes. In addition, the prediction power of the models was validated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), and R-Squared (R2). Finally, for all the six wells that did not participate in the training phase, the prediction functions of the trained models were run 10 times and their accuracy was assessed. The results indicate that LSTM (R2=95.6895, RMSE=0.4744 m, NRMSE=0.0558, MAE=0.3383 m) had a better performance compared to that of GRU (R2=95.2433, RMSE=0.4984 m, NRMSE=0.0586, MAE=0.3658 m) on the GWL modeling
Machine Learning for Mathematical Software
While there has been some discussion on how Symbolic Computation could be
used for AI there is little literature on applications in the other direction.
However, recent results for quantifier elimination suggest that, given enough
example problems, there is scope for machine learning tools like Support Vector
Machines to improve the performance of Computer Algebra Systems. We survey the
authors own work and similar applications for other mathematical software.
It may seem that the inherently probabilistic nature of machine learning
tools would invalidate the exact results prized by mathematical software.
However, algorithms and implementations often come with a range of choices
which have no effect on the mathematical correctness of the end result but a
great effect on the resources required to find it, and thus here, machine
learning can have a significant impact.Comment: To appear in Proc. ICMS 201
Assembly and Cleaning of CSPs for High, Low, and UltraLow Volume Applications
ABSTRACT A JPL-led CSP Consortium of enterprises, composed of representing government agencies and private companies, recently joined together to pool in-kind resources for developing the quality and reliability of chip scale packages (CSPs) for a variety of projects. Since last year, more than 150 test vehicles, single-and double-sided multilayer PWBs, have been assembled and are presently being subjected to various environmental tests. Recent reliability data, specifically the impact of assembly underfill on reliability, is being presented in another paper in this conference. This paper presents lessons learned on assemblies at three facilities with high, low, and ultralow volume production
Synthesis and characterization of ZnO nanostructures using palm olein as biotemplate
Background:
A green approach to synthesize nanomaterials using biotemplates has been subjected to intense research due to several advantages. Palm olein as a biotemplate offers the benefits of eco-friendliness, low-cost and scale-up for large scale production. Therefore, the effect of palm olein on morphology and surface properties of ZnO nanostructures were investigated.
Results:
The results indicate that palm olein as a biotemplate can be used to modify the shape and size of ZnO particles synthesized by hydrothermal method. Different morphology including flake-, flower- and three dimensional star-like structures were obtained. FTIR study indicated the reaction between carboxyl group of palm olein and zinc species had taken place. Specific surface area enhanced while no considerable change were observed in optical properties.
Conclusion:
Phase-pure ZnO particles were successfully synthesized using palm olein as soft biotemplating agent by hydrothermal method. The physico-chemical properties of the resulting ZnO particles can be tuned using the ratio of palm olein to Zn cation
Removal of hydrogen sulfide by zinc oxide nanoparticles in drilling fluid
Hydrogen sulfide is a very dangerous, toxic and corrosive gas. It can
diffuse into drilling fluid from formations during drilling of gas and
oil wells. Hydrogen sulfide should be removed from this fluid to reduce
the environmental pollution, protect the health of drilling workers and
prevent corrosion of pipelines and equipments. In this research nano
zinc oxide with 14-25 nm particle size and 44-56 m2/g specific surface
area was synthesized by spray pyrolysis method. The synthesized
nanoparticles were used to remove hydrogen sulfide from water based
drilling fluid. The efficiency of these nanoparticles in the removal of
hydrogen sulfide from drilling mud were evaluated and compared with
that of bulk zinc oxide. The obtained results show that synthesized
zinc oxide nanoparticles are completely able to remove hydrogen sulfide
from water based drilling mud in just 15 min., whereas bulk zinc oxide
is able to remove 2.5% of hydrogen sulfide in as long as 90 min. under
the same operating conditions
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