Abstract: In this paper, the implementation of a rule-based
intuitive reasoner is presented. The implementation included two
parts: the rule induction module and the intuitive reasoner. A large
weather database was acquired as the data source. Twelve weather
variables from those data were chosen as the “target variables"
whose values were predicted by the intuitive reasoner. A “complex"
situation was simulated by making only subsets of the data available
to the rule induction module. As a result, the rules induced were
based on incomplete information with variable levels of certainty.
The certainty level was modeled by a metric called "Strength of
Belief", which was assigned to each rule or datum as ancillary
information about the confidence in its accuracy. Two techniques
were employed to induce rules from the data subsets: decision tree
and multi-polynomial regression, respectively for the discrete and the
continuous type of target variables. The intuitive reasoner was tested
for its ability to use the induced rules to predict the classes of the
discrete target variables and the values of the continuous target
variables. The intuitive reasoner implemented two types of
reasoning: fast and broad where, by analogy to human thought, the
former corresponds to fast decision making and the latter to deeper
contemplation. . For reference, a weather data analysis approach
which had been applied on similar tasks was adopted to analyze the
complete database and create predictive models for the same 12
target variables. The values predicted by the intuitive reasoner and
the reference approach were compared with actual data. The intuitive
reasoner reached near-100% accuracy for two continuous target
variables. For the discrete target variables, the intuitive reasoner
predicted at least 70% as accurately as the reference reasoner. Since
the intuitive reasoner operated on rules derived from only about 10%
of the total data, it demonstrated the potential advantages in dealing
with sparse data sets as compared with conventional methods.
Abstract: Photovoltaic power generation forecasting is an
important task in renewable energy power system planning and
operating. This paper explores the application of neural networks
(NN) to study the design of photovoltaic power generation
forecasting systems for one week ahead using weather databases
include the global irradiance, and temperature of Ghardaia city
(south of Algeria) using a data acquisition system. Simulations were
run and the results are discussed showing that neural networks
Technique is capable to decrease the photovoltaic power generation
forecasting error.
Abstract: Long term rainfall analysis and prediction is a
challenging task especially in the modern world where the impact of
global warming is creating complications in environmental issues.
These factors which are data intensive require high performance
computational modeling for accurate prediction. This research paper
describes a prototype which is designed and developed on grid
environment using a number of coupled software infrastructural
building blocks. This grid enabled system provides the demanding
computational power, efficiency, resources, user-friendly interface,
secured job submission and high throughput. The results obtained
using sequential execution and grid enabled execution shows that
computational performance has enhanced among 36% to 75%, for
decade of climate parameters. Large variation in performance can be
attributed to varying degree of computational resources available for
job execution.
Grid Computing enables the dynamic runtime selection, sharing
and aggregation of distributed and autonomous resources which plays
an important role not only in business, but also in scientific
implications and social surroundings. This research paper attempts to
explore the grid enabled computing capabilities on weather indices
from HOAPS data for climate impact modeling and change
detection.
Abstract: In this paper, in order to investigate the effects of
photovoltaic system introduction to detached houses in Japan, two
kinds of works were done. Firstly, the hourly generation amount of a
4.2kW photovoltaic system were simulated in 46 cities to investigate
the potential of the system in different regions in Japan using a
simulation model of photovoltaic system. Secondly, based on the
simulated electricity generation amount, the energy saving, the
environmental and the economic effect of the photovoltaic system
were examined from hourly to annual timescales, based upon
calculations of typical electricity, heating, cooling and hot water
supply load profiles for Japanese dwellings. The above analysis was
carried out using a standard year-s hourly weather data for the
different city provided by the Expanded AMeDAS Weather Data
issued by AIJ (Architectural Institute of Japan).
Abstract: Since primary school trips usually start from home,
attention by many scholars have been focused on the home end for
data gathering. Thereafter category analysis has often been relied
upon when predicting school travel demands. In this paper, school
end was relied on for data gathering and multivariate regression for
future travel demand prediction. 9859 pupils were surveyed by way
of questionnaires at 21 primary schools. The town was divided into 5
zones. The study was carried out in Skudai Town, Malaysia. Based
on the hypothesis that the number of primary school trip ends are
expected to be the same because school trips are fixed, the choice of
trip end would have inconsequential effect on the outcome. The
study compared empirical data for home and school trip end
productions and attractions. Variance from both data results was
insignificant, although some claims from home based family survey
were found to be grossly exaggerated. Data from the school trip ends
was relied on for travel demand prediction because of its
completeness. Accessibility, trip attraction and trip production were
then related to school trip rates under daylight and dry weather
conditions. The paper concluded that, accessibility is an important
parameter when predicting demand for future school trip rates.
Abstract: The connection between solar activity and adverse phenomena in the Earth’s environment that can affect space and ground based technologies has spurred interest in Space Weather (SW) research. A great effort has been put on the development of suitable models that can provide advanced forecast of SW events. With the progress in computational technology, it is becoming possible to develop operational large scale physics based models which can incorporate the most important physical processes and domains of the Sun-Earth system. In order to enhance our SW prediction capabilities we are developing advanced numerical tools. With operational requirements in mind, our goal is to develop a modular simulation framework of propagation of the disturbances from the Sun through interplanetary space to the Earth. Here, we report and discuss on the development of coronal field and solar wind components for a large scale MHD code. The model for these components is based on a potential field source surface model and an empirical Wang-Sheeley-Arge solar wind relation.
Abstract: Adhesion strength of exterior or interior coating of
steel pipes is too important. Increasing of coating adhesion on
surfaces can increase the life time of coating, safety factor of
transmitting line pipe and decreasing the rate of corrosion and costs.
Preparation of steel pipe surfaces before doing the coating process is
done by shot and grit blasting. This is a mechanical way to do it.
Some effective parameters on that process, are particle size of
abrasives, distance to surface, rate of abrasive flow, abrasive physical
properties, shapes, selection of abrasive, kind of machine and its
power, standard of surface cleanness degree, roughness, time of
blasting and weather humidity. This search intended to find some
better conditions which improve the surface preparation, adhesion
strength and corrosion resistance of coating. So, this paper has
studied the effect of varying abrasive flow rate, changing the
abrasive particle size, time of surface blasting on steel surface
roughness and over blasting on it by using the centrifugal blasting
machine. After preparation of numbers of steel samples (according to
API 5L X52) and applying epoxy powder coating on them, to
compare strength adhesion of coating by Pull-Off test. The results
have shown that, increasing the abrasive particles size and flow rate,
can increase the steel surface roughness and coating adhesion
strength but increasing the blasting time can do surface over blasting
and increasing surface temperature and hardness too, change,
decreasing steel surface roughness and coating adhesion strength.
Abstract: In most wheat growing moderate regions and
especially in the north of Iran climate, is affected grain filling by
several physical and abiotic stresses. In this region, grain filling often
occurs when temperatures are increasing and moisture supply is
decreasing. The experiment was designed in RCBD with split plot
arrangements with four replications. Four irrigation treatments
included (I0) no irrigation (check); (I1) one irrigation (50 mm) at
heading stage; (I2) two irrigation (100 mm) at heading and anthesis
stage; and (I3) three irrigation (150 mm) at heading, anthesis and
early grain filling growth stage, two wheat cultivars (Milan and
Shanghai) were cultured in the experiment. Totally raining was 453
mm during the growth season. The result indicated that biological
yield, grain yield and harvest index were significantly affected by
irrigation levels. I3 treatment produced more tillers number in m2,
fertile tillers number in m2, harvest index and biological yield. Milan
produced more tillers number in m2, fertile tillers in m2, while
Shanghai produced heavier tillers and grain 1000 weight. Plant height
was significant in wheat varieties while were not statistically
significant in irrigation levels. Milan produced more grain yield,
harvest index and biological yield. Grain yield shown that I1, I2, and
I3 produced increasing of 5228 (21%), 5460 (27%) and 5670 (29%)
kg ha-1, respectively. There was an interaction of irrigation and
cultivar on grain yields. In the absence of the irrigation reduced grain
1000 weight from 45 to 40 g. No irrigation reduced soil moisture
extraction during the grain filling stage. Current assimilation as a
source of carbon for grain filling depends on the light intercepting
viable green surfaces of the plant after anthesis that due to natural
senescence and the effect of various stresses. At the same time the
demand by the growing grain is increasing. It is concluded from
research work that wheat crop irrigated Milan cultivar could increase
the grain yield in comparison with Shanghai cultivar. Although, the
grain yield of Shanghai under irrigation was slightly lower than
Milan. This grain yield also was related to weather condition, sowing
date, plant density and location conditions and management of
fertilizers, because there was not significant difference in biological
and straw yield. The best result was produced by I1 treatment. I2 and
I3 treatments were not significantly difference with I1 treatment.
Grain yield of I1 indicated that wheat is under soil moisture
deficiency. Therefore, I1 irrigation was better than I0.
Abstract: Batteries and fuel cells contain a great potential to back up severe photovoltaic power fluctuations under inclement weather conditions. In this paper comparison between batteries and fuel cells is carried out in detail only for their PV power backup options, so their common attributes and different attributes is discussed. Then, the common and different attributes are compared; accordingly, the fuel cell is selected as the backup of Photovoltaic system. Finally, environmental evaluation of the selected hybrid plant was made in terms of plant-s land requirement and lifetime CO2 emissions, and then compared with that of the conventional fossilfuel power generating forms.
Abstract: The amount of urban artificial heat which affects the
urban temperature rise in urban meteorology was investigated in order
to clarify the relationships between urbanization and urban
meteorology in this study.
The results of calculation to identify how urban temperate was
increased through the establishment of a model for measuring the
amount of urban artificial heat and theoretical testing revealed that the
amount of urban artificial heat increased urban temperature by plus or
minus 0.23 ˚ C in 2007 compared with 1996, statistical methods
(correlation and regression analysis) to clarify the relationships
between urbanization and urban weather were as follows.
New design techniques and urban growth management are
necessary from urban growth management point of view suggested
from this research at city design phase to decrease urban temperature
rise and urban torrential rain which can produce urban disaster in terms
of urban meteorology by urbanization.
Abstract: This paper presents the methodology from machine
learning approaches for short-term rain forecasting system. Decision
Tree, Artificial Neural Network (ANN), and Support Vector Machine
(SVM) were applied to develop classification and prediction models
for rainfall forecasts. The goals of this presentation are to
demonstrate (1) how feature selection can be used to identify the
relationships between rainfall occurrences and other weather
conditions and (2) what models can be developed and deployed for
predicting the accurate rainfall estimates to support the decisions to
launch the cloud seeding operations in the northeastern part of
Thailand. Datasets collected during 2004-2006 from the
Chalermprakiat Royal Rain Making Research Center at Hua Hin,
Prachuap Khiri khan, the Chalermprakiat Royal Rain Making
Research Center at Pimai, Nakhon Ratchasima and Thai
Meteorological Department (TMD). A total of 179 records with 57
features was merged and matched by unique date. There are three
main parts in this work. Firstly, a decision tree induction algorithm
(C4.5) was used to classify the rain status into either rain or no-rain.
The overall accuracy of classification tree achieves 94.41% with the
five-fold cross validation. The C4.5 algorithm was also used to
classify the rain amount into three classes as no-rain (0-0.1 mm.),
few-rain (0.1- 10 mm.), and moderate-rain (>10 mm.) and the overall
accuracy of classification tree achieves 62.57%. Secondly, an ANN
was applied to predict the rainfall amount and the root mean square
error (RMSE) were used to measure the training and testing errors of
the ANN. It is found that the ANN yields a lower RMSE at 0.171 for
daily rainfall estimates, when compared to next-day and next-2-day
estimation. Thirdly, the ANN and SVM techniques were also used to
classify the rain amount into three classes as no-rain, few-rain, and
moderate-rain as above. The results achieved in 68.15% and 69.10%
of overall accuracy of same-day prediction for the ANN and SVM
models, respectively. The obtained results illustrated the comparison
of the predictive power of different methods for rainfall estimation.
Abstract: Weather disaster events were frequent and caused loss
of lives and property in Taiwan recently. Excessive concentration of
population and lacking of integrated planning led to Taiwanese coastal
zone face the impacts of climate change directly. Comparing to many
countries which have already set up legislation, competent authorities
and national adaptation strategies, the ability of coastal management
adapting to climate change is still insufficient in Taiwan. Therefore, it
is necessary to establish a complete institutional arrangement for
coastal management due to climate change in order to protect
environment and sustain socio-economic development. This paper
firstly reviews the impact of climate change on Taiwanese coastal
zone. Secondly, development of Taiwanese institutional arrangement
of coastal management is introduced. Followed is the analysis of four
dimensions of legal basis, competent authority, scientific and financial
support and international cooperations of institutional arrangement.
The results show that Taiwanese government shall: 1) integrate climate
change issue into Coastal Act, Wetland Act and territorial planning
Act and pass them; 2) establish the high level competent authority for
coastal management; 3) set up the climate change disaster coordinate
platform; 4) link scientific information and decision markers; 5)
establish the climate change adjustment fund; 6) participate in
international climate change organizations and meetings actively; 7)
cooperate with near countries to exchange experiences.
Abstract: This research was conducted for the first time at the
southeastern coasts of the Caspian Sea in order to evaluate the
performance of osteichthyes cooperatives through production (catch)
function. Using one of the indirect valuation methods in this research,
contributory factors in catch were identified and were inserted into
the function as independent variables. In order to carry out this
research, the performance of 25 Osteichthyes catching cooperatives
in the utilization year of 2009 which were involved in fishing in
Miankale wildlife refuge region. The contributory factors in catch
were divided into groups of economic, ecological and biological
factors. In the mentioned function, catch rate of the cooperative were
inserted into as the dependant variable and fourteen partial variables
in terms of nine general variables as independent variables. Finally,
after function estimation, seven variables were rendered significant at
99 percent reliably level. The results of the function estimation
indicated that human resource (fisherman quantity) had the greatest
positive effect on catch rate with an influence coefficient of 1.7 while
weather conditions had the greatest negative effect on the catch rate
of cooperatives with an influence coefficient of -2.07. Moreover,
factors like member's share, experience and fisherman training and
fishing effort played the main roles in the catch rate of cooperative
with influence coefficients of 0.81, 0.5 and 0.21, respectively.
Abstract: The paper presents a part of the results obtained in a
complex research project on Romanian Grey Steppe breed, owner of
some remarkable qualities such as hardiness, longevity, adaptability,
special resistance to ban weather and diseases and included in the
genetic fund (G.D. no. 822/2008.) from Romania.
Following the researches effectuated, we identified alleles of six
loci, codifying the six types of major milk proteins: alpha-casein S1
(α S1-cz); beta-casein (β-cz); kappa-casein (K-cz); beta-lactoglobulin
(β-lg); alpha-lactalbumin (α-la) and alpha-casein S2 (α S2-cz). In
system αS1-cz allele αs1-Cn B has the highest frequency (0.700), in
system β-cz allele β-Cn A2 ( 0.550 ), in system K-cz allele k-CnA2 (
0.583 ) and heterozygote genotype AB ( 0.416 ) and BB (0.375), in
system β-lg allele β-lgA1 has the highest frequency (0.542 ) and
heterozygote genotype AB ( 0.500 ), in system α-la there is
monomorphism for allele α-la B and similarly in system αS2-cz for
allele αs2-Cn A.
The milk analysis by the isoelectric focalization technique (I.E.F.)
allowed the identification of a new allele for locus αS1-casein, for two
of the individuals under analysis, namely allele called αS1-casein
IRV. When experiments were repeated, we noticed that this is not a
proteolysis band and it really was a new allele that has not been
registered in the specialized literature so far. We identified two
heterozygote individuals, carriers of this allele, namely: BIRV and
CIRV. This discovery is extremely important if focus is laid on the
national genetic patrimony.
Abstract: Thailand is the agriculture country as the weather and geography are suitable for agriculture environment. In 2011, the quantity of exported fresh vegetable was 126,069 tons which valued 117.1 million US dollars. Although the fresh vegetable has a high potential in exporting, there also have a lack of knowledge such as chemical usage, land usage, marketing and also the transportation and logistics. Nakorn Pathom province is the area which the farmer and manufacturer of fresh vegetable located. The objectives of this study are to study the basic information of the local fresh vegetable farmers in Nakorn Pathom province, to study the factor which effects the management of the fresh vegetable supply chain in Nakorn Pathom province and to study the problems and obstacle of the fresh vegetable supply chain in Nakorn Pathom province. This study is limited to the flow of the Nakorn Pathom province fresh vegetable from the farmers to the country which import the vegetable from Thailand. The populations of this study are 100 local farmers in Nakorn Pathom province. The result of this study shows that the key process of the fresh vegetable supply chain is in the supply sourcing process and manufacturing process.
Abstract: Text Mining is around applying knowledge discovery techniques to unstructured text is termed knowledge discovery in text (KDT), or Text data mining or Text Mining. In Neural Network that address classification problems, training set, testing set, learning rate are considered as key tasks. That is collection of input/output patterns that are used to train the network and used to assess the network performance, set the rate of adjustments. This paper describes a proposed back propagation neural net classifier that performs cross validation for original Neural Network. In order to reduce the optimization of classification accuracy, training time. The feasibility the benefits of the proposed approach are demonstrated by means of five data sets like contact-lenses, cpu, weather symbolic, Weather, labor-nega-data. It is shown that , compared to exiting neural network, the training time is reduced by more than 10 times faster when the dataset is larger than CPU or the network has many hidden units while accuracy ('percent correct') was the same for all datasets but contact-lences, which is the only one with missing attributes. For contact-lences the accuracy with Proposed Neural Network was in average around 0.3 % less than with the original Neural Network. This algorithm is independent of specify data sets so that many ideas and solutions can be transferred to other classifier paradigms.
Abstract: The three steps of the standard one-way nested grid
for a regional scale of the third generation WAve Model Cycle 4
(WAMC4) is scrutinized. The model application is enabled to solve
the energy balance equation on a coarse resolution grid in order to
produce boundary conditions for a smaller area by the nested grid
technique. In the present study, the model takes a full advantage of the
fine resolution of wind fields in space and time produced by the available
U.S. Navy Global Atmospheric Prediction System (NOGAPS)
model with 1 degree resolution. The nested grid application of the
model is developed in order to gradually increase the resolution from
the open ocean towards the South China Sea (SCS) and the Gulf of
Thailand (GoT) respectively. The model results were compared with
buoy observations at Ko Chang, Rayong and Huahin locations which
were obtained from the Seawatch project. In addition, the results were
also compared with Satun based weather station which was provided
from Department of Meteorology, Thailand. The data collected from
this station presented the significant wave height (Hs) reached 12.85
m. The results indicated that the tendency of the Hs from the model
in the spherical coordinate propagation with deep water condition in
the fine grid domain agreed well with the Hs from the observations.
Abstract: The paper presents the brief information on particular results of experimental study focused to the problems of behavior of structural plated components made of fiber-cement-based materials and used in building constructions, exposed to atmospheric physical effects given by the weather changes in the summer period. Weather changes represented namely by temperature and rain cause also the changes of the temperature and moisture of the investigated structural components. This can affect their static behavior that means stresses and deformations, which have been monitored as the main outputs of tests performed. Experimental verification is based on the simulation of the influence of temperature and rain using the defined procedure of warming and water sprinkling with respect to the corresponding weather conditions during summer period in the South Moravian region at the Czech Republic, for which the application of these structural components is mainly planned. Two types of components have been tested: (i) glass-fiber-concrete panels used for building façades and (ii) fiber-cement slabs used mainly for claddings, but also as a part of floor structures or lost shuttering, and so on.
Abstract: The mitigation of crop loss due to damaging freezes requires accurate air temperature prediction models. An improved model for temperature prediction in Georgia was developed by including information on seasonality and modifying parameters of an existing artificial neural network model. Alternative models were compared by instantiating and training multiple networks for each model. The inclusion of up to 24 hours of prior weather information and inputs reflecting the day of year were among improvements that reduced average four-hour prediction error by 0.18°C compared to the prior model. Results strongly suggest model developers should instantiate and train multiple networks with different initial weights to establish appropriate model parameters.
Abstract: This paper presents the development of analysis tools
for Home Agriculture project. The tools are required for monitoring
the condition of greenhouse which involves two components:
measurement hardware and data analysis engine. Measurement
hardware is functioned to measure environment parameters such as
temperature, humidity, air quality, dust and etc while analysis tool is
used to analyse and interpret the integrated data against the condition
of weather, quality of health, irradiance, quality of soil and etc. The
current development of the tools is completed for off-line data
recorded technique. The data is saved in MMC and transferred via
ZigBee to Environment Data Manager (EDM) for data analysis.
EDM converts the raw data and plot three combination graphs. It has
been applied in monitoring three months data measurement for
irradiance, temperature and humidity of the greenhouse..