• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.23-30
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• 2021

In recent years, hazardous flash flooding has caused deaths and damage to infrastructure in Saudi Arabia. In this paper, our aim is to assess patterns and trends in climate means and extremes affecting flash flood hazards and water resources in Saudi Arabia for the purpose to improve risk assessment for forecast capacity. We would like to examine temperature, precipitation climatology and trend magnitudes at surface stations in Saudi Arabia. Based on the assessment climate patterns maps and trends are accurately used to identify synoptic situations and tele-connections associated with flash flood risk. We also study local and regional changes in hydro-meteorological extremes over recent decades through new applications of statistical methods to weather station data and remote sensing based precipitation products; and develop remote sensing based high-resolution precipitation products that can aid to develop flash flood guidance system for the flood-prone areas. A dataset of extreme events has been developed using the multi-decadal station data, the statistical analysis has been performed to identify tele-connection indices, pressure and sea surface temperature patterns most predictive to heavy rainfall. It has been combined with time trends in extreme value occurrence to improve the potential for predicting and rapidly detecting storms. A methodology and algorithms has been developed for providing a well-calibrated precipitation product that can be used in the early warning systems for elevated risk of floods.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.196-200
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• 2024

Weather forecasting is considered to be of utmost important among various important sectors such as flood management and hydro-electricity generation. Although there are various numerical methods for weather forecasting but majority of them are reported to be Mechanistic computationally demanding due to their complexities. Therefore, it is necessary to develop and build models for accurately predicting the weather conditions which are faster as well as efficient in comparison to the prevalent meteorological models. The study has been undertaken to forecast various atmospheric parameters in the city of Bangalore using Naïve Bayes algorithms. The individual parameters analyzed in the study consisted of wind speed (WS), wind direction (WD), relative humidity (RH), solar radiation (SR), black carbon (BC), radiative forcing (RF), air temperature (AT), bar pressure (BP), PM₁₀ and PM_2.5 of the Bangalore city collected from Air Quality Monitoring Station for a period of 5 years from January 2015 to May 2019. The study concluded that Naive Bayes is an easy and efficient classifier that is centered on Bayes theorem, is quite efficient in forecasting the various air pollution parameters of the city of Bangalore.

• International Journal of Aeronautical and Space Sciences
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• v.10 no.1
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• pp.91-97
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• 2009

It is undertaken systematization of results of satellite and ground observation parameters characterizing a current condition and climatic variability of two selected geographical areas. One of them covers territory of Azerbaijan and another covers a wide area of Caspian See region. Average values and mean square deviations of following values are investigated: outgoing long wave radiation during a day and night (in nebulosity and cloudless). absorbed within a day of the stream of a sunlight of the system in "a terrestrial surface-atmosphere". degree of a covering by clouds of the selected areas during a day and at night, ground temperature values of air. pressure and speed of a wind. Monthly average values of corresponding parameters create a basis of suggested investigations. It has been presented features of a time course of investigated parameters for each month and year in the whole due to the continuously observations since 1982-2000. The scientific problem consists that there are no existed models which authentically would be cover the main aspects of a realities specified changes: they are identified by economic activities. growth of the population and other features of development of a human society or internal fluctuations of biogeophysical/climatic system. Possibilities of predictability of biosphere and climate changes depend on available timely supervision. adequacy of construction of appropriate models. understanding of mechanisms of direct and feedback influences in such complicated systems.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.233-246
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• 2023

In terms of understanding the water cycle and efficient water resource management, the importance of soil moisture has been highlighted. However, in Korea, the lack of qualified in-situ soil moisture data results in very limited utility. Even if satellite-based data are applied, the absence of ground reference data makes objective evaluation and correction difficult. The cosmic-ray neutron probe (CRNP) can play a key role in producing data for satellite data calibration. The installation of CRNP is non-invasive, minimizing damage to the soil and vegetation environment, and has the advantage of having a spatial representative for the intermediate scale. These characteristics are advantageous to establish an observation network in Korea which has lots of mountainous areas with dense vegetation. Therefore, this study was conducted to evaluate the applicability of the CRNP soil moisture observatory in Korea as part of the establishment of a Korean cOsmic-ray Soil Moisture Observing System (KOSMOS). The CRNP observation station was installed with the Gunup-ri observation station, considering the ease of securing power and installation sites and the efficient use of other hydro-meteorological factors. In order to evaluate the CRNP soil moisture data, 12 additional in-situ soil moisture sensors were installed, and spatial representativeness was evaluated through a temporal stability analysis. The neutrons generated by CRNP were found to be about 1,087 counts per hour on average, which was lower than that of the Solmacheon observation station, indicating that the Hongcheon observation station has a more humid environment. Soil moisture was estimated through neutron correction and early-stage calibration of the observed neutron data. The CRNP soil moisture data showed a high correlation with r=0.82 and high accuracy with root mean square error=0.02 m³/m³ in validation with in-situ data, even in a short calibration period. It is expected that higher quality soil moisture data production with greater accuracy will be possible after recalibration with the accumulation of annual data reflecting seasonal patterns. These results, together with previous studies that verified the excellence of CRNP soil moisture data, suggest that high-quality soil moisture data can be produced when constructing KOSMOS.