Spatio-Temporal Rainfall Variability Analysis, Case Study: KSA

SpatioTemporal Rainfall Variability Analysis, Case Study: KSA
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© 2021 by IJETT Journal
Volume-69 Issue-12
Year of Publication : 2021
Authors : Salma M. Elsherif, Alaa El-Zawahry, ahmed H. Soliman
DOI :  10.14445/22315381/IJETT-V69I12P216

How to Cite?

Salma M. Elsherif, Alaa El-Zawahry, ahmed H. Soliman, "SpatioTemporal Rainfall Variability Analysis, Case Study: KSA," International Journal of Engineering Trends and Technology, vol. 69, no. 12, pp. 136-143, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I12P216

Abstract
Rainfall amount and distribution are varied spatially and temporally all over the world. Moreover, the rainfall variability may significantly vary within the same local region. Identification of rainfall amount and pattern is one of the main challenges facing all hydrologic analysts. Several approaches are available nowadays to deal with the variability of data sets. Some of these approaches can be simply applied, while others are more complicated and maybe not appropriate to be used to handle rainfall variability. So, this paper is devoted to presenting a comprehensive framework of rainfall variability analysis and handling to be followed. The framework is built by combining the K-means approach with some newly developed techniques as part of this research to enhance the results of the current approaches and convert them to be more dynamic. The built framework is tested using rainfall data collected from more than 280 rainfall gauges distributed all over the Kingdom of Saudi Arabia, which has high diversity with no defined pattern, neither spatially nor temporal. The testing results confirmed that the framework is a very powerful tool and gives robust results.

Keywords
K-means, KSA Rainfall, Rainfall Variability, Spatial Clustering, Two-Step Clustering.

Reference
[1] H. E. Beck, N. E. Zimmermann, T. R. McVicar, N. Vergopolan, A. Berg, and E. F. Wood, Present and future Köppen-Geiger climate classification maps at 1-km resolution, Sci. data, 5 (2018) 180-214.
[2] M. S. ALYamani and Z. Sen, Regional variations of monthly rainfall amounts in the Kingdom of Saudi Arabia, Earth Sci., 6 (1) (1993).
[3] P. Wan, POINT RAINFALL CHARACTERISTICS OF SAUDI ARABIA, Proc. Inst. Civ. Eng., 61 (1) 91976) 179–187.
[4] M. El-Nesr, A. Abdulrahman, and M. Abu-Zreig, Analysis of evapotranspiration variability and trends in the Arabian Peninsula, Am. J. Environ. Sci., 6 (6) (2010) 535–547.
[5] S. Hag-elsafi and M. El-Tayib, Spatial and statistical analysis of rainfall in the Kingdom of Saudi Arabia from 1979 to 2008, Weather, 71 (10) (2016) 262–266.
[6] A. Mashat and H. Abdel Basset, Analysis of rainfall over Saudi Arabia, J. King Abdulaziz Univ. Metrol. Environ. Arid L. Agric. Sci., 142 (592) (2011) 1–40
[7] A. O. Alamodi, A. S. Mashat, and H. M. Abdel Basset, On the relation between atmospheric pressure systems and rainfall prediction over the Kingdom of Saudi Arabia, Project, (2008).
[8] B., Bashir, andH., Fouli, Studying the spatial distribution of maximum monthly rainfall in selected regions of Saudi Arabia using geographic information systems, Arabian Journal of Geosciences, 8(11) (2015) 9929–9943.
[9] M. A., Al-Saleh, Variability and frequency of daily rainfall in Riyadh, Saudi Arabia. The Geographical Bulletin, 39(1) (1997) 48–57.
[10] S., Rehman, Temperature and rainfall variation over Dhahran, Saudi Arabia,(1970–2006), International Journal of Climatology: A Journal of the Royal Meteorological Society, 30(3) (2010) 445–449.
[11] M. M. A., Abdullah,A. M., Youssef, F., Nashar, and E. A., AlFadail, Statistical Analysis of Rainfall Patterns in Jeddah City, KSA: Future Impacts, In Rainfall. IntechOpen, (2019).
[12] R. M., Atif, M., Almazroui, S., Saeed, M. A., Abid, M. N., Islam, and M., Ismail, Extreme precipitation events over Saudi Arabia during the wet season and their associated teleconnections, Atmospheric Research, 231(February 2019), 104655. https://doi.org/10.1016/j.atmosres.2019.104655
[13] M., Almazroui, Calibration of TRMM rainfall climatology over Saudi Arabia during 1998–2009, Atmospheric Research, 99(3–4) (2011) 400–414.
[14] M. Almazroui, R. Dambul, M. N. Islam, and P. D. Jones, Principal components-based regionalization of the Saudi Arabian climate, Int. J. Climatol., 35 (9) (2015) 2555–2573.https://doi.org/10.1002/joc.4139
[15] W. M., Abdeen, A. G., Awadallah, and N. A. Hassan, Investigating regional distribution for maximum daily rainfall in arid regions: a case study in Saudi Arabia, Arabian Journal of Geosciences, 13(13) (2020). https://doi.org/10.1007/s12517-020-05413-8.
[16] D. C., Hoaglin, andB., Iglewicz, Fine-tuning some resistant rules for outlier labeling, Journal of the American Statistical Association, 82(400) (1987) 1147–1149.
[17] D. C., Montgomery, G. C., Runger, andN. F. Hubele, Engineering statistics. John Wiley & Sons, (2011).
[18] J. K., Eischeid, P. A., Pasteris, H. F., Diaz, M. S., Plantico, andN. J., Lott, Creating a serially complete, national daily time series of temperature and precipitation for the western United States, Journal of Applied Meteorology, 39(9) (2000) 1580–1591.
[19] M. F., Villazón, andP., Willems, Filling gaps and daily dis accumulation of precipitation data for the rainfall-runoff model, in Proc. 4th Int. Sci. Conf. BALWOI, (2010) 25–29.
[20] A., Masood, J., Bahrawi, andA., Elfeki, Modeling annual rainfall time series in Saudi Arabia using first-order autoregressive AR(1) model, Arabian Journal of Geosciences, 12(6) (2019). https://doi.org/10.1007/s12517-019-4330-3
[21] J. L., Morales, F. A., Horta-Rangel, I., Segovia-Domínguez, A. R., Morua, andJ. H., Hernández, Analysis of a new spatial interpolation weighting method to estimate missing data applied to rainfall records,Atmósfera, 32(3) (2019) 237–259.
[22] R. S. V., Teegavarapu, Missing precipitation data estimation using optimal proximity metric-based imputation, nearest-neighbor classification and cluster-based interpolation methods, Hydrological Sciences Journal, 59(11) (2014) 2009–2026.
[23] S., Gopalakrishnan, Dr. B., Ebenezer Abishek, Dr. A., Vijayalakshm, and Dr. V., Rajendran, Analysis, And Diagnosis Using Deep-LearningAlgorithm On Erythemato-Squamous Disease, International Journal of Engineering Trends and Technology, 69(3) (2021)52–57.
[24] M. E., Celebi, H. A., Kingravi, andP. A. Vela, A comparative study of efficient initialization methods for the k-means clustering algorithm, Expert Systems with Applications, 40(1) (2013) 200–210.
[25] E. W., Forgy, Cluster analysis of multivariate data: efficiency versus interpretability of classifications, Biometrics, 21(1965) 768–769.
[26] S., Lloyd, Least squares quantization in PCM, IEEE Transactions on Information Theory, 28(2) (1982) 129–137.
[27] T. F., Gonzalez, Clustering to minimize the maximum intercluster distance, Theoretical Computer Science, 38(1985) 293–306.
[28] S., Sieranoja, andP. Fränti, Random projection for k-means clustering, International Conference on Artificial Intelligence and Soft Computing, Springer (2018) 680–689.
[29] D., Arthur, andS., Vassilvitskii, k-means++: the advantages of careful seeding,. SODA’07: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, Philadelphia, PA, (2007) 1027–1035.
[30] C. M., Potera?, M. C., Mih?escu, andM., Mocanu, An optimized version of the K-Means clustering algorithm, Federated Conference on Computer Science and Information Systems, IEEE, (2014) 695–699.
[31] P., Fränti, andS. Sieranoja, How much can k-means be improved by using better initialization and repeats?, Pattern Recognition, 93(2019) 95–112.
[32] T. M., Kodinariya, andP. R., Makwana, Review on determining the number of Cluster in K-Means Clustering, International Journal of Advanced Research in Computer Science and Management Studies, 1(6) (2013) 2321–7782.
[33] T., Chiu, D., Fang, J., Chen, Y., Wang, andC., Jeris, A robust and scalable clustering algorithm for mixed type attributes in the large database environment. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2001) 263–268.
[34] T., Zhang, R., Ramakrishnan, and M. Livny, BIRCH: An efficient Data Clustering Method for Very Large Databases, Proceedings of the 1996 ACM SIGMODInternationalConferenceonManagementof Data,(1997) 103–114.