JETI Admin2
Abstract
The research aims to address the significant challenge associated with attenuation due to water vapor in locations in Nigeria whose meteorological data could not be obtained from Nigerian Meteorological Agency (NIMET). This study investigates the attenuation in the 2 to 4 GHz spectrum caused by water vapor in Nigeria, focusing on spatial interpolation techniques for accurate prediction and analysis. Data was collected from the Nigeria Meteorological Agency (NIMET) over five years, including temperature, humidity, and pressure values at different heights. These values were processed and interpolated using four models: Inverse Distance Weighting (IDW), Kriging, Spline, and Linear interpolation. In this study, IDW uses a weighted average where weights decrease with distance, Kriging leverages spatial correlation through semivariogram modeling, Spline interpolation creates smooth curves using piecewise polynomials, and Linear Regression fits a linear equation to the data. The results show varying levels of sensitivity and accuracy across different regions in Nigeria. For example, in Ibadan (S/W), the interpolated values using IDW, Kriging, Spline, and Linear methods were 0.001234, 0.002345, 0.0045255, and 0.001234, respectively, indicating higher variability with Kriging and Spline. Similar patterns were observed in other regions, such as Benue (NC), Kogi (NC), Asaba (N/E), Jalingo (N/E), and Zaria (N/W), with varying interpolation values. Validation of the interpolation results was performed using error metrics such as RMSE, MAE, and R-squared. Linear interpolation had the lowest MAE (0.001077) and RMSE (0.001391), and the highest R-squared (0.875650), indicating the most accurate predictions and best fit among the four techniques
References
[1] Moses, O., Olla., O., Akinsanmi., Ilesanmi, Banjo, Oluwafemi., O., S., Ayodele. (2022). Impact of Attenuation due to Atmospheric Gases on the Communication Signal for fixed Satellite Links in Abuja, North Central Nigeria. FUOYE Journal of Engineering and Technology, doi: 10.46792/fuoyejet.v7i4.907 [2] Kadiri K., Keshinro K. K., Omotayo M. and Enem T. (2022). “Development of rain attenuation prediction in south west Nigeria on terrestrial link using artificial neural network”. doi: 10.33545/27076571.2022.v3.i1a.40. [3] Bawa, S., Isioye, O. A., Moses, M., & Abdulmumin, L. (2022). An appraisal of the ECMWF ReAnalysis5 (ERA5) model in estimating and monitoring atmospheric water vapour variability over Nigeria. Geodesy and Cartography, 48(3), 150–159. https://doi.org/10.3846/gac.2022.14777 [4] Moses, O., Olla., Akinsanmi, Olaitan., Oluwafemi, Ilesanmi, Banjo., O., S., Ayodele. (2022). Prediction of Gaseous Attenuation of Satellite Signal in Nigeria. Journal La Multiapp, doi: 10.37899/journallamultiapp.v3i3.671 [5] Mohammed, S. S., Shamsuddin, S., and Inhwan, P. (2021). “Projection of Water Availability and Sustainability in Nigeria Due to Climate Change”. Sustainability, doi: 10.3390/SU13116284 [6] Folasade, A S., Adeyanju, J. A., Robert, O., Abolade., Oluwole, A., Adegbola. (2021). Prediction of Rain Attenuation Trend due to Climate Change in Some Locations of Southwestern Nigeria. Radioelectronics and Communications Systems, doi: 10.3103/S0735272721010052. [7] Luini, L., Riva, C., and Capsoni, C. (2019) "On the Accuracy of Simplified Models for Water Vapor Attenuation Prediction at Ka band and Q band," 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC), New Delhi, India, 2019, pp. 1-4, doi: 10.23919/URSIAP-RASC.2019.8738387. [8] Omotosho., T. V., Akinyemi, M., L., Mandeep, J., S., and Mardina, A. (2014). “Total atmospheric absorption of fixed satellite communication signal due to oxygen and water vapor in Nigeria”. IEEE Antennas and Propagation Magazine, doi: 10.1109/MAP.2014.6837069. [9] Danladi., A., Silikwa, N., W., Gaya, K., G., Augustine, A., H., (2018). “Selection and Optimization of Most Suitable Path Loss Prediction Model for Suburban City in Nigeria Using Spline Interpolation”. doi: 10.9734/JERR/2018/V2I19900. [10] Lazarus, M. O., and Yusuf, D. O. (2019). “Monitoring atmospheric water vapour variability over Nigeria from ERA-Interim and NCEP reanalysis data”. doi: 10.1007/S42452-019-1177-X [11] Olaniran, J., Matthew., Olawale, E., Abiye., Muritala, A., Ayoola. (2021). “Assessment of static stability indices and related thermodynamic parameters for predictions of atmospheric convective potential and precipitation over Nigeria”. Meteorology and Atmospheric Physics, doi: 10.1007/S00703-020-00772-Z. [12] Davidson, O. A., Mukhtar, I. I., Wahidat, M., Simeon, I. S., Hassan, T. S., Samson, P A., and Mohammed, B. A. (2019). “Models for Estimating Precipitable Water Vapour and Variation of Dew Point Temperature with Other Parameters at Owerri, South Eastern, Nigeria”. doi: 10.11648/J.WROS.20190803.11 [13] Chibuike, C. I., and Itohan, O. A. (2023). “Rainfall variability patterns in Nigeria during the rainy season”. Dental science reports, doi: 10.1038/s41598-023-34970-7 [14] Mallaiah, M. (2023). “The Climate of Nigeria and Its Role in Landscape Modification”. doi: 10.1007/978-3-031-17972-3_2 [15] Daniel, E. O., Amajama, J., Enobong, P. O. (2015). “Seasonal tropospheric radio refractivity variation in calabar”, cross river state, Nigeria. [16] Omogbai. B., E., (2010). “An Empirical Prediction of Seasonal Rainfall in Nigeria”. doi: 10.1080/09709274.2010.11906317. [17] Adenodi. R.A. (2018). “A centurial analysis of rainfall variability in Nigeria”. Nigerian Journal of Technology, doi: 10.4314/NJT.V37I2.34 [18] Chamankar, S., Amerian, Y. and Naderi Salim S. (2023). Global positioning system precipitable water vapor interpolation using inverse multiquadric, artificial neural network and inverse distance weighted. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, doi: 10.5194/isprs-annals-x-4-w1-2022-109-2023 [19] D. S. Bisht, D. S., Rao, T. N., Rao, N. R., Chandrakanth, S. V. and Sharma, A. (2022). Prediction of Integrated Water Vapor Using a Machine Learning Technique. IEEE Geoscience and Remote Sensing Letters, doi: 10.1109/lgrs.2022.3217094 [20] Yu, A., Keiichi, E., Hisayasu, K., and Shin-ya, K. (2021). “Examination of Water Temperature Interpolation Method for Prediction”. doi: 10.1007/978-3-030-81523-3_33 [21] Hadeer, A., Abdelfatah., M., and Gamal, E. (2023). “A proposed neural network model for obtaining precipitable water vapor. doi: 10.1515/jag-2023-0035 [22] Hitesh, S., Vivek, K., Kumud, S., Boncho, B., and Ramjee, P. (2021). Prediction of Radio Wave Attenuation due to Cloud Using Machine Learning Techniques. doi: 10.1109/ICEST52640.2021.9483524. [23] Si, M. Z., Juan, H., Wen, H. C., Yinhui, Z., Peng, L., Gaoyuan, Z., Baofeng, J., Jiafeng, Z., and Congzheng, H. (2022). Water vapor estimation based on 1-year data of E-band millimeter wave link in North China. Atmospheric Measurement Techniques, doi: 10.5194/amt-15-1675-2022. [24] Małgorzata, W., Anna, L., Jackiewicz-Rek, W., and Andrzej, G. (2019). “Influence of variability of water content in different states on electromagnetic waves parameters affecting accuracy of GPR measurements of asphalt and concrete pavements”. doi: 10.1051/MATECCONF/201926206012 [25] Chen, T., Ren, L., Yuan, F., Yang, X., Jiang, S., Tang, T., Liu, Y., Zhao, C., and Zhang, L. (2017) “Comparison of Spatial Interpolation Schemes for Rainfall Data and Application in Hydrological Modeling”. Water 2017, 9, 342. https://doi.org/10.3390/w9050342. [26] Lazhar B., Ammar T., and Lotfi M. (2020) “Spatial distribution of the groundwater quality using kriging and Co-kriging interpolations”, Groundwater for Sustainable Development, Volume 11, 2020, 100473, ISSN 2352-801X, https://doi.org/10.1016/j.gsd.2020.100473. (https://www.sciencedirect.com/science/article/pii/S2352801X20300461)