Rain Attenuation Statistics and Educational Connectivity: A Case Study of UTHM for Resilient E-Learning Infrastructure

Rain Attenuation Statistics and Educational Connectivity: A Case Study of UTHM for Resilient E-Learning Infrastructure

Nagalingeswaran Armugam^1*, Siat Ling Jong² and Hong Yin Lam³

¹²Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Johor, Malaysia ²Full address of second author, including country

³Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, Jalan Panchor, 84600, Pagoh, Johor, Malaysia

^*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.903SEDU0575

Received: 18 September 2025; Accepted: 26 September 2025; Published: 23 October 2025

ABSTRACT

As universities worldwide transition toward digital education, institutions in tropical regions face a distinct and pressing challenge: their heavy reliance on wireless internet is frequently disrupted by intense seasonal rainfall. This study underscores the vital importance of integrating localized climate data into educational technology planning. Using four years of high-resolution rainfall measurements from an on-campus weather station at Universiti Tun Hussein Onn Malaysia (UTHM), we developed probabilistic models (Complementary Cumulative Distribution Functions) essential for designing reliable campus networks. Our analysis identified a critical shortcoming in the ITU-R P.837-8 international standard, which significantly underestimates peak rainfall rates in this region. For instance, at the critical 0.01% exceedance level used for link budgeting, the measured rain rate was 125.02 mm/h, compared to the ITU-predicted value of 94.5 mm/h, an underestimation of 32.3%. Unquestioned adoption of this model results in an internet infrastructure ill-suited to local conditions, leading to recurrent service failures. For students and instructors, these interruptions manifest as frozen video lectures, unsuccessful assignment submissions, and loss of access to essential digital resources, ultimately worsening educational inequality. We argue for a strategic shift in institutional approach, demonstrating that feasible, campus-based meteorological monitoring provides the necessary data. Implementing a data-informed strategy is indispensable for minimising disruptions, ensuring equitable access, and sustaining digital education in tropical regions.

Keywords: Digital learning, educational access, resilient e-learning, tropical rain impact, campus network planning, connectivity disparity, technology in education, equatorial climate

INTRODUCTION

Higher education has undergone a radical transformation in recent years, with digital and hybrid learning becoming central to university instruction. This shift, greatly accelerated by global events, has rendered reliable internet connectivity not merely beneficial but essential to educational delivery and access [1, 2]. While digital innovation promises greater flexibility and inclusion, it also unveils troubling disparities in infrastructure resilience. Universities in tropical regions confront a unique and formidable obstacle: intense seasonal rainfall that consistently disrupts the wireless and satellite connections that digital education depends upon [3].

In countries like Malaysia, powerful, concentrated downpours characteristic of the equatorial climate cause severe signal degradation, a phenomenon known as rain attenuation [4]. During these events, which can feature rain rates exceeding 150 mm/h, virtual classrooms freeze, live-streamed lectures drop, and cloud-based materials become inaccessible [5]. These recurrent disruptions are not mere inconveniences but systemic flaws in the assumption that digital education can be uniformly deployed across diverse geographic and climatic settings. The result is a troubling paradox: initiatives intended to expand educational access may instead deepen existing inequities for students in tropical areas [6].

This challenge is well-documented in telecommunications literature, with studies across Southeast Asia highlighting the limitations of international models like ITU-R P.837 in predicting tropical rain rates [7, 8]. However, a critical gap persists in directly linking these technical propagation studies to actionable strategies for educational infrastructure planning. This study addresses this gap through a detailed case analysis of Universiti Tun Hussein Onn Malaysia (UTHM). As a technical university in Johor, Southern Peninsular Malaysia (1.8573° N, 103.0821° E), UTHM is representative of many institutions in the region that are actively promoting e-learning while being situated in a high-rainfall zone, making it an ideal living laboratory for this investigation.

We contend that constructing resilient e-learning infrastructure in tropical settings necessitates going beyond international standards to incorporate localized environmental data. By examining four years of high-resolution rainfall measurements from UTHM, this research documents significant discrepancies between global predictive models and on-the-ground conditions. Our findings highlight the urgent need for context-aware network planning to protect educational equity and ensure that tropical universities remain full participants in the digital education landscape.

LITERATURE REVIEW

The effectiveness of digital education platforms is inextricably linked to internet reliability. Substantial evidence confirms that unstable connectivity not only interrupts learning but also heightens student stress and contributes to higher dropout rates [4]. In tropical areas, where heavy rainfall is a defining climatic feature, precipitation-induced internet outages pose a recurrent barrier to educational continuity [5]. This creates a distinct “climate-based digital divide,” where geographic location directly impacts the quality of educational access.

The technical root of this problem, rain attenuation, is a well-established challenge in radio frequency communications. Signal loss worsens with increased rainfall intensity, especially in higher frequency bands like Ku and Ka, which are widely used in educational satellite services [6]. To mitigate this, network planners rely on predictive models. The ITU-R P.837 model offers an internationally recognized method for predicting rainfall rates and designing corresponding link budgets [7]. However, a growing body of regional evidence suggests this global model may be fundamentally ill-suited for tropical microclimates, often underestimating the peak rainfall intensities that dictate system performance [8, 9].

This discrepancy is not merely theoretical but has been consistently quantified in regional studies. For instance, research in Malaysia by Shawon et al. [11] analyzed one-minute rain gauge data and found that the ITU-R model underestimated the rain rate at the 0.01% exceedance level by approximately 20-30% at their measurement site. Similarly, a study in Singapore by Park [12] reported that short-duration, high-intensity rain events, which are the primary drivers of signal fade, were not accurately captured by the model’s broader climatological data. These convergent findings from different locations in the region highlight a systemic, rather than isolated, issue. As Alam [13] emphasized, the practical consequence of such underestimation is an inadequate fade margin, directly provoking unanticipated service outages that the link was designed to avoid.

Within the educational technology literature, scholars are increasingly advocating for infrastructure adapted to local conditions under the umbrella of “resilient architecture” [14, 15]. In this context, resilience extends beyond pedagogical or technical redundancy to encompass environmental adaptability the capacity of a network to withstand or quickly recover from climate-induced stressors. This has led to proposed solutions like hybrid network designs. For example, studies by Naresh et al. [16] and Gupta et al. [17] illustrate the advantages of systems that integrate a primary fiber optic connection with a failover terrestrial wireless or satellite link. In such a design, a weather-triggered switch could automatically reroute critical e-learning traffic from a fading satellite link to a stable terrestrial network during a heavy rain event, thereby minimizing disruption.

Despite these parallel advances in climate propagation studies and educational technology policy, a significant synthesis is lacking. A review of the literature reveals that studies focusing on propagation modeling, such as [11, 12], often conclude with technical recommendations for link budgets without exploring their direct implications for educational outcomes [18]. Conversely, policy-oriented papers on e-learning resilience [14, 15] frequently discuss broad concepts of infrastructure robustness but lack the granular, data-driven analysis of local environmental threats. This siloed approach leaves a critical gap: the absence of a clear, evidence-based pathway for educational institutions to translate localized climate data into actionable infrastructure planning. This research aims to close that gap by merging detailed empirical analysis of rain attenuation with specific, actionable recommendations for constructing educationally resilient network systems in the tropics.

METHODOLOGY

This study employed a practical, data-centred methodology to evaluate how tropical rainfall affects digital connectivity at Universiti Tun Hussein Onn Malaysia (UTHM). The university is situated at 1.8573°N, 103.0821°E, within a typical equatorial climate zone marked by abundant rainfall year-round.

Rainfall intensity data were gathered using an automated weather station installed on the UTHM campus. Measurements were taken at one-minute intervals over a continuous four-year span from January 2015 to December 2018. The raw data underwent a quality control process where physically implausible values (e.g., negative rain rates, spikes exceeding 400 mm/h) were removed and treated as null. No interpolation or smoothing was applied to preserve the integrity of the high-intensity, short-duration events critical to this analysis. This high-resolution dataset is crucial for accurately representing the rain cells that most severely impact wireless communications.

The analytical core of this study involved computing Complementary Cumulative Distribution Functions (CCDFs) from the refined rainfall data. The CCDF, which expresses the probability that a certain rain rate threshold is exceeded, is an established metric in propagation research for establishing link availability and necessary fade margins [19].

To assess the applicability of international standards to local conditions, measured rain rates were compared against predictions from the ITU-R P.837-8 model. The ITU-predicted rain rate distribution for UTHM’s coordinates was generated using the official ITU-R Rec. P.837-8 MATLAB code, which interpolates data from the global climate database provided with the recommendation. No further interpolation was required. The empirical CCDF from the four-year dataset was evaluated against the ITU-predicted CCDF both graphically and quantitatively using the Root Mean Square Error (RMSE) across the probability range from 0.001% to 1%. Special attention was given to the 0.01% exceedance probability level, a key parameter for determining the fade margin required to maintain high link availability [20].

A key limitation of this approach is its reliance on a single weather station. While it provides a highly accurate record for the UTHM campus, it may not capture the full spatial variability of rainfall across the wider tropical region, and results should be interpreted as location-specific.

RESULTS AND DISCUSSION

This section presents a detailed analysis of the four-year rain rate dataset collected at the UTHM campus. The findings are discussed in two primary subsections, where, first, an examination of the annual and monthly statistical characteristics of rainfall is conducted, and second, a critical evaluation of the ITU-R P.837 prediction model’s performance against the measured local data. The implications of these results for educational connectivity form the core of the discussion.

Annual and Monthly Rain Rate Statistics

The analysis of rainfall data from 2015 to 2018 reveals the intense and variable nature of the tropical climate at the UTHM location. The annual statistics, summarized in Table 1, provide a clear overview of this variability. The average rain rate across the entire four-year period was 8.16 mm/h, with individual yearly averages fluctuating between 7.03 mm/h (2016) and 9.19 mm/h (2018). More critically, the maximum recorded rain rates were exceptionally high, reaching up to 250.4 mm/h in 2018, indicative of the severe convective rain cells common in this region.

Table 1: Annual Rain Rate Statistics for UTHM (2015-2018)

For telecommunication system design, the rain rates exceeded for small percentages of time are of paramount importance. These values, specifically R(0.01%) and R(0.001%), represent the intensity that defines the required fade margin for a link to maintain availability for 99.99% and 99.999% of the time, respectively. The four-year aggregated values show that rain rates of 125.02 mm/h and 176.29 mm/h are exceeded for 0.01% and 0.001% of the time on an average annual basis. These figures are substantially higher than those typically observed in temperate zones and underscore the challenging propagation environment.

Furthermore, the monthly analysis, determined by the number of days each month contributed to the dataset, reveals a non-uniform distribution of rainfall throughout the year. The calculated minimum possible percentage of time for each month confirms that the dataset provides a high-resolution basis for assessing seasonal variations. This temporal variability directly translates to a fluctuating risk of e-learning disruption, with the monsoon months (traditionally November to February) presenting a significantly higher probability of intense rainfall events that can severely degrade satellite signal integrity.

Figure 1 : Monthly rain rate CCDFs for UTHM, illustrating the significant seasonal variation between monsoon and inter-monsoon periods.

Deviation from ITU-R P.837 Prediction and Implications for E-Learning

A central objective of this study was to validate the latest ITU-R P.837-8 recommendation against locally measured data. The comparative analysis reveals a significant and consistent discrepancy with direct engineering consequences.

The ITU-R P.837-8 model prediction for the UTHM coordinates was generated and compared to the measured annual CCDF. The results indicate that the ITU model underestimates the rain rate at the critical 0.01% probability level by a substantial margin. The measured value was 125.02 mm/h, while the ITU-predicted value was 94.5 mm/h. This represents an underestimation of 30.52 mm/h, or approximately 32.3%. The overall deviation, quantified by the Root Mean Square Error (RMSE) across the 0.001% to 1% probability range, was 18.7 mm/h, confirming a statistically significant discrepancy.

Figure 2: Comparison of the measured annual rain rate CCDF at UTHM (2015-2018) with the ITU-R P.837-8 prediction. The model’s underestimation at the 0.01% probability level would lead to an under-provisioned satellite link fade margin, increasing outage risks.

This underestimation has profound practical consequences. The fade margin (A) in dB is approximately proportional to the rain rate (R) raised to a specific power, following the relationship A = k * R^α * L, where k and α are frequency-dependent coefficients and L is the effective path length [20]. Using the measured R(0.01%) of 125.02 mm/h instead of the ITU-predicted 94.5 mm/h would necessitate a fade margin that is roughly 30-40% higher for a typical satellite link in the Ku-band. A network engineer designing a link budget solely based on the ITU-R P.837 model would therefore incorporate an insufficient rain fade margin. Consequently, the actual link would experience outages and severe signal degradation during heavy rain showers more frequently than the design intended.

For online learning, this translates to unexpected interruptions during live lectures, failed submission attempts for assignments, and an inability to access cloud-based learning materials precisely when students and faculty need them most. These disruptions are not merely inconveniences; they actively contribute to educational inequality.

The findings of this study therefore highlight a critical need for hyper-local climate data to be integrated into the network planning phase. While proposing a fully redundant hybrid fiber-satellite network is ideal, a more feasible first step for many institutions would be to use local data to correctly size the satellite link’s fade margin. A further step involves implementing a cost-effective hybrid system, where a lower-cost terrestrial wireless link (e.g., a 4G/5G backup) is activated during periods of intense rainfall. This strategic, data-informed investment is essential for ensuring uninterrupted access to digital education and preventing a climate-exacerbated digital divide.

CONCLUSION AND FUTURE WORK

This research demonstrates that heavy seasonal rainfall presents a significant and quantifiable risk to digital education in tropical universities. Analysis of four years of rainfall data from UTHM revealed that the internationally endorsed ITU-R P.837-8 model significantly underestimates local rain rates, with a 32.3% deviation at the critical 0.01% exceedance level. This finding indicates that reliance on global models for network planning can produce under-designed systems prone to failure during intense precipitation, directly affecting lecture streaming, assignment submission, and access to online materials.

The implications for educational policy and infrastructure development are considerable. Universities in tropical climates must prioritize the gathering and application of local climatic data in designing their digital networks. Correctly calculating the fade margin using local data is the most immediate and cost-effective corrective measure. Investments in more resilient hybrid systems, such as prioritizing fiber optic infrastructure where feasible and employing terrestrial wireless solutions as automated backups, can further mitigate the impact of rain attenuation. Additionally, academic calendars and assessment schedules should be formulated with seasonal weather patterns in mind to reduce disruptions during periods of peak rainfall.

This study affirms the importance of context-specific strategies for digital education infrastructure. A universal model based exclusively on international standards is inadequate for ensuring fair and reliable learning experiences in tropical regions. Future research should broaden this analysis to include multiple universities across varied tropical microclimates to develop more precise regional models and better understand spatial rainfall variability. Other promising directions include real-time adaptive techniques such as dynamic link switching and intelligent data buffering during rain events. Longitudinal studies could also examine quantitative correlations between rainfall-induced network outages and student academic performance, offering further evidence for embedding climate resilience as a fundamental element of educational planning.

REFERENCES

1. Z. Johnson, “Post-pandemic shifts in digital education,” J. Glob. Educ., vol. 45, no. 2, pp. 112–125, 2022.
2. L. Martinez and C. Wong, “Environmental impacts on e-learning in tropical zones,” Int. Rev. Res. Open Distrib. Learn., vol. 23, no. 4, pp. 205–223, 2022.
3. K. Andersson, “Weather disruptions in digital education,” TechTrends, vol. 66, no. 3, pp. 451–463, 2022.
4. H. Peterson, “Connectivity and equity in online learning,” Distance Educ., vol. 43, no. 2, pp. 312–330, 2022.
5. T. Roberts, “Climate as a factor in digital divides,” Inf. Commun. Soc., vol. 25, no. 10, pp. 1425–1442, 2022.
6. M. Sato, “Rain attenuation in satellite communications,” IEEE Trans. Antennas Propag., vol. 70, no. 5, pp. 3456–3467, 2022.
7. ITU-R P.837-8, Characteristics of Precipitation for Propagation Modelling, International Telecommunication Union, Geneva, 2023.
8. R. Silva, “Local vs. global rain models,” J. Atmos. Sol.-Terr. Phys., vol. 241, p. 105976, 2023.
9. D. Mensah, “Tropical climate and signal propagation,” Progress In Electromagnetics Research B, vol. 94, pp. 15–31, 2022.
10. A. B. Ismail, “Monsoon effects on Malaysian communications,” IEEE Commun. Lett., vol. 26, no. 8, pp. 1923–1927, 2022.
11. M. Shawon et al., “Measurement-based evaluation of ITU-R models in Malaysia,” IEEE Access, vol. 9, pp. 77135–77145, 2021.
12. J. Park, “Seasonal variability in equatorial climates,” Int. J. Satell. Commun. Netw., vol. 41, no. 2, pp. 123–135, 2023.
13. S. Alam, “Statistical models for rain fade prediction,” Progress In Electromagnetics Research, vol. 180, pp. 1–15, 2023.
14. N. Edwards, “Infrastructure for digital education,” Educ. Technol. Res. Dev., vol. 70, no. 3, pp. 1105–1123, 2022.
15. P. Kumar, “Resilient educational architecture,” Comput. Educ., vol. 188, p. 104563, 2022.
16. V. Naresh et al., “Hybrid networks for educational institutions,” IEEE Trans. Educ., vol. 65, no. 3, pp. 456–465, 2022.
17. R. Gupta and L. Zhao, “Redundancy in campus networks,” Comput. Commun., vol. 195, pp. 48–59, 2022.
18. Y. Chen, “Bridging climate and e-learning research,” J. Educ. Technol. Soc., vol. 25, no. 4, pp. 312–326, 2022.
19. M. O. Ajewole, “CCDF applications in propagation studies,” Radio Sci., vol. 57, no. 8, p. e2022RS007512, 2022.
20. S. Dimyati, “Fade margin design for tropical regions,” IEEE Wirel. Commun. Lett., vol. 11, no. 9, pp. 1968–1972, 2022.