Vol.15 (2025) No. 4 ISSN: 2088-5334 A Bayesian Approach to Spatio-Temporal Extreme Rainfall Modeling: Insights from West Java Achi Rinaldi a,* , Anik Djuraidah b , Aji Hamim Wigena b a Department of Mathematics Education, UIN Raden Intan Lampung, Indonesia b Department of Statistics, Bogor Agricultural University, Indonesia Corresponding author: * achi@radenintan.ac.id Abstract—The increasing phenomenon of extreme rainfall due to global climate change poses a serious challenge to hydrometeorological disaster risk management. Spatio-temporal modeling has proven to be a practical approach to understanding the distribution and intensity of extreme rainfall; however, its implementation still faces methodological obstacles, primarily due to the complexity of spatial and temporal data structures and limitations in the flexibility of grid-based spatial zoning. This study proposes the development of a Bayesian-based spatio-temporal model specifically designed to estimate extreme rainfall in West Java Province, a region with complex topographical conditions and high vulnerability to disasters. Three types of models were built, namely linear models with time trends, additive models without interaction, and additive models with spatial-temporal interaction. Spatial effects were modeled through the Conditional Autoregressive (CAR) approach, while parameter estimation was performed using the Integrated Nested Laplace Approximation (INLA) method. Evaluation results using RMSEP and DIC metrics show that the additive model with spatial-temporal interaction has the most optimal performance in predicting extreme rainfall. Longitude and latitude factors are identified as the dominant determining variables, which reinforces the critical role of geographical aspects in spatial estimation. This research not only expands the application scope of Bayesian methods in climate modeling but also provides a strong scientific basis for the development of early warning systems and data-driven disaster mitigation strategies. This approach can potentially be adapted for use in other regions and combined with Internet of Things (IoT) technologies to produce more precise and adaptive real-time extreme rainfall predictions. Keywords— Bayesian; CAR; extreme rainfall; Spatio-temporal. Manuscript received 26 Nov. 2024; revised 6 Jun. 2025; accepted 5 Jul. 2025. Date of publication 31 Aug. 2025. IJASEIT is licensed under a Creative Commons Attribution-Share Alike 4.0 International License. I. INTRODUCTION Extreme rainfall can cause significant impacts, such as floods and landslides, which affect multiple sectors [1], [2], including infrastructure, agriculture, and people's livelihoods [3]. This issue becomes critical to study and analyze, given the high risk posed [4]. In Indonesia, the West Java region stands out due to its high population density [5], unique geographical characteristics, and vulnerability to extreme weather events [6]. With a population of more than 50 million, West Java is the most populous province in Indonesia [7], where extreme rainfall events can have widespread impacts on urban and rural areas and economic activity [8]. Additionally, West Java hosts major watersheds such as the Citarum and Ciliwung rivers, which play a vital role in water resource management but are highly sensitive to extreme rainfall patterns [9]. The region's topography, consisting of mountains and lowlands, makes it prone to landslides and flooding. Therefore, accurate rainfall estimation is crucial to support disaster risk mitigation efforts and evidence-based policy planning [10], [11] particularly in vulnerable areas like West Java. Extreme rainfall modeling has incorporated various components designed to enhance accuracy, including spatial and temporal elements [11]. Spatial modeling is often employed to map disaster-prone areas, such as the creation of extreme rainfall zones in West Java [12], or rainfall prediction maps in Switzerland using the return value approach [13]. Similar research has been conducted by [14], [15], who utilized the CAR (Conditional Auto-Regressive) model to capture spatial and temporal patterns of extreme rainfall. Spatio-temporal models based on CAR have been developed using various approaches. Bernardinelli et al. [16] separated spatio-temporal random components, assuming a linear time trend. Ismail et. al [17] described spatio-temporal variability in three forms: spatial effects over the entire period, temporal 1029