Toward Intelligent and Adaptive Rainfall Prediction Systems for Optimizing Agricultural Management in Tropical Regions: Future Directions for the Next Two Decades
DOI:
https://doi.org/10.22303/it.14.1.2026.01-12Keywords:
Rainfall Prediction, Artificial Intelligence, Deep Learning, Agricultural Management; Decision Support System (DSS)Abstract
Rainfall prediction plays a crucial role in supporting agricultural management, particularly in tropical regions where climate variability significantly affects farming activities. This study aims to examine the evolution of artificial intelligence–based rainfall prediction technologies while identifying emerging trends, key challenges, and future research directions. The research adopts a narrative literature review approach by analyzing scientific publications from the past decade across reputable databases.The findings reveal a clear transition from traditional statistical methods toward more advanced approaches, including machine learning, deep learning, and transformer-based architectures, which offer greater adaptability and improved capability in handling complex climate data. Furthermore, several critical challenges are highlighted, such as climate data non-stationarity, limited data availability, issues of model interpretability, and the lack of integration with agricultural management systems.As a primary contribution, this study proposes the Agro-AI Rainfall Framework (AARF), a layered conceptual framework that integrates multi-source data, computational intelligence, adaptive mechanisms, and practical applications in agricultural management. This framework is expected to support the development of more adaptive, accurate, and applicable rainfall prediction systems, contributing to smart and sustainable agriculture. Additionally, the study identifies opportunities for future research, particularly in multimodal learning, real-time analytics, and the advancement of Explainable AI.
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