Integrating NLP and Indian Traditional Knowledge for Climate Change Mitigation and Adaptation: A Novel Rainfall Prediction Model

The traditional Indian knowledge system encompasses rich intellectual, philosophical, and scientific traditions, mostly documented in Sanskrit, making it less accessible today. This research proposes Sanskrit-to-English Machine Translation to bridge this gap, enabling practical applications. It focuses on translating Meghamala, a Sanskrit text on rainfall prediction, to extract key parameters. These insights will be computationally modeled to develop an improved rainfall prediction system. This approach not only enhances weather forecasting but also promotes interest in Sanskrit literature and its scientific relevance in modern society. Objectives: This research applies NLP-based Machine Translation for accurate Sanskrit-to-English translation, making ancient Indian knowledge accessible. It focuses on developing a rainfall prediction model by extracting meteorological parameters from Meghamala and integrating them with modern computational techniques. By bridging traditional wisdom with advanced technology, this approach enhances rainfall forecasting accuracy, benefiting agriculture, water resource management, and climate studies, while also promoting the relevance of Sanskrit literature in scientific research. Methods: The methodology involves applying NLP-based Machine Translation algorithms to translate Meghamala from Sanskrit to English, extracting rainfall-related parameters. These parameters are analyzed and structured for integration into a predictive model. Machine learning techniques are then employed to develop a rainfall prediction system, combining traditional insights with modern meteorological data. The model is validated against historical weather records to assess its accuracy and applicability in real-world forecasting. Results: The Sanskrit-to-English Machine Translation of ‘Meghamala’ can extract rainfall-related parameters, enabling their integration into modern prediction models. This approach enhances rainfall forecasting accuracy, benefiting agriculture and water management. Bridging ancient knowledge with technology, the model demonstrates potential for broader applications in weather prediction and other scientific domains. Conclusions: Rainfall prediction is crucial for India's economy, which heavily relies on agriculture. Traditional Indian knowledge, documented in Sanskrit, holds valuable scientific insights but remains inaccessible to many. Sanskrit-to-English Machine Translation can bridge this gap, enabling its application in real-world problems like weather prediction, healthcare, and agriculture. The proposed translation of Sanskrit-rich texts like Meghamala, which contains extensive rainfall parameters and observations, can aid in developing improved rainfall prediction systems tailored to India’s needs.