Development of a Precipitation Prediction Model Using Water Resource Measurement Data

Abstract

In response to the pressing global issue of water scarcity, numerous studies are under way both domestically and internationally to develop water-saving technologies and ensure clean water supply. These efforts heavily rely on water resource measurement data obtained from various sensing instruments operated by government and local agencies. These data are stored in servers and utilized for real-time predictive analyses and services. However, a challenge arises from the unrefined and fragmented nature of the data due to variations in sensing instruments and collection procedures. To address this issue and enable efficient big data analysis of water resource data, this paper introduces a novel approach. It presents the Smart Water Grid-based model for storing and classifying water resource measurement data, along with a comprehensive methodology for constructing a robust big data framework. By implementing this model, researchers and practitioners can enhance the effectiveness of their analyses and derive valuable insights from water resource data, contributing to the development of sustainable water management strategies.