Department of Health Risk Management, China Medical University, 91 Hsueh-Shi Rd., Taichung, 40402, Taiwan, Republic of China.
Environ Monit Assess. 2012 Jan;184(1):381-95. doi: 10.1007/s10661-011-1975-0. Epub 2011 Mar 17.
The groundwater level represents a critical factor to evaluate hillside landslides. A monitoring system upon the real-time prediction platform with online analytical functions is important to forecast the groundwater level due to instantaneously monitored data when the heavy precipitation raises the groundwater level under the hillslope and causes instability. This study is to design the backend of an environmental monitoring system with efficient algorithms for machine learning and knowledge bank for the groundwater level fluctuation prediction. A Web-based platform upon the model-view controller-based architecture is established with technology of Web services and engineering data warehouse to support online analytical process and feedback risk assessment parameters for real-time prediction. The proposed system incorporates models of hydrological computation, machine learning, Web services, and online prediction to satisfy varieties of risk assessment requirements and approaches of hazard prevention. The rainfall data monitored from the potential landslide area at Lu-Shan, Nantou and Li-Shan, Taichung, in Taiwan, are applied to examine the system design.
地下水水位是评估边坡滑坡的关键因素。由于强降雨会导致山坡下的地下水水位瞬间升高，从而引发不稳定，因此，建立一个带有在线分析功能的实时预测平台的监测系统对于预测地下水水位非常重要。本研究旨在设计一个环境监测系统的后端，该系统具有高效的机器学习算法和地下水水位波动预测知识库。基于模型-视图-控制器架构的基于 Web 的平台，结合 Web 服务和工程数据仓库技术，支持在线分析过程，并为实时预测反馈风险评估参数。所提出的系统结合了水文计算模型、机器学习、Web 服务和在线预测，以满足各种风险评估要求和灾害防治方法。从台湾南投县庐山和台中梨山的潜在滑坡区域监测到的降雨数据被应用于检验系统设计。
