The Application of Wireless Underground Sensor Networks to Monitor Seepage inside an Earth Dam.

Earth dams or embankments are susceptible to instability due to internal seepage, piping, and erosion, which can lead to catastrophic failure. Therefore, monitoring the seepage water level before the dam collapses is an important task for early warning of dam failure. Currently, there are hardly any monitoring methods that use wireless underground transmission to monitor the water content inside earth dams. Real-time monitoring of changes in the soil moisture content can more directly determine the water level of seepage. Wireless transmission of sensors buried underground requires signal transmission through the soil medium, which is more complex than traditional air transmission. Henceforth, this study establishes a wireless underground transmission sensor that overcomes the distance limitation of underground transmission through a hop network. A series of feasibility tests were conducted on the wireless underground transmission sensor, including peer-to-peer transmission tests, multi-hop underground transmission tests, power management tests, and soil moisture measurement tests. Finally, field seepage tests were conducted to apply wireless underground transmission sensors to monitor the internal seepage water level before an earth dam failure. The findings show that wireless underground transmission sensors can achieve the monitoring of seepage water levels inside earth dams. In addition, the results supersede those of a conventional water level gauge. This could be crucial in early warning systems during the era of climate change, which has caused unprecedented flooding events.

The variation of riverbed material due to tropical storms in Shi-Wen River, Taiwan.

Taiwan, because of its location, is a flood prone region and is characterised by typhoons which brings about two-thirds to three quarters of the annual rainfall amount. Consequently, enormous flows result in rivers and entrain some fractions of the grains that constitute the riverbed. Hence, the purpose of the study is to quantify the impacts of these enormous flows on the distribution of grain size in riverbeds. The characteristics of riverbed material prior to and after the typhoon season are compared in Shi-Wen River located at southern Taiwan. These include grain size variation, bimodality, and roughness coefficient. A decrease (65%) and increase (50%) in geometric mean size of grains were observed for subsurface and surface bed material, respectively. Geometric standard deviation decreased in all sites after typhoon. Subsurface material was bimodal prior to typhoons and polymodal after. For surface material, modal class is in the gravel class, while after typhoons it shifts towards cobble class. The reduction in geometric mean resulted to a decrease in roughness coefficient by up to 30%. Finally, the relationship of Shields and Froude numbers are studied and a change in the bed form to antidunes and transition form is observed, respectively.

Prediction of missing flow records using multilayer perceptron and coactive neurofuzzy inference system.

Hydrological data are often missing due to natural disasters, improper operation, limited equipment life, and other factors, which limit hydrological analysis. Therefore, missing data recovery is an essential process in hydrology. This paper investigates the accuracy of artificial neural networks (ANN) in estimating missing flow records. The purpose is to develop and apply neural networks models to estimate missing flow records in a station when data from adjacent stations is available. Multilayer perceptron neural networks model (MLP) and coactive neurofuzzy inference system model (CANFISM) are used to estimate daily flow records for Li-Lin station using daily flow data for the period 1997 to 2009 from three adjacent stations (Nan-Feng, Lao-Nung and San-Lin) in southern Taiwan. The performance of MLP is slightly better than CANFISM, having R (2) of 0.98 and 0.97, respectively. We conclude that accurate estimations of missing flow records under the complex hydrological conditions of Taiwan could be attained by intelligent methods such as MLP and CANFISM.