An entropy method for floodplain monitoring network design

In recent years an increasing number of flood-related fatalities has highlighted the necessity of improving flood risk management to reduce human and economic losses. In this framework, monitoring of flood-prone areas is a key factor for building a resilient environment. In this paper a method for designing a floodplain monitoring network is presented. A redundant network of cheap wireless sensors (GridStix) measuring water depth is considered over a reach of the River Dee (UK), with sensors placed both in the channel and in the floodplain. Through a Three Objective Optimization Problem (TOOP) the best layouts of sensors are evaluated, minimizing their redundancy, maximizing their joint information content and maximizing the accuracy of the observations. A simple raster-based inundation model (LISFLOOD-FP) is used to generate a synthetic GridStix data set of water stages. The Digital Elevation Model (DEM) that is used for hydraulic model building is the globally and freely available SRTM DEM

Development of watershed-based modeling approach to pollution source identification

Identification of unknown pollution sources is essential to environmental protection and emergency response. A review of recent publications in source identification revealed that there are very limited numbers of research in modeling methods for rivers. What’s more, the majority of these attempts were to find the source strength and release time, while only a few of them discussed how to identify source locations. Comparisons of these works indicated that a combination of biological, mathematical and geographical method could effectively identify unknown source area(s), which was a more practical trial in a watershed. This thesis presents a watershed-based modeling approach to identification of critical source area. The new approach involves (1) identification of pollution source in rivers using a moment-based method and (2) identification of critical source area in a watershed using a hydrograph-based method and high-resolution radar rainfall data. In terms of the moment-based method, the first two moment equations are derived through the Laplace transform of the Variable Residence Time (VART) model. The first moment is used to determine the source location, while the second moment can be employed to estimate the total mass of released pollutant. The two moment equations are tested using conservative tracer injection data collected from 23 reaches of five rivers in Louisiana, USA, ranging from about 3km to 300 km. Results showed that the first moment equation is able to predict the pollution source location with a percent error of less than 18% in general. The predicted total mass has a larger percent error, but a correction could be added to reduce the error significantly. Additionally, the moment-based method can be applied to identify the source location of reactive pollutants, provided that the special and temporal concentrations are recorded in downstream stations. In terms of the hydrograph-based method, observed hydrographs corresponding to pollution events can be utilized to identify the critical source area in a watershed. The time of concentration could provide a unique fingerprint for each subbasin in the watershed. The observation of abnormally high bacterial levels along with high resolution radar rainfall data can be used to match the most possible storm events and thus the critical source area

Identification of Flash floods using Soil Flux and CO2: An implementation of Neural Network with Less False Alarm Rate

Flash floods are very sudden and abrupt and are the major root cause of casualties and loss of infrastructure. Flash floods can be regarded as the topmost natural disasters in many countries. Usually floods are due to high precipitation, wind velocity, water wave current and melting of ice bergs. Diversified strategies have been designed and applied to identify the flash floods. Mainly dozen of sensors have been utilized to detect the flash floods like upstream level, rainfall intensity, run-off magnitude, run-off speed, color of the water, precipitation velocity, pressure, temperature, wind speed, wave current pattern and cloud to ground (CG flashes). Ultrasonic and passive infrared (PIR) sensors have also been utilized for this purpose. Sensors generate high amount of fake alerts due to the incompetent algorithms. In our research we have proposed a novel approach analysis of soil flux depicting atmospheric carbon dioxide level as the plants take smaller amount of water from the soil due to the heightened levels of carbon dioxide. Due to this newly discovered research the soil is saturated abruptly causes more floods and run-offs. In our research we have reduced the false alarms and reduced the false alarms by using scaled conjugate gradient back propagation. Simulation results showed that scaled conjugate gradient propagation performed better than the other previous methods

Optimization of rain gauge network in Johor using hybrid particle swarm optimization and simulated annealing

An optimal design of rain gauge network is important as it produces fast, accurate and important rainfall data used in designing an effective and economic hydraulic structure for flood control. The use of inaccurate rainfall data may result in significant design errors in other water resources project. The objective of this study is to determine the optimal number and location of the rain gauge network in Johor by using geostatistical method integrated with hybrid method consisting of simulated annealing and particle swarm optimization. This study also explored and compared the results of all existing methods namely the coefficient of variations, the maximum covering location problems and geostatistical method with the proposed model. The use of methods such as maximal covering location problem and coefficient of variation can only provide the figure for number of rain gauge stations but not the optimal location for the stations. The geostatistics method however, can provide the optimal number of rain gauge station and its location through the minimum variant value. The integration of geostatistics with hybrid methods comprised of simulated annealing method and particle swarm optimization is successful in providing the optimum number and location of the stations. In order to identify the effect of rain gauge station locations toward rainfall data, this study considered the repositioning of the existing rain gauge into new locations to improve their effectiveness and reduce the error. The analysis analysed the density of the rain gauge, daily rainfall data from 1977 to 2008, latitude and longitude of the rain gauge location, elevation, humidity, wind speed, temperature and solar radiation to determine the new optimal network design for the rain gauge network. The minimum value of estimated variance produced by the proposed method indicates that the method is successful in determining the optimal rain gauge network from the existing 84 rain gauges in Johor. Relocation of all 84 rain gauge stations to new locations give better results in terms of the estimated variance value but, it is not necessary to relocate all of the stations due to the expensive costs. Therefore, the location of the station also influences the result. In this study, hybrid simulated annealing and particle swarm optimization as an optimization method successfully determined the optimal rain gauges network in Johor. In conclusion, this study has shown that a well-design rain gauge network will help to provide essential input for effective planning, designing and managing of water resources project such as flood frequency analysis and forecasting