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Diop, LamineAssessing and predicting stream-flow at different time scales in the context of climate change: Case of the upper Senegal River basin
Doctor of Philosophy, The Ohio State University, 2017, Food, Agricultural and Biological Engineering
The objectives of this research were to: (i) investigate different aspects of long-term trend detection in monthly, seasonal, and annual streamflow; (ii) assess climate change impacts on the upper Senegal River basin stream-flow; and (iii) evaluate data driven models to forecast daily stream at the upper Senegal River basin. A preliminary study investigated long-term trends on stream-flow at the upper Senegal River basin. Results showed a trend of decreasing annual stream flow; but this was not significant at p < 0.05 for the whole period. However, when integrating various temporal breaking points, a decreasing trend was significant before the first breaking point (1976) but was reversed for the period of 1976 to 1993. For the monthly series, all months exhibit a non-significant decreasing trend except for the month of June that had an increasing trend. The seasonal series showed a decreasing trend that was significant (p <0.05) at MAMJ season. The extremely low and high daily streamflow had significant positive and negative trends, respectively. This research used five General Circulation Models (CNRM, CSIRO, HadGEM2-CC, HadGEM2-ES, and MIROC5), two RCP (RCP4.5 and RCP8.5) scenarios, and the GR4J hydrological model to evaluate the impact of climate change on stream flow in the near future. The results showed that for all models, there were simulated increases in mean monthly temperature under the RCP 4.5 and RCP 8.5. Increases of temperature ranged from 0.54° C to 2.32° C under RCP 4.5 scenario and from 1.12° C to 2.78° C under RCP8.5. However, models were not consistently in agreement in the direction and magnitude of future precipitation changes for monthly rainfall. Some models predicted an increase while other a decrease. The multi-model ensemble projected a decrease of rainfall for all months except for September. The greatest precipitation increases were in September, at 15.6 mm and 10.1 mm with RCP4.5 and RCP8.5, respectively. The greatest precipitation decreases were between June to August for both scenarios with a maximum in July under RCP 4.5 (-9.53 mm) and RCP 8.5 (-20.1mm). Compared to the 1971 – 2000 reference period, results showed a decrease in annual stream-flow of 2.9% and 7.7% under RCP4.5 and RCP8.5 scenarios, respectively. Monthly variations showed a decrease of stream-flow in wet season for both scenarios. This research verified the utility of the support vector regression (SVR) model and generalized regression neural network (GRNN) to predict one day ahead, the daily river flow in the upper Senegal River basin at the Bafing Makana station in West Africa. Two modeling scenarios were considered: (A): where only stream-flow data were used as an input via antecedent values and (B): where rainfall, evapotranspiration and stream-flow data are used. The results showed that the accuracy of the models varied by scenario. Combining the stream-flow data with rainfall and evapotranspiration can substantially improve the accuracy of the two models to predict one-day ahead stream-flow. A comparison of the optimal SVR and the GRNN models indicated that SVR model had superior performance compared to the GRNN.

Committee:

Larry C Brown, PhD (Advisor)

Subjects:

Agricultural Engineering

Keywords:

climate change, RCP, Support vector regression, generalized regression neural networks, trend test, change point, Senegal River basin

Gummadi, JayaramA Comparison of Various Interpolation Techniques for Modeling and Estimation of Radon Concentrations in Ohio
Master of Science in Engineering, University of Toledo, 2013, Engineering (Computer Science)
Radon-222 and its parent Radium-226 are naturally occurring radioactive decay products of Uranium-238. The US Environmental Protection Agency (USEPA) attributes about 10 percent of lung cancer cases that is `around 21,000 deaths per year’ in the United States, caused due to indoor radon. The USEPA has categorized Ohio as a Zone 1 state (i.e. the average indoor radon screening level greater than 4 picocuries per liter). In order to implement preventive measures, it is necessary to know radon concentration levels in all the zip codes of a geographic area. However, it is not possible to survey all the zip codes, owing to reasons such as inapproachability. In such places where radon data are unavailable, several interpolation techniques are used to estimate the radon concentrations. This thesis presents a comparison between recently developed interpolation techniques to new techniques such as Support Vector Regression (SVR), and Random Forest Regression (RFR). Recently developed interpolation techniques include Artificial Neural Network (ANN), Knowledge Based Neural Networks (KBNN), Correction-Based Artificial Neural Networks (CBNN) and the conventional interpolation techniques such as Kriging, Local Polynomial Interpolation (LPI), Global Polynomial Interpolation (GPI) and Radial Basis Function (RBF) using the K-fold cross validation method.

Committee:

William Acosta (Committee Chair); Vijay Devabhaktuni (Committee Co-Chair); Ashok Kumar (Committee Member); Rob Green (Committee Member)

Subjects:

Computer Science

Keywords:

artificial neural networks; cross-validation; correction based artificial neural networks; prior knowledge input; source difference; space-mapped neural networks; support vector regression; radon; random forest regression

Dougherty, Andrew W.Intelligent Design of Metal Oxide Gas Sensor Arrays Using Reciprocal Kernel Support Vector Regression
Doctor of Philosophy, The Ohio State University, 2010, Physics

Metal oxides are a staple of the sensor industry. The combination of their sensitivity to a number of gases, and the electrical nature of their sensing mechanism, make the particularly attractive in solid state devices. The high temperature stability of the ceramic material also make them ideal for detecting combustion byproducts where exhaust temperatures can be high. However, problems do exist with metal oxide sensors. They are not very selective as they all tend to be sensitive to a number of reduction and oxidation reactions on the oxide’s surface. This makes sensors with large numbers of sensors interesting to study as a method for introducing orthogonality to the system. Also, the sensors tend to suffer from long term drift for a number of reasons.

In this thesis I will develop a system for intelligently modeling metal oxide sensors and determining their suitability for use in large arrays designed to analyze exhaust gas streams. It will introduce prior knowledge of the metal oxide sensors’ response mechanisms in order to produce a response function for each sensor from sparse training data. The system will use the same technique to model and remove any long term drift from the sensor response. It will also provide an efficient means for determining the orthogonality of the sensor to determine whether they are useful in gas sensing arrays.

The system is based on least squares support vector regression using the reciprocal kernel. The reciprocal kernel is introduced along with a method of optimizing the free parameters of the reciprocal kernel support vector machine. The reciprocal kernel is shown to be simpler and to perform better than an earlier kernel, the modified reciprocal kernel. Least squares support vector regression is chosen as it uses all of the training points and an emphasis was placed throughout this research for extracting the maximum information from very sparse data.

The reciprocal kernel is shown to be effective in modeling the sensor responses in the time, gas and temperature domains, and the dual representation of the support vector regression solution is shown to provide insight into the sensor’s sensitivity and potential orthogonality. Finally, the dual weights of the support vector regression solution to the sensor’s response are suggested as a fitness function for a genetic algorithm, or some other method for efficiently searching large parameter spaces.

Committee:

Bruce Patton, PhD (Advisor); Ralf Bundschuh, PhD (Committee Member); David Stroud, PhD (Committee Member); Patricia Morris, PhD (Committee Member); Edward Overman, PhD (Committee Member)

Subjects:

Artificial Intelligence; Materials Science; Physics

Keywords:

support vector regression; metal oxide; sensor arrays