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"In 2011, massive flooding and inundation in the Chao Phraya River basin, in Thailand, causedserious damage to various activities for a prolonged period of time. Although snapshot images ofthe inundated area are available, detailed information including temporal changes of the inundatedareas and the relationship with meteorological and hydrological conditions are not welldocumented, particularly for the middle and upper sections of the basin. Therefore, we conductedan analysis using two types of satellite data, HJ-1A and Envisat, to better understand behavior ofthe large-scale inundation occurred in 2011, focusing on the middle section of the Chao PhrayaRiver basin. In the analysis, water surface in selected domains was extracted using the NDWI valuecalculated from HJ-1A data. The threshold value of the Envisat ASAR image was then adjusted sothat the inundated area estimated from Envisat gives the closest possible match with that estimatedfrom HJ-1A. Finally, the inundated area was estimated for the whole study domain based on thesame threshold value from the Envisat data. Results indicated that the inundated area began toextend along the Yom and Nan rivers in early August and continued to spread down to the NakhonSawan city area until October. A significant increase in inundated areas occurred between Sep. 2and Sep. l3, during which higher rainfall intensity was observed. Even after the water level inrivers receded below the bank-full elevation, large areas were left inundated along rivers,particularly over lowlying marsh and paddy fields. In addition, several areas located far fromrivers were also inundated, which was likely a consequence of water ponding in paddy fields."

"Nowadays, severe flooding frequently occurs in various parts of Thailand resulted from changesin climatic condition and land use patterns. The flooding has caused great damages to propertiesand lives and affects country economy. Experience from the most severe flooding in the northernand central regions of Thailand in the year 2011 reveals that reliable flood warning system is stilllagging. For flood warning purpose, it is necessary to have an accurate flood routing system. Thisstudy is aimed at developing mathematical models for flood routing so as to provide data for floodwarning. Two different models are developed, i.e., kinematic overland flow model and kinematicstream flow model. The finite element method with Galerkin’s weighted residual technique is usedin model development. The second order Runge-Kutta method is applied to solve the set ofdifferential equations obtained from finite element formulation. The developed models are appliedto simulate flows in the Wang river basin in the northern region of Thailand during July 1 -October 31, 2011 when severe flooding occurred in this region. Model calibration is made byadjusting some parameters in the models and comparing the obtained results with measured datarecorded by RID at 5 stream flow gauge stations along the Wang river. For correlation analysis.three statistical indices are determined, these include coefficient of determination, R2, Nash-Sutcliffe model efficiency coefficient, NSE, and coefficient of variation of the root mean squareerror, CV(RMSE). It is found that the model results at the upstream portion of the riversatisfactorily agree with the observed data, with the values of R2 greater than 0.55 and CV(RMSE)less than 0.57. For the downstream portion of the river, there are remarkable differences betweenthe model results and the observed data. The values of R2 are less than 0.35, CV(RMSE) greaterthan 0.76, and the NSE values are less than 0.16. This might be due to some errors in the inputdata, including rainfall pattern, topography, land use, river cross-sectional area, and water seepagealong the river. More detailed field investigation and model calibration are still needed."

"Parameter estimation is the process of using observations from a system to develop mathematical models that adequately represent the system dynamics. The assumed model consists of a finite set of parameters, the values of which are calculated using estimation techniques. Most of the techniques that exist are based on least-square minimization of error between the model response and actual system response. However, with the proliferation of high speed digital computers, elegant and innovative techniques like filter error method, H-infinity and Artificial Neural Networks are finding more and more use in parameter estimation problems.Modelling and Systems Parameter Estimation for Dynamic Systems presents a detailed examination of the estimation techniques and modeling problems. The theory is furnished with several illustrations and computer programs to promote better understanding of system modeling and parameter estimation. The material is presented in a way that makes for easy reading and enables the user to implement and execute the programs himself to gain first hand experience of the estimation process."

"Uncertainty and risk are integral to engineering because real systems have inherent ambiguities that arise naturally or due to our inability to model complex physics. The authors discuss probability theory, stochastic processes, estimation, and stochastic control strategies and show how probability can be used to model uncertainty in control and estimation problems. The material is practical and rich in research opportunities.The authors provide a comprehensive treatment of stochastic systems from the foundations of probability to stochastic optimal control. The book covers discrete- and continuous-time stochastic dynamic systems leading to the derivation of the Kalman filter, its properties, and its relation to the frequency domain Wiener filter as well as the dynamic programming derivation of the linear quadratic Gaussian (LQG) and the linear exponential Gaussian (LEG) controllers and their relation to H2 and H controllers and system robustness.Stochastic Processes, Estimation, and Control is divided into three related sections. First, the authors present the concepts of probability theory, random variables, and stochastic processes, which lead to the topics of expectation, conditional expectation, and discrete-time estimation and the Kalman filter. After establishing this foundation, stochastic calculus and continuous-time estimation are introduced. Finally, dynamic programming for both discrete-time and continuous-time systems leads to the solution of optimal stochastic control problems, resulting in controllers with significant practical application."

"Topics emphasized include nonparametric density estimation as an exploratory device plus the deeper models to which the exploratory analysis points, multi-dimensional data analysis, and analysis of remote sensing data, cancer progression, chaos theory, epidemiological modeling, and parallel based algorithms. New methods discussed are quick nonparametric density estimation based techniques for resampling and simulation based estimation techniques not requiring closed form solutions."

"Estimation of regression curve usually conducted using three methods; parametric method, non- parametric method, and semi-parametric methods..."