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Integrating Multiple Sources Of Information For Improving Hydrological Modelling An Ensemble Approach
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Book Synopsis Integrating Multiple Sources of Information for Improving Hydrological Modelling: an Ensemble Approach by : Isnaeni Murdi Hartanto
Download or read book Integrating Multiple Sources of Information for Improving Hydrological Modelling: an Ensemble Approach written by Isnaeni Murdi Hartanto and published by CRC Press. This book was released on 2019-04-24 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: The availability of Earth observation and numerical weather prediction data for hydrological modelling and water management has increased significantly, creating a situation that today, for the same variable, estimates may be available from two or more sources of information. Yet, in hydrological modelling, usually, a particular set of catchment characteristics and input data is selected, possibly ignoring other relevant data sources. In this thesis, therefore, a framework is being proposed to enable effective use of multiple data sources in hydrological modelling. In this framework, each available data source is used to derive catchment parameter values or input time series. Each unique combination of catchment and input data sources thus leads to a different hydrological simulation result: a new ensemble member. Together, the members form an ensemble of hydrological simulations. By following this approach, all available data sources are used effectively and their information is preserved. The framework also accommodates for applying multiple data-model integration methods, e.g. data assimilation. Each alternative integration method leads to yet another unique simulation result. Case study results for a distributed hydrological model of Rijnland, the Netherlands, show that the framework can be applied effectively, improve discharge simulation, and partially account for parameter and data uncertainty.
Book Synopsis Integrating Data and Models for Sustainable Decision-making in Hydrology by : Lijing Wang
Download or read book Integrating Data and Models for Sustainable Decision-making in Hydrology written by Lijing Wang and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Climate change results in both long-term droughts and short-term extreme precipitation, which can significantly affect water quality and quantity. To make smart decisions about water resources under uncertain climates, it is important for scientists to convey accurate predictions of water systems to water resource managers. This requires integrating multiple geophysical, geochemical, and hydrologic datasets to build accurate hydrologic models and provide predictions of water flow and quality. However, the model-data integration process can be hindered by challenges such as complex hydrologic modeling, lack of geologically realistic models, and slow or ineffective model calibration methods. These challenges limit the use of model-data integration methods from theory to practice and make it difficult to translate hydrologic models into effective decisions. In this dissertation, we present new method developments for addressing model-data integration's challenges and provide real-world hydrologic examples of using the process of model-data integration. We start by introducing the model-data integration process and associated challenges in Chapter 1. In Chapter 2, we introduce a new geological interface modeling method to integrate multiple datasets and, most importantly, geological knowledge: a data-knowledge-driven trend surface analysis. We define different density functions for different information sources, and sample trend interfaces using the Metropolis-Hastings algorithm with stationary Gaussian field perturbations. This method works for both explicit and implicit interface modeling, where the key advance of the implicit model is to represent complex interfaces and geometries without heavy parameterization. We demonstrate our method in three different test cases: modeling stochastic interfaces of Greenland subglacial topography, magmatic intrusion, and palaeovalleys for groundwater mapping in South Australia. This new trend surface analysis tool is useful for building geological models and hydrostratigraphic layers for hydrologic site characterization. In Chapter 3, we design the hierarchical Bayesian formulation to invert both uncertain global and spatial variables hierarchically. We propose a machine learning-based inversion method that calculates summary statistics using machine learning to invert both linear and non-linear forward models. We also introduce a new local principal component analysis (local PCA) approach that provides a more efficient method for local inversion of large-scale spatial fields. In addition, we provide a likelihood-free inverse method using density estimators, using both traditional kernel density estimation and newly developed neural density estimation. To illustrate the hierarchical Bayesian formulation, one linear volume average inversion, and two non-linear hydrologic modeling cases are presented, including a 3D case study. This Chapter provides possible solutions to many model calibration challenges we face in model-data integration: hierarchical modeling, likelihood definitions, and effective calibration for large spatial fields. In Chapter 4 and Chapter 5, we show two real case studies of model-data integration. Chapter 4 examines the impact of beaver ponds on flow dynamics in a mountainous floodplain in Colorado using hydrologic modeling and model-data integration. The recovery of beavers in North America has been adapted as an ecosystem restoration tool to increase surface and groundwater storage and improve biodiversity on reach scales. We investigate the effects of beavers on hydrologic flows, particularly on the deep baseflow in aquifers, by constructing a 3D hydrologic floodplain model. We calibrate the model to the baseflow piezometer measurement using likelihood-free methods in Chapter 3. Our sensitivity analysis shows that beaver ponds increase the cumulative vertical flow from the fines to the gravel bed but have little effect on the deep underflow in the gravel bed aquifer, suggesting that beaver ponds are disconnected from the main downstream flow. This study aims to improve our understanding of the hydrologic consequences associated with the increasing use of beaver restoration as a climate adaptation strategy. In Chapter 5, we propose a statistical model for constructing 3D redox structures in Danish farmlands to address agricultural nitrogen pollution, which is a global problem that could be exacerbated by hydrologic shifts from climate change. The redox environment in the subsurface is essential for the natural removal of nitrate by denitrification. We combine the towed transient electromagnetic resistivity (tTEM) and redox boreholes to model 3D redox architecture stochastically. However, tTEM survey and redox boreholes are often non-colocated. To address this issue, we perform geostatistical simulations to generate multiple resistivity data colocated with redox boreholes. We then use a statistical learning method, multinomial logistic regression, to predict multiple 3D redox architectures given the uncertain surrounding resistivity structures. We reveal the statistically significant resistivity structures for redox predictions and formulate an inverse problem to better match the redox borehole data using the local PCA method in Chapter 3. These two chapters provide two alternative approaches for providing hydrologic predictions: physics-based modeling or statistical modeling. In Chapter 6, we introduce a fast surrogate flow and transport model to evaluate the climate impact on groundwater contamination. The surrogate modeling approach is applied at the Department of Energy's Savannah River Site F-Area, which contains nuclear wastewater. We present two time-dependent neural network architectures: U-FNO-3D and U-FNO-2D, each with a different approach to incorporating the time dimension. Furthermore, we integrate a custom loss function that takes both data-driven factors and physical boundary constraints into account. This chapter offers a solution to reduce the computational cost of numerical modeling, which is critical in making timely decisions that bridge science and practical applications. This dissertation provides novel methods for geological modeling and model calibration and applies them to real-world problems, highlighting the importance of both method development and practical implementation in addressing hydrologic challenges posed by uncertain climates.
Book Synopsis Hydrological Modeling by : Ramakar Jha
Download or read book Hydrological Modeling written by Ramakar Jha and published by Springer Nature. This book was released on 2022-02-05 with total page 517 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book carefully considers hydrological models which are essential for predicting floods, droughts, soil moisture estimation, land use change detection, geomorphology and water structures. The book highlights recent advances in the area of hydrological modelling in the Ganga Basin and other internationally important river basins. The impact of climate change on water resources is a global concern. Water resources in many countries are already stressed, and climate change along with burgeoning population, rising standard of living and increasing demand are adding to the stress. Furthermore, river basins are becoming less resilient to climatic vagaries. Fundamental to addressing these issues is hydrological modelling which is covered in this book. Integrated water resources management is vital to ensure water and food security. Integral to the management is groundwater and solute transport, and this book encompasses tools that will be useful to mitigate the adverse consequences of natural disasters.
Book Synopsis Mathematical Models of Small Watershed Hydrology and Applications by : Vijay P. Singh
Download or read book Mathematical Models of Small Watershed Hydrology and Applications written by Vijay P. Singh and published by Water Resources Publication. This book was released on 2002 with total page 984 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive account of some of the most popular models of small watershed hydrology and application ~~ of interest to all hydrologic modelers and model users and a welcome and timely edition to any modeling library
Book Synopsis Integrating Multiscale Observations of U.S. Waters by : National Research Council
Download or read book Integrating Multiscale Observations of U.S. Waters written by National Research Council and published by National Academies Press. This book was released on 2008-05-16 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: Water is essential to life for humans and their food crops, and for ecosystems. Effective water management requires tracking the inflow, outflow, quantity and quality of ground-water and surface water, much like balancing a bank account. Currently, networks of ground-based instruments measure these in individual locations, while airborne and satellite sensors measure them over larger areas. Recent technological innovations offer unprecedented possibilities to integrate space, air, and land observations to advance water science and guide management decisions. This book concludes that in order to realize the potential of integrated data, agencies, universities, and the private sector must work together to develop new kinds of sensors, test them in field studies, and help users to apply this information to real problems.
Download or read book Treatise on Water Science written by and published by Newnes. This book was released on 2010-09-01 with total page 2131 pages. Available in PDF, EPUB and Kindle. Book excerpt: Water quality and management are of great significance globally, as the demand for clean, potable water far exceeds the availability. Water science research brings together the natural and applied sciences, engineering, chemistry, law and policy, and economics, and the Treatise on Water Science seeks to unite these areas through contributions from a global team of author-experts. The 4-volume set examines topics in depth, with an emphasis on innovative research and technologies for those working in applied areas. Published in partnership with and endorsed by the International Water Association (IWA), demonstrating the authority of the content Editor-in-Chief Peter Wilderer, a Stockholm Water Prize recipient, has assembled a world-class team of volume editors and contributing authors Topics related to water resource management, water quality and supply, and handling of wastewater are treated in depth
Book Synopsis Flood Forecasting Using Machine Learning Methods by : Fi-John Chang
Download or read book Flood Forecasting Using Machine Learning Methods written by Fi-John Chang and published by MDPI. This book was released on 2019-02-28 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays, the degree and scale of flood hazards has been massively increasing as a result of the changing climate, and large-scale floods jeopardize lives and properties, causing great economic losses, in the inundation-prone areas of the world. Early flood warning systems are promising countermeasures against flood hazards and losses. A collaborative assessment according to multiple disciplines, comprising hydrology, remote sensing, and meteorology, of the magnitude and impacts of flood hazards on inundation areas significantly contributes to model the integrity and precision of flood forecasting. Methodologically oriented countermeasures against flood hazards may involve the forecasting of reservoir inflows, river flows, tropical cyclone tracks, and flooding at different lead times and/or scales. Analyses of impacts, risks, uncertainty, resilience, and scenarios coupled with policy-oriented suggestions will give information for flood hazard mitigation. Emerging advances in computing technologies coupled with big-data mining have boosted data-driven applications, among which Machine Learning technology, with its flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications. This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative solutions from a local, regional or global perspective.
Book Synopsis Handbook of HydroInformatics by : Saeid Eslamian
Download or read book Handbook of HydroInformatics written by Saeid Eslamian and published by Elsevier. This book was released on 2022-12-06 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Machine Learning Techniques includes the theoretical foundations of modern machine learning, as well as advanced methods and frameworks used in modern machine learning. Handbook of HydroInformatics, Volume II: Advanced Machine Learning Techniques presents both the art of designing good learning algorithms, as well as the science of analyzing an algorithm's computational and statistical properties and performance guarantees. The global contributors cover theoretical foundational topics such as computational and statistical convergence rates, minimax estimation, and concentration of measure as well as advanced machine learning methods, such as nonparametric density estimation, nonparametric regression, and Bayesian estimation; additionally, advanced frameworks such as privacy, causality, and stochastic learning algorithms are also included. Lastly, the volume presents Cloud and Cluster Computing, Data Fusion Techniques, Empirical Orthogonal Functions and Teleconnection, Internet of Things, Kernel-Based Modeling, Large Eddy Simulation, Patter Recognition, Uncertainty-Based Resiliency Evaluation, and Volume-Based Inverse Mode. This is an interdisciplinary book, and the audience includes postgraduates and early-career researchers interested in: Computer Science, Mathematical Science, Applied Science, Earth and Geoscience, Geography, Civil Engineering, Engineering, Water Science, Atmospheric Science, Social Science, Environment Science, Natural Resources, Chemical Engineering. Key insights from 24 contributors in the fields of data management research, climate change and resilience, insufficient data problem, etc. Offers applied examples and case studies in each chapter, providing the reader with real world scenarios for comparison. Defines both the designing of good learning algorithms, as well as the science of analyzing an algorithm's computational and statistical properties and performance guarantees.
Book Synopsis Floods and Landslides: Integrated Risk Assessment by : Riccardo Casale
Download or read book Floods and Landslides: Integrated Risk Assessment written by Riccardo Casale and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: A review of such natural disasters as floods and landslides, highlighting the possibility of safe and correct land planning and management by means of a global approach to territory. Since the events deriving from slope and fluvial dynamics are commonly triggered by the same factor, occur at the same time and are closely related, this book analyses floods and slope stability phenomena as different aspects of the same dynamic system: the drainage basin.
Book Synopsis Hydrological Data Driven Modelling by : Renji Remesan
Download or read book Hydrological Data Driven Modelling written by Renji Remesan and published by Springer. This book was released on 2014-11-03 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/artificial intelligence techniques have come to be used for time series modeling. However, though initial studies have shown this approach to be effective, there are still concerns about their accuracy and ability to make predictions on a selected input space.
Book Synopsis Improved Data Uncertainty Handling in Hydrologic Modeling and Forecasting Applications by : Hongli Liu
Download or read book Improved Data Uncertainty Handling in Hydrologic Modeling and Forecasting Applications written by Hongli Liu and published by . This book was released on 2019 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: In hydrologic modeling and forecasting applications, many steps are needed. The steps that are relevant to this thesis include watershed discretization, model calibration, and data assimilation. Watershed discretization separates a watershed into homogeneous computational units for depiction in a distributed hydrologic model. Objective identification of an appropriate discretization scheme remains challenging in part because of the lack of quantitative measures for assessing discretization quality, particularly prior to simulation. To solve this problem, this thesis contributes to develop an a priori discretization error metrics that can quantify the information loss induced by watershed discretization without running a hydrologic model. Informed by the error metrics, a two-step discretization decision-making approach is proposed with the advantages of reducing extreme errors and meeting user-specified discretization error targets. In hydrologic model calibration, several uncertainty-based calibration frameworks have been developed to explicitly consider different hydrologic modeling errors, such as parameter errors, forcing and response data errors, and model structure errors. This thesis focuses on climate and flow data errors. The common way of handling climate and flow data uncertainty in the existing calibration studies is perturbing observations with assumed statistical error models (e.g., addictive or multiplicative Gaussian error model) and incorporating them into parameter estimation by integration or repetition with multiple climate and (or) flow realizations. Given the existence of advanced climate and flow data uncertainty estimation methods, this thesis proposes replacing assumed statistical error models with physically-based (and more realistic and convenient) climate and flow ensembles. Accordingly, this thesis contributes developing a climate-flow ensemble based hydrologic model calibration framework. The framework is developed through two stages. The first stage only considers climate data uncertainty, leading to the climate ensemble based hydrologic calibration framework. The framework is parsimonious and can utilize any sources of historical climate ensembles. This thesis demonstrates the method of using the Gridded Ensemble Precipitation and Temperature Estimates dataset (Newman et al., 2015), referred to as N15 here, to derive precipitation and temperature ensembles. Assessment of this framework is conducted using 30 synthetic experiments and 20 real case studies. Results show that the framework generates more robust parameter estimates, reduces the inaccuracy of flow predictions caused by poor quality climate data, and improves the reliability of flow predictions. The second stage adds flow ensemble to the previously developed framework to explicitly consider flow data uncertainty and thus completes the climate-flow ensemble based calibration framework. The complete framework can work with likelihood-free calibration methods. This thesis demonstrates the method of using the hydraulics-based Bayesian rating curve uncertainty estimation method (BaRatin) (Le Coz et al., 2014) to generate flow ensemble. The continuous ranked probability score (CRPS) is taken as an objective function of the framework to compare the scalar model prediction with the measured flow ensemble. The framework performance is assessed based on 10 case studies. Results show that explicit consideration of flow data uncertainty maintains the accuracy and slightly improves the reliability of flow predictions, but compared with climate data uncertainty, flow data uncertainty plays a minor role of improving flow predictions. Regarding streamflow forecasting applications, this thesis contributes by improving the treatment of measured climate data uncertainty in the ensemble Kalman filter (EnKF) data assimilation. Similar as in model calibration, past studies usually use assumed statistical error models to perturb climate data in the EnKF. In data assimilation, the hyper-parameters of the statistical error models are often estimated by a trial-and-error tuning process, requiring significant analyst and computational time. To improve the efficiency of climate data uncertainty estimation in the EnKF, this thesis proposes the direct use of existing climate ensemble products to derive climate ensembles. The N15 dataset is used here to generate 100-member precipitation and temperature ensembles. The N15 generated climate ensembles are compared with the carefully tuned hyper-parameter generated climate ensembles in ensemble flow forecasting over 20 catchments. Results show that the N15 generated climate ensemble yields improved or similar flow forecasts than hyper-parameter generated climate ensembles. Therefore, it is possible to eliminate the time-consuming climate relevant hyper-parameter tuning from the EnKF by using existing ensemble climate products without losing flow forecast performance. After finishing the above research, a robust hydrologic modeling approach is built by using the thesis developed model calibration and data assimilation methods. The last contribution of this thesis is validating such a robust hydrologic model in ensemble flow forecasting via comparison with the use of traditional multiple hydrologic models. The robust single-model forecasting system considers parameter and climate data uncertainty and uses the N15 dataset to perturb historical climate in the EnKF. In contrast, the traditional multi-model forecasting system does not consider parameter and climate data uncertainty and uses assumed statistical error models to perturb historical climate in the EnKF. The comparison study is conducted on 20 catchments and reveal that the robust single hydrologic model generates improved ensemble high flow forecasts. Therefore, robust single model is definitely an advantage for ensemble high flow forecasts. The robust single hydrologic model relieves modelers from developing multiple (and often distributed) hydrologic models for each watershed in their operational ensemble prediction system.
Book Synopsis Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrologic and Hydraulic Models by : Maurizio Mazzoleni
Download or read book Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrologic and Hydraulic Models written by Maurizio Mazzoleni and published by CRC Press. This book was released on 2017-03-16 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, the continued technological advances have led to the spread of low-cost sensors and devices supporting crowdsourcing as a way to obtain observations of hydrological variables in a more distributed way than the classic static physical sensors. The main advantage of using these type of sensors is that they can be used not only by technicians but also by regular citizens. However, due to their relatively low reliability and varying accuracy in time and space, crowdsourced observations have not been widely integrated in hydrological and/or hydraulic models for flood forecasting applications. Instead, they have generally been used to validate model results against observations, in post-event analyses. This research aims to investigate the benefits of assimilating the crowdsourced observations, coming from a distributed network of heterogeneous physical and social (static and dynamic) sensors, within hydrological and hydraulic models, in order to improve flood forecasting. The results of this study demonstrate that crowdsourced observations can significantly improve flood prediction if properly integrated in hydrological and hydraulic models. This study provides technological support to citizen observatories of water, in which citizens not only can play an active role in information capturing, evaluation and communication, leading to improved model forecasts and better flood management.
Book Synopsis Improved Hydrological Understanding of a Semi-Arid Subtropical Transboundary Basin Using Multiple Techniques - The Incomati River Basin by : Saraiva Okello
Download or read book Improved Hydrological Understanding of a Semi-Arid Subtropical Transboundary Basin Using Multiple Techniques - The Incomati River Basin written by Saraiva Okello and published by CRC Press. This book was released on 2019-05-13 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study aims at improving the hydrological process understanding of the semi-arid and transboundary Incomati river basin to enable better water management. Comprehensive statistical and trend analysis of rainfall and streamflow were conducted, and the Indicators of Hydrological Alteration tool was deployed to describe the streamflow regime and trends over time. Land use and land cover change, particularly the conversion of natural vegetation into forest plantation, the expansion of irrigated agriculture and the flow regulation due to dam operation were identified as critical drivers of flow regime alteration. Hydrograph separation using long-term hydrochemical data at seasonal scale, and hydrochemical and isotope data at event scale were performed to quantify runoff components. A novel methodology to calibrate recursive digital filters using routinely collected water quality data was developed and tested in the catchment. This method allows for estimation of daily baseflow from readily available daily streamflow data. Dominant runoff generation zones were mapped using the Height Above Nearest Drainage approach. The hydrological model STREAM was then employed, informed by the runoff generation zones mapping and the process understanding gained in the catchment, as well as remote sensing data. The study provides the basis for better operational water management in the catchment.
Book Synopsis Advances In Data-based Approaches For Hydrologic Modeling And Forecasting by : Bellie Sivakumar
Download or read book Advances In Data-based Approaches For Hydrologic Modeling And Forecasting written by Bellie Sivakumar and published by World Scientific. This book was released on 2010-08-10 with total page 542 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprehensively accounts the advances in data-based approaches for hydrologic modeling and forecasting. Eight major and most popular approaches are selected, with a chapter for each — stochastic methods, parameter estimation techniques, scaling and fractal methods, remote sensing, artificial neural networks, evolutionary computing, wavelets, and nonlinear dynamics and chaos methods. These approaches are chosen to address a wide range of hydrologic system characteristics, processes, and the associated problems. Each of these eight approaches includes a comprehensive review of the fundamental concepts, their applications in hydrology, and a discussion on potential future directions.
Book Synopsis Hydrological Uncertainty Quantification and Propagation in Multimodel Approaches by : Edom Melesse Moges
Download or read book Hydrological Uncertainty Quantification and Propagation in Multimodel Approaches written by Edom Melesse Moges and published by . This book was released on 2018 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model uncertainties and inaccuracies can limit the application of hydrological modeling as decision making tool. Analysis and insight derived from uncertain models can significantly undermine the implication of their results, recommendations and conclusions. This thesis intends to deal with the various sources of uncertainty in hydrological modeling, particularly multi-modeling approaches, by using different statistical, computational and physically-based diagnostic measures. The uncertainty and the proposed approaches are evaluated using various hydrologic problems including -- extreme event frequency analysis, rainfall-runoff modeling, and coupled surface and subsurface models. First, the significance of model averaging, particularly Bayesian Model Averaging (BMA), is demonstrated by exploring extensive data, fundamental theory, and systematic diagnostic measures. Second, the study integrated hydrological signature measures and a multi model integration approach - Hierarchical Mixture of Experts (HME), in order to reduce structural uncertainty. Third, the study developed uncertainty quantification and propagation framework for coupled hydrological models that can readily be transferred to other coupled models. Using the framework, the study explored uncertainty propagation and their interplay in coupled hydrological models. The findings from this study -- in terms of developing a systematic uncertainty quantification framework and model diagnostic approaches -- are expected to improve the applications of hydrological and environmental models in understanding the underlying physical processes and making improved predictions.
Book Synopsis AGU 2004 Joint Assembly by : American Geophysical Union. Joint Assembly
Download or read book AGU 2004 Joint Assembly written by American Geophysical Union. Joint Assembly and published by . This book was released on 2004 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Dissertation Abstracts International by :
Download or read book Dissertation Abstracts International written by and published by . This book was released on 2007 with total page 924 pages. Available in PDF, EPUB and Kindle. Book excerpt:
