Preface The growing world water crisis is a failure of human development process. The failure might be due to non involvement of society in the management of water resources. But, the water management now requires an integrated involvement of many factors from technical to social. The solutions need to be sought in collaboration with people from a number of different disciplines including society as a stakeholder. The hydroinformatics dealt with water management process.  The hydroinformatics tools viz. simulation modeling, SCADA, remote sensing and GIS, artificial intelligence etc are now a days, generally used in the planning of water based systems. These are quite young technologies, but complex and so budding engineers could keep a safe distance from these courses. Number of books are available in this area but there is derth of comprehensive information of all these topics in a single book form. It is difficult to read and grasp all information within a short span of time. In this book, a wide and varied scope of hydroinformatics is concise in the simple language for easy understanding and remembrance of the readers. The aim of this book is to introduce the budding engineers with hydroinformatics and it’s tools for planning and management of water based systems. Each chapter describes basic preliminary information about the topic. This book will be helpful to undergraduate and postgraduate engineering students in preparation of the subject and research. This book might generate interest in the budding engineers in hydroinformatics. The intention of the authors in presenting this book is to have wide application of hydroinformatics. The authors would welcome suggestions from the readers for improvement of this book.

Water is a social requirement. Society now become aware about it’s dependence upon water and hence realized the importance of water in sustaining not only the development but also life. Water: too little, too much, too dirty; is general scenario in the world. But with increase in population water become limiting resource and is becoming scarcer and scarcer day by day. As water become scarcer commodity, the users (stakeholders) become more conscious regarding their share in water and ultimately this situation may lead to water conflicts. Earlier water related problems were generally tackled by hydro-engineers, but now in present climate changed scenario these problems go beyond the hydraulics and hydrology. Now a days politicians, ecologists, Non Government Organizations (NGOs), the media and the stakeholders etc are playing a crucial role in decision making in respect to water management issues. Now water projects are designed in reference to social context (Social context means cultural, economic, political, sociological, legal and other such aspects). When social factors are taken into consideration while tackling the water problems, it could not possible to satisfy all involved. Thus it is required to agree upon a compromise solution acceptable to all. Over recent few decades, information and communication technology (ICT) flourishes with great advances. It provides the ways to search for compromise solutions. In this chapter information, hydroinformatics, evolution of hydroinformatics and role of internet in hydroinformatics are discussed.

Data collection is a tedious and time consuming work, particularly from remote places. So many times the collected data is noisy. But data is the base for future planning and management of water resources. Planning based on noisy data could lead to failure. The modern data collection and subsequent control system i.e. telemetry and SCADA, are introduced in this chapter.

Modeling is heart of hydroinformatics. Simulation modeling became integrated part of modern science to justify feasibility of selected option among various competitive alternatives. Simulation modeling is a tool to evaluate the performance of a system, existing or proposed, under different configurations of interest and over long periods of real time. This chapter describes basic concepts, model simulation, model variables, modeling process, model performance etc. in brief.

In this era of computer, mathematical models become integral part of hydrologic studies due to advancement in soft technology. But question always arises as ‘Why should modeling?’ It was well expressed by Rosenblueth and Wiener (1945) as No substantial part of the universe is so simple that it can be grasped and controlled without abstraction. Abstraction consists in replacing the parts of the universe under consideration by a model of similar but simpler structure. Models, formal or intellectual on the one hand, or material on the other are thus a central necessity of scientific procedure.

Data mining is a powerful technology which helps in extracting future trends and behaviour from large databases. Thereby it helps decision makers to make knowledge-driven decisions. One common form of real-world data is time series. The suitable models are required to analyze time series data. This chapter provides a short introduction to stochastic system, uncertainty, basic statistics time series analysis and some stochastic models.

Artificial Intelligence (AI) technology is increasingly used in our everyday lives. It is used in a variety of industries from gaming, journalism/media, to finance, as well as in the research fields from water resources to robotics. In this chapter concept and applications of AI are discussed in brief. 6.1 Historical Background After World War II, a number of people worked independently on intelligent machines. The English mathematician Alan Turing was the first who explained the concept of intelligent machines through a lecture in 1947. He stated that intelligent machines could be developed by programming computers rather than by building machines. Since late 1950 many researchers are working on intelligent machines, mostly on programming computers

The artificial neural network (ANN), a data driven model, is inherently suited to the problems that are mathematically difficult to describe. An ANN is a flexible mathematical structure that is capable of identifying complex nonlinear relationships between input and output data sets, where explicit knowledge of the internal processes is not available. In this chapter general structure of ANN is described and also explained briefly with a case study. 7.1 What is Artificial Neural Network? An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous system, such as brain process the information. An ANN is an adaptive (Adaptive means that the system parameters are changed during operation i.e. training phase), most often nonlinear system that learns to perform a function (an input/output map) from data. In more practical term, neural networks are non-linear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find out patterns in the data.

Remote sensing is emerged as an important application of science in all fields for collection of information from a far. This chapter describes the fundamental concepts of remote sensing, electromagnetic radiation (EMR), the interactions between EMR and earth surface features, satellites etc. 8.1 What is Remote Sensing? Remote sensing is nothing but acquiring information about an object without touching the object itself. In other words, remote sensing is the acquisition of information about an object or phenomenon, without making physical contact with that object. In present era, the term generally refers to the use of aerial sensor technologies to detect and classify objects on the Earth by means of propagated signals. As remote sensing is obtaining information about objects without making physical contact with them, remote sensing can be described as ‘reconnaissance from a distance’, ‘teledetection’ or a form of the common phrase ‘look but don’t touch’.

More information, more accurate and timely decision. In present high tech world, the more information you have it is easier to make an accurate decision. The information comes in many different ways, from reports to digital photos. A new way to analyze data is a geographic information system (GIS). GIS allows to bring all types of data together based on the geographic and locational component of the data. GIS is a useful tool for seeking innovative ways to solve the problems. 9.1 What is GIS? A geographic information system (GIS) is a system designed to capture, store manipulate, analyze, manage and present all types of geographically referenced data. GIS can be defined as a computer-based system for digital entry, storage, transformation, analysis and display of spatial data. A geographic information system integrates hardware, software and geographically referenced data for further analysis. GIS allows users to interpret the georeferenced data in many ways that reveal relationships, patterns and trends.

Vegetation is a key component in hydrologic modeling. Transpiration, a component in hydrologic cycle, is contributed by vegetation. Most of the hydrologic model thus includes vegetation as a parameter. For accurate and appropriate model output, first requirement is good quality data. Most of the time, the data on vegetation is confusing, as in literature various researcher have used different values for common vegetation parameters. Here some common parameters of some important crops, which yield good quality results, are given. Kharif crops are generally sown during last week of May and first week of June, while rabi crop are sown during last week of November and first week of December. But this period will vary from region to region as well as depends on variety of crop.