eChapters

An efficient irrigation system implies effective transfer of water from the source to the filled with minimum possible loss. The objective of the efficiency concept is to identify the nature of water loss and to decide the type of improvements in the system.

Evaluation of performance in terms of efficiency is prerequisite for proper use of irrigation water.

Abstract

The anomalies observed in the reverine systems have been demonstrated to be the potential tool in deciphering the tectonically active zones. Such various drainage anomalies viz: radial drainages, anomalous compressed meanders, eyed drainage and the youthful floodplains observed in Annamali – Palani - Varushanad hill ranges of the Western Ghats of Tamil Nadu show that the N-S and NE-SW trending faults are tectonically active in the recent years.

Abstract

The Remote Sensing and the followed up field based investigations show that the Tamil Nadu coast, India, is facing series of geoenvironmental problems due to tectonic movements and morphodynamic processes. Such problems pose a challenge to the engineering geologists. The paper discusses such challenging engineering geological problems and plausible remedial measures.

In agricultural practices pesticide application is an inevitable factor for maintaining high crop productivity and is currently an integral part of modern farming. Pests (insects, microbes and weeds) cause approximately 20-30% loss in crop production so making pesticide use indispensable for crop protection.
 
Pesticide : A pesticide may be defined as ‘any agent or chemical or a mixture of chemicals/ substances intended to prevent, destroy, repel or irritate one or more pest populations causing economic loss in crop production’. Worldwide more than 450 active pesticide chemicals formulated into more than 10,000 commercial products are currently used by farmers. The most worrying aspect is that about two-third of the 800 chemicals tested so far have been found involved in causing or promoting tumor in rodents. Yet more than 50,000 synthetic chemicals/formulations remain untested so far.

Boost to crop production has been the ultimate goal of researchers involved in agriculture so as to feed the ever-increasing human and domestic herbivorous animal populations. Crop production is not only constrained by the limited inherent potential of available varieties currently in use, but is also largely affected by the limitations of soil nutrient availability and damages by diseases, insects and weeds. To counter later challenges, augmentation of soil nutrients through use of biofertilizers in integrated nutrient management programmes and restricting the use of chemical pesticide by integrating biopesticides in plant protection programmes has received much attention in recent years because of consumer’s preference for organic food.

Biotechnological research started in early seventies with some impressive discoveries in the field of genetics, microbiology, biochemistry, biology and chemical bioengineering. The amalgamation of these sciences particularly integration of their basic principles and applied aspects gave rise to a new field, completely diverse and versatile in nature, what we call today ‘biotechnology’. Originating from research laboratories in universities, biotechnology rapidly captured the attention of industries as the number of its potential products increased. This newly developed frontier science later found broad application in medical science, pharmaceutical industry, agriculture, veterinary sciences and chemical industry for the welfare of mankind. Biotechnology by definition means ‘any technique that uses living organism or a part thereof to make or modify a product, to improve plant or animal or to develop microorganisms for specific purposes’.

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.

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.

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.

Introduction

A system is a simplified version of reality and is defined as a collection of interrelated parts that work together. Most systems share certain common characteristics. These common characteristics include the following:
Systems have a structure that is defined by its parts and processes. Systems are generalizations of reality.
Systems tend to function in the same way as nature. This involves inputs getting changed in a process and realization of new outputs.
The various parts of a system have functional as well as structural relationships between each other.
Owing to the functional relationships that exist between the parts there is a flow and transfer of some type of energy and/or matter. In case of information systems there is a flow of information in the structure.
The parts that make up a system show certain degree of integration. In other words the parts work well together.

Introduction

In a technology that consistently maintains a fantastic development curve with capabilities doubling up year-to-year, freezing the hardware specification is not easy or very meaningful. GIS (not geography) owes to its genesis and progress to the developments in information technology front. It may be asserted that any and all upgrade in the hardware industry will only add to its own ability to effectively process more and more data and handle more complex routines. The personal computers of today are seen to be adequate to run typical GIS software. A reference hardware specification is discussed here. GIS software development is an involved process but then the software developers have made good use of hardware capabilities available and have released upgrades consistently. Hardware developments are such that they defy a static spcification. However a reference specification as on 2009. An overview of various software packages used in the past as well as present is attempted. ESRI released a new version in 2002 and claimed that their ArcGIS is an “integrated collection of GIS software products for building a complete GIS that enable users to deploy GIS functionality wherever it is needed, in desktops, servers, or custom applications; over the Web; or in the field.” The details are discussed below. While a number of software overviews are available, this text is not aimed at providing a tutorial to any of the software packages nor do the comparisons made are to promote any particular software. Similarly, it is presumed that the reader is familiar with the hardware elements and functionality. Lot of information about the software packages are available on their respective web sites and the reader is exhorted to look for current, additional and in depth information required in the pages. A brief discussion on Liveware or human resource can be found at the end of the chapter.

Introduction

All spatial data stored in a GIS are associated with a map projection, either implicitly (so-called “unprojected” data stored with raw longitude, latitude coordinates) or explicitly (where the data have been transformed into a known map projection). Map projections are a particularly important feature of spatial data because they introduce error and distortion, since a sphere cannot be transformed to a plane without some stretching and twisting of the sphere’s surface.

The typical GIS package supports anywhere from 5 to 50 map projection types, and most projections may be modified or customized by varying their parameters. This flexibility is important for two reasons First, in order to use two or more layers together in a GIS for visualization or analysis, all data must be projected in the same way. Second, certain types of analyses, such as the measurement of area, are only valid when using certain types of projections (and conversely, are completely wrong when using an inappropriate projection).

Introduction

Spatial variation structure of soil properties is a key input for digital soil mapping and generally determined through geostatistical techniques (Webster and Oliver 2007). In geostatistics, spatial variation is expressed by semivariogram ?(h) , which measures the average dissimilarity between data separated by a vector h. It is generally computed as half of the average squared difference between the components of data pairs:

Introduction

Data are the most critical element of a GIS. It is reported that more than two thirds of the time and expenditure to develop a GIS is spent on data collection and organization. Understanding and appreciating the criticality of data is of paramount significance to practice GIS. This chapter is complementary to database concepts chapter discussed in Level II and deals with the fundamentals to start with and the concepts of building an information organization.

Data - A Definition and Certain Points to Ponder

“Data” are defined as a body of facts or figures, which have been gathered systematically for one or more specific purposes.”

Introduction

The concept of database is the approach to information organization in computer-based data processing. (Date, 1995 and Everest, 1986).

A Geographic Information System is as good as the data contained in it. Accurate and efficiently organized data are critical to the durability of the system. Data collection, capture and organization will occupy much of the time in building a GIS solution. The analysis potential of a GIS will depend on the database. It is essential to understand database concepts to practice GIS. It is erroneous to view these GIS databases as mere stores of information. The database is used to abstract very specific sorts of information about reality and organize it in a way that will prove useful during subsequent analysis. The database should be viewed as a representation or model of the world developed for a very specific application.

Conversion of real world geographical variation into discrete objects is done through data models. They represent the linkage between the real world domain of geographic data and computer representation of these features. Data models discussed here are for representing the spatial information.

Introduction

Data input or capture is the operation of encoding the data and incorporating in the database. The creation of a clean error free digital database is a complex and critical task determining the very usefulness of a GIS. GIS data represent not only where the graphic or cartographic features occur, but also the associated attributes of them.

Data input to a geographical information system involves

(a) Entering the spatial data (digitizing);

(b) Entering the non-spatial, associated attributes and

(c) Linking the spatial to the non-spatial data.

At each stage necessary verification and checking procedures shall be deployed to ensure an error free database.