Data-Driven Modeling: Using MATLAB® in Water Resources and by Shahab Araghinejad

By Shahab Araghinejad

“Data-Driven Modeling: utilizing MATLAB® in Water assets and Environmental Engineering” presents a scientific account of significant suggestions and methodologies for data-driven versions and provides a unified framework that makes the topic extra obtainable to and appropriate for researchers and practitioners. It integrates vital theories and purposes of data-driven versions and makes use of them to house quite a lot of difficulties within the box of water assets and environmental engineering reminiscent of hydrological forecasting, flood research, water caliber tracking, regionalizing climatic info, and normal functionality approximation.
The ebook offers the statistical-based versions together with easy statistical research, nonparametric and logistic regression tools, time sequence research and modeling, and aid vector machines. It additionally offers with the research and modeling according to synthetic intelligence concepts together with static and dynamic neural networks, statistical neural networks, fuzzy inference structures, and fuzzy regression. The ebook additionally discusses hybrid types in addition to multi-model info fusion to wrap up the coated types and strategies.
The resource documents of quite basic and complex courses demonstrating easy methods to use the types are provided including sensible suggestion on tips on how to top practice them. The courses, that have been built utilizing the MATLAB® unified platform, are available on extras.springer.com.
The major viewers of this publication comprises graduate scholars in water assets engineering, environmental engineering, agricultural engineering, and normal assets engineering. This ebook can be tailored to be used as a senior undergraduate and graduate textbook by means of targeting chosen issues. then again, it might probably even be used as a precious source e-book for working towards engineers, consulting engineers, scientists and others fascinated with water assets and environmental engineering.

Show description

Read or Download Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering PDF

Similar hydrology books

Aquatic Chemical Kinetics Reaction Rates of Processes in Natural Waters

Aquatic Chemistry An advent Emphasizing Chemical Equilibria in ordinary Waters moment variation Edited through Werner Stumm and James J. Morgan This moment version of the well known vintage unites innovations, functions, and strategies with the transforming into quantities of information within the box. accelerated therapy is on the market on steady-state and dynamic types making use of mass-balance ways and kinetic info.

Hydrology and Water Resources of India

India is endowed with diversified topographical positive aspects, equivalent to excessive mountains, vast plateaus, and broad plains traversed by means of effective rivers. Water is a vital enter within the socio-economic improvement of a state. In India, this dependence is much more obvious, as 70% of her inhabitants relies on agriculture.

Neural Networks for Hydrological Modeling

A brand new method of the fast-developing international of neural hydrological modelling, this booklet is vital studying for lecturers and researchers within the fields of water sciences, civil engineering, hydrology and actual geography. each one bankruptcy has been written via a number of eminent specialists operating in a number of fields of hydrological modelling.

Environmental Contaminants: Using natural archives to track sources and long-term trends of pollution

The human footprint at the international atmosphere now touches each nook of the realm. This e-book explores the myriad ways in which environmental data can be utilized to check the distribution and long term trajectories of chemical contaminants. the amount first specializes in experiences that research the integrity of the historical checklist, together with components with regards to hydrology, post-depositional diffusion, and combining methods.

Additional resources for Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering

Example text

4). The front whisker goes from Q1 to the smallest non-outlier in the data set, and the back whisker goes from Q3 to the largest non-outlier (Trauth 2008). 4 Probability Distribution Functions 25 Fig. 4 Probability Distribution Functions A probability distribution function assigns a probability to each of the probable outcomes of a random variable. In statistics, the empirical distribution function, or empirical CDF, is the cumulative distribution function associated with the empirical measure of the sample.

The result h is 1 if the hypothesis that X has a standard normal distribution is rejected. H is 0 if that hypothesis cannot be rejected. The hypothesis is rejected if the test is significant at the 5 % level. 11 No. 5 À1 No. 3 h ¼ kstest(X) The following command performs a chi-square goodness-of-fit test of the data in the vector X against the normal distribution with mean and variance estimated from X: h ¼ chi2gof(X) The result obtained by both test is h ¼ 0. 7 Summary A stochastic variable is considered as a combination of two components: deterministic variable, D, and random variable, ε.

It is to be notified that the small value of the above correlation coefficient does not necessarily mean that two variables are independent. As an example two correlated data of X and Y which are related by function Y ¼ sin(2πX) are shown in Fig. 3. The data shown in this figure are completely correlated; however, the correlation coefficient results in 0 for those specific data. It demonstrates that the correlation coefficient only represents linear correlation and might not be much trusted in case of nonlinear relationship between two sets of data.

Download PDF sample

Rated 4.30 of 5 – based on 12 votes