Appraisal of Hydrological Components Using Soft Computing Techniques

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Appraisal of Hydrological Components Using Soft Computing Techniques provides a detailed account of the various available hydrological components, including precipitation, stream flow, draught, Infiltration, evapotranspiration etc.

The Hydrological cycle is a very complex phenomenon of continuous movement of water in different forms among the earth and atmosphere. There are several classical models are available in literature to solve or estimate different hydrological components, but these classical models are very complex. In the past few decades soft computing-based models have been successfully used for the solution of complex problems in various fields. Through this book Precipitation, stream flow, drought, Evapotranspiration, Humidity, Wind speed, Infiltration, soil temperature etc. are estimated using soft computing techniques.

Appraisal of Hydrological Components Using Soft Computing Techniques presents modeling related issues including over fitting, input variable selection, data separation, performance evaluation indices. Case studies are also presented, to enable a better understanding of how these techniques can be used and work. in this book for better understanding. The latest data and soft computing techniques for the estimation of hydrological components are covered and this content is for graduates and researchers in Hydrology, Environmental Science and Environmental Engineering.

Contenu

1. Introduction of hydrological cycle
   
   2. Machine Learning and soft computing based techniques
   3. Hydrological data and processes
   4. Precipitation estimation
   5. Stream flow modeling using M5P and multivariate adaptive regression splines (MARS)
   6. Prediction of Drought using Gene Expression Programming(GEP) and artificial neural network
   7. Evapotranspiration modeling using Random Forest, Random Tree and M5P
   8. Humidity modelling using pruned, unpruned and bagged approach based M5P
   9. Wind speed estimation using tree based techniques
   10. Soil temperature prediction using artificial neural network and adaptive neuro fuzzy inference system
   11. Estimation of Infiltration of soil using multivariate adaptive regression splines (MARS) and Group method of data handling (GMDH)
   12. Ensemble and Hybrid Models for Hydrological Cycles