Name: Python-Driven Multivariate Analysis for Sustainable Irrigation Water (en Inglés)
Price: 61.39 USD
Availability: InStock
Author: K. N., Jakir Hussain; B. R., Jagadeesh; Channavar, Vijayakumari Raveendra
ISBN: 9786209506376

portada Python-Driven Multivariate Analysis for Sustainable Irrigation Water (en Inglés)

Python-Driven Multivariate Analysis for Sustainable Irrigation Water (en Inglés)

Formato

Libro Físico

Editorial

LAP Lambert Academic Publishing

Autor

K. N., Jakir Hussain; B. R., Jagadeesh; Channavar, Vijayakumari Raveendra

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

Dimensiones

22.9 x 15.2 x 0.3 cm

ISBN13

9786209506376

Python-Driven Multivariate Analysis for Sustainable Irrigation Water (en Inglés)

K. N., Jakir Hussain; B. R., Jagadeesh; Channavar, Vijayakumari Raveendra (Autor) · LAP Lambert Academic Publishing · Tapa Blanda

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Reseña del libro "Python-Driven Multivariate Analysis for Sustainable Irrigation Water (en Inglés)"

This book presents an integrated Python-driven multivariate framework for comprehensive groundwater quality assessment with a strong focus on irrigation suitability. Using Northern Ranebennur taluk of Haveri district, Karnataka, as a case study, it combines hydrochemical analysis of 150 groundwater samples with bibliometric review and advanced machine-learning techniques to link field-scale observations with global research trends. Key parameters including pH, EC, TDS, SAR, TH, MAR, Kelley's Index, and irrigation water quality indices are analyzed to evaluate salinity, sodicity, and soil permeability hazards. Results indicate significant spatial variability, with groundwater ranging from fresh to brackish and a majority of samples classified as moderately suitable to unsuitable for irrigation under standard hazard diagrams. Bibliometric insights reveal evolving research priorities in groundwater quality management, while predictive models such as PCR, LASSO, Ridge Regression, and SVMR highlight the strengths and limitations of data-driven approaches, particularly for complex indices.