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portada Python-Driven Multivariate Analysis for Sustainable Irrigation Water
Type
Physical Book
Publisher
Author
Language
English
Pages
52
Format
Paperback
Dimensions
22.9x15.2x0.3 cm
ISBN13
9786209506376

Python-Driven Multivariate Analysis for Sustainable Irrigation Water

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

Python-Driven Multivariate Analysis for Sustainable Irrigation Water - K. N., Jakir Hussain; B. R., Jagadeesh; Channavar, Vijayakumari Raveendra

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Synopsis "Python-Driven Multivariate Analysis for Sustainable Irrigation Water"

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.

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