Python-Driven Multivariate Analysis for Sustainable Irrigation Water - Couverture souple

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

9786209506376: Python-Driven Multivariate Analysis for Sustainable Irrigation Water

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ISBN 10 : 6209506372 ISBN 13 : 9786209506376

Editeur : LAP Lambert Academic Publishing, 2026

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�diteur: LAP Lambert Academic Publishing
Date d'�dition: 2026
Langue: anglais
ISBN 10: 6209506372
ISBN 13: 9786209506376
Reliure: Broch�
Nombre de pages: 52
Coordonn�es du fabricant: non disponible
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Python-Driven Multivariate Analysis for Sustainable Irrigation Water

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

Edit� par LAP LAMBERT Academic Publishing, 2026

ISBN 10 : 6209506372 ISBN 13 : 9786209506376

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Python-Driven Multivariate Analysis for Sustainable Irrigation Water

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

Edit� par LAP Lambert Academic Publishing, 2026

ISBN 10 : 6209506372 ISBN 13 : 9786209506376

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Python-Driven Multivariate Analysis for Sustainable Irrigation Water

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

Edit� par LAP LAMBERT Academic Publishing, 2026

ISBN 10 : 6209506372 ISBN 13 : 9786209506376

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Python-Driven Multivariate Analysis for Sustainable Irrigation Water (Paperback)

Jakir Hussain K.N.

Edit� par LAP Lambert Academic Publishing, 2026

ISBN 10 : 6209506372 ISBN 13 : 9786209506376

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Paperback. Etat : new. Paperback. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N� de r�f. du vendeur 9786209506376

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Python-Driven Multivariate Analysis for Sustainable Irrigation Water

Jakir Hussain K. N.

Edit� par LAP LAMBERT Academic Publishing Jan 2026, 2026

ISBN 10 : 6209506372 ISBN 13 : 9786209506376

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 52 pp. Englisch. N� de r�f. du vendeur 9786209506376

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Python-Driven Multivariate Analysis for Sustainable Irrigation Water (Paperback)

Jakir Hussain K.N.

Edit� par LAP Lambert Academic Publishing, 2026

ISBN 10 : 6209506372 ISBN 13 : 9786209506376

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Paperback. Etat : new. Paperback. 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N� de r�f. du vendeur 9786209506376

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Python-Driven Multivariate Analysis for Sustainable Irrigation Water

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

Edit� par LAP Lambert Academic Publishing, 2026

ISBN 10 : 6209506372 ISBN 13 : 9786209506376

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Python-Driven Multivariate Analysis for Sustainable Irrigation Water

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

Edit� par LAP Lambert Academic Publishing, 2026

ISBN 10 : 6209506372 ISBN 13 : 9786209506376

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Python-Driven Multivariate Analysis for Sustainable Irrigation Water

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

Edit� par LAP Lambert Academic Publishing, 2026

ISBN 10 : 6209506372 ISBN 13 : 9786209506376

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Python-Driven Multivariate Analysis for Sustainable Irrigation Water (Paperback)

Jakir Hussain K.N.

Edit� par LAP Lambert Academic Publishing, 2026

ISBN 10 : 6209506372 ISBN 13 : 9786209506376

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Paperback. Etat : new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N� de r�f. du vendeur 9786209506376

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