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Python-Driven Multivariate Analysis for Sustainable Irrigation Water
Print on DemandDa: PBShop.store US, Wood Dale, IL, U.S.A.
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PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Codice articolo L0-9786209506376
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Python-Driven Multivariate Analysis for Sustainable Irrigation Water
Editore:
LAP Lambert Academic Publishing, 2026
ISBN 10: 6209506372
ISBN 13: 9786209506376
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Da: California Books, Miami, FL, U.S.A.
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Condizione: New. Codice articolo I-9786209506376
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Python-Driven Multivariate Analysis for Sustainable Irrigation Water
Print on DemandDa: PBShop.store UK, Fairford, GLOS, Regno Unito
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PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Codice articolo L0-9786209506376
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Python-Driven Multivariate Analysis for Sustainable Irrigation Water (Paperback)
Editore:
LAP Lambert Academic Publishing, 2026
ISBN 10: 6209506372
ISBN 13: 9786209506376
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Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
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Paperback. Condizione: 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. Codice articolo 9786209506376
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Python-Driven Multivariate Analysis for Sustainable Irrigation Water
Editore:
LAP LAMBERT Academic Publishing Jan 2026, 2026
ISBN 10: 6209506372
ISBN 13: 9786209506376
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 52 pp. Englisch. Codice articolo 9786209506376
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Python-Driven Multivariate Analysis for Sustainable Irrigation Water (Paperback)
Editore:
LAP Lambert Academic Publishing, 2026
ISBN 10: 6209506372
ISBN 13: 9786209506376
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Da: AussieBookSeller, Truganina, VIC, Australia
Valutazione del venditore 5 su 5 stelle
Paperback. Condizione: 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. Codice articolo 9786209506376
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Python-Driven Multivariate Analysis for Sustainable Irrigation Water
Editore:
LAP Lambert Academic Publishing, 2026
ISBN 10: 6209506372
ISBN 13: 9786209506376
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Da: Books Puddle, New York, NY, U.S.A.
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Condizione: New. Codice articolo 26405804109
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Python-Driven Multivariate Analysis for Sustainable Irrigation Water
Editore:
LAP Lambert Academic Publishing, 2026
ISBN 10: 6209506372
ISBN 13: 9786209506376
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Da: Majestic Books, Hounslow, Regno Unito
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Condizione: New. Print on Demand. Codice articolo 407350162
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Python-Driven Multivariate Analysis for Sustainable Irrigation Water
Editore:
LAP Lambert Academic Publishing, 2026
ISBN 10: 6209506372
ISBN 13: 9786209506376
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Da: Biblios, Frankfurt am main, HESSE, Germania
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Condizione: New. PRINT ON DEMAND. Codice articolo 18405804103
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Python-Driven Multivariate Analysis for Sustainable Irrigation Water (Paperback)
Editore:
LAP Lambert Academic Publishing, 2026
ISBN 10: 6209506372
ISBN 13: 9786209506376
Nuovo
Paperback
Da: CitiRetail, Stevenage, Regno Unito
Valutazione del venditore 5 su 5 stelle
Paperback. Condizione: 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. Codice articolo 9786209506376
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