Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Python-Driven Multivariate Analysis for Sustainable Irrigation Water

Python-Driven Multivariate Analysis for Sustainable Irrigation Water

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

Paperback

Botany & Horticulture

ISBN10: 6209506372
ISBN13: 9786209506376
Publisher: LAP Lambert Academic Publishing
Published: Jan 26 2026
Pages: 52
Weight: 0.18
Height: 0.12 Width: 6.00 Depth: 9.00
Language: English
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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