Python-Driven Multivariate Analysis for Sustainable Irrigation Water: DE - Softcover

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

 
9786209506376: Python-Driven Multivariate Analysis for Sustainable Irrigation Water: DE

Inhaltsangabe

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.

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Über die Autorin bzw. den Autor

Dr. Jakir Hussain K. N., Ph.D. scholar in Dept. of Soil Science and Agricultural Chemistry at the University of Agricultural Sciences, Dharwad. His research focuses on groundwater quality, irrigation water assessment, and multivariate modeling using Python. He has published research on hydrochemical analysis and sustainable water management.

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.