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Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
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Predictive Modeling and Spatial Analysis of Irrigation Water Quality in a Key Agricultural Region: An ANN-Based
Deepali Goyal1, A K Haritash1, S K Singh1
1Department of Environmental Engineering, Delhi Technological University, Delhi, India.
Summary
Groundwater quality in Ludhiana, India, shows significant salinity and sodicity concerns for irrigation. An optimized model accurately predicts Irrigation Water Quality Index (IWQI), aiding sustainable water management.
Area of Science:
- Environmental Science
- Agricultural Science
- Water Resource Management
Background:
- Groundwater quality is crucial for irrigation, impacting crop yield and soil health.
- Assessing irrigation water suitability is vital for sustainable agriculture and food security.
Purpose of the Study:
- To evaluate the suitability of groundwater in Ludhiana, Punjab, India for irrigation purposes.
- To analyze salinity and sodium hazards and determine the overall Irrigation Water Quality Index (IWQI).
Main Methods:
- Analysis of groundwater samples (n=152) for electrical conductivity (EC), %Na, Sodium Adsorption Ratio (SAR), and Piper (PI) values.
- Calculation of Irrigation Water Quality Index (IWQI) using EC, SAR, Na+, Cl-, and HCO3- values.
- Development of an Artificial Neural Network (ANN) model using IBM SPSS for IWQI prediction, validated with Root Mean Square Error (RMSE).
Main Results:
- 62.5% of samples exhibited medium salinity hazard (250-750 μS/cm), with 37.5% showing high salinity hazard.
- Most samples were categorized as excellent to good for %Na and low sodicity for SAR.
- A significant portion of groundwater samples (21.7% severe, 37.5% high) indicated restrictions for irrigation use based on IWQI.
Conclusions:
- Groundwater in Ludhiana district presents considerable challenges for irrigation due to salinity and sodicity.
- The developed ANN model accurately predicts IWQI, offering a valuable tool for future water quality assessments.
- Findings support sustainable water resource management and informed policy decisions for agricultural practices in the region, aligning with SDG 6.
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