Predicting Irrigation Water Quality Indices Based on Data-Driven Algorithms: Case Study in Semiarid Environment

Ascertaining water quality for irrigational use by employing conventional methods is often time taking and expensive due to the determination of multiple parameters needed, especially in developing countries. Therefore, constructing precise and adequate models may be beneficial in resolving this pro...

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Bibliographic Details
Published in:Journal of chemistry Vol. 2022; pp. 1 - 17
Main Authors: Dimple, Dimple, Rajput, Jitendra, Al-Ansari, Nadhir, Elbeltagi, Ahmed
Format: Journal Article
Language:English
Published: New York Hindawi 29.08.2022
John Wiley & Sons, Inc
Wiley
Subjects:
Agriculture
Algorithms
Aquatic resources
Artificial intelligence
California
Case studies
Chemical oxygen demand
Correlation coefficients
Crop production
Crop yield
Data mining
Developing countries
Error reduction
Fresh water
Geoteknik
Global positioning systems
GPS
Groundwater
Groundwater quality
India
Irrigation
Irrigation water
LDCs
Machine learning
Magnesium
Management
Mathematical models
Model accuracy
Neural networks
Parameters
Permeability
Quality assessment
Rivers
Root-mean-square errors
Semi arid environments
Sodium
Soil Mechanics
Support vector machines
Surface water
Water
Water management
Water quality
Water resources
Water, Underground
California
India
ISSN:2090-9063, 2090-9071, 2090-9071
Online Access:Get full text
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Summary:Ascertaining water quality for irrigational use by employing conventional methods is often time taking and expensive due to the determination of multiple parameters needed, especially in developing countries. Therefore, constructing precise and adequate models may be beneficial in resolving this problem in agricultural water management to determine the suitable water quality classes for optimal crop yield production. To achieve this objective, five machine learning (ML) models, namely linear regression (LR), random subspace (RSS), additive regression (AR), reduced error pruning tree (REPTree), and support vector machine (SVM), have been developed and tested for predicting of six irrigation water quality (IWQ) indices such as sodium adsorption ratio (SAR), percent sodium (%Na), permeability index (PI), Kelly ratio (KR), soluble sodium percentage (SSP), and magnesium hazards (MH) in groundwater of the Nand Samand catchment of Rajasthan. The accuracy of these models was determined serially using the mean squared error (MSE), correlation coefficients (r), mean absolute error (MAE), and root mean square error (RMSE). The SVM model showed the best-fit model for all irrigation indices during testing, that is, RMSE: 0.0662, 4.0568, 3.0168, 0.1113, 3.7046, and 5.1066; r: 0.9364, 0.9618, 0.9588, 0.9819, 0.9547, and 0.8903; MSE: 0.004381, 16.45781, 9.101218, 0.012383, 13.72447, and 26.078; MAE: 0.042, 3.1999, 2.3584, 0.0726, 2.9603, and 4.0582 for KR, MH, SSP, SAR, %Na, and PI, respectively. The KR and SAR values were predicted accurately by the SVM model in comparison to the observed values. As a result, machine learning algorithms can improve irrigation water quality characteristics, which is critical for farmers and crop management in various irrigation procedures. Additionally, the findings of this research suggest that ML models are effective tools for reliably predicting groundwater quality using general water quality parameters that may be acquired directly on periodical basis. Assessment of water quality indices may also help in deriving optimal strategies to utilise inferior quality water conjunctively with fresh water resources in the water-limited areas.
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ISSN:2090-9063
2090-9071
2090-9071
DOI:10.1155/2022/4488446