Combination of discretization regression with data-driven algorithms for modeling irrigation water quality indices

Dimple,; Singh, Pradeep Kumar; Rajput, Jitendra; Kumar, Dheeraj; Gaddikeri, Venkatesh; Elbeltagi, Ahmed

doi:10.1016/j.ecoinf.2023.102093

Cited by 15 publications

(3 citation statements)

References 89 publications

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“…Groundwater (GW) plays a fundamental role in satisfying various demands for over fifty percent of the population across the world [ 1 , 2 ]. Groundwater is indispensable for life existence and healthy lifestyle, economic growth, and environmental sustainability of developing countries in particular and the world in general [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Spatial and seasonal groundwater quality assessment for drinking suitability using index and machine learning approach

Berhanu,

Lohani,

Hatiye

2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

Spatial and seasonal groundwater quality assessment for drinking suitability using index and machine learning approach

Berhanu,

Lohani,

Hatiye

2024

Heliyon

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“…Achieving a nearly uniform water application to all plants in the field necessitates a well-designed irrigation system that maintains the desired hydraulic pressure in the pipe network and provides the required operating pressure at the emitter [6,7,8,9]. Diverse models are available for designing, installing, and effectively managing drip irrigation systems [10,11], all of which revolve around understanding the process of infiltration from either a point or a line source. These models serve as valuable tools to optimize the design and performance of drip irrigation setups, ensuring efficient water distribution to crops and enhancing overall agricultural productivity.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Drip Irrigation Efficiency through Novel Statistical Modeling of Wetting Patterns in Water-Scarce Agriculture

Pal,

Arunadevi,

Pazhanivelan

et al. 2023

IJPSS

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Drip irrigation is a critical solution for agricultural water scarcity, especially in water-scarce regions. This study introduces innovative statistical models to estimate essential wetting patterns in clay loam soil for optimizing drip irrigation systems. Using a drip system with a 45 cm emitter spacing and a 4 litre per hour discharge rate, operational durations from 10 to 140 minutes were examined. Statistical models (Exponential, Linear, Logarithmic, Polynomial, and Power) were developed to predict vertical and horizontal water movement. The results highlight the Logarithmic model's excellence in predicting vertical water movement, with a remarkable Coefficient of Correlation (CC) 0.98 cm and Root Mean Square Error (RMSE) 0.76 cm, while the Polynomial model is best for horizontal movement with a CC 0.98 cm, and RMSE 1.25 cm respectively. Importantly, these models can be applied to generate artificial data for both horizontal and vertical water movements under unchanged conditions when no observable wetting pattern is present.

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“…The utilization of ML models for ET forecasting has gained significant traction in recent years [5][6][7][8][9]. Various ML algorithms are employed worldwide for ET0 prediction [10], including adaptive neuro-fuzzy neural networks [11], least square-support vector machines (LS-SVM) [12], fuzzy logic [13], multiple-layer perceptron neural networks [14], relevance vector machines [15], multivariate regression splines [16], and Least Square-Support Vector Regression (LS-SVM) [17]. Multiple studies have demonstrated that ML-based models provide more accurate ET0 estimates compared to empirical methods like the Hargreaves-Samani method, Blaney-Criddle method, Thornthwaite method, Makkink method, and Penman method across various regions globally [18].…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling and Comparative Analysis of Reference Evapotranspiration with Machine Learning Algorithms

Gaddikeri,

Jatav,

Siddharam

et al. 2023

IJECC

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Accurate estimation of reference evapotranspiration (ET0) is crucial for a multitude of applications, encompassing drought detection, irrigation scheduling, water resource management, and disaster risk reduction. This investigation utilized the FAO-PM equation for ET0 estimation and subsequently incorporated meteorological variables as input variables with machine learning (ML) models to enhance ET0 predictions. The dataset was bifurcated into training and testing data segments. Four distinct machine learning models were deployed in this study, namely Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Linear Regression (LR). The performance of these models was evaluated using various statistical indices, including Mean Absolute Error (MAE), Mean Sum of Error (MSE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and the coefficient of determination (R2), to pinpoint the most efficacious ML algorithm. After conducting a comprehensive analysis involving both training and testing data, the results unequivocally identify GBM with MAE values of 0.054 and 0.077, MSE values of 0.005 and 0.011, MAPE values of 0.014 and 0.022, RMSE values of 0.072 and 0.107, and an R2 value of 0.096 and 0.092 during training and testing, respectively. This model has been selected as the optimal choice for precise ET0 estimation within the study region. Subsequently, SVM, RF, and LR follow as alternatives in terms of performance, in descending order.

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Combination of discretization regression with data-driven algorithms for modeling irrigation water quality indices

Cited by 15 publications

References 89 publications

Spatial and seasonal groundwater quality assessment for drinking suitability using index and machine learning approach

Spatial and seasonal groundwater quality assessment for drinking suitability using index and machine learning approach

Enhancing Drip Irrigation Efficiency through Novel Statistical Modeling of Wetting Patterns in Water-Scarce Agriculture

Predictive Modeling and Comparative Analysis of Reference Evapotranspiration with Machine Learning Algorithms

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