Evaluation of Machine Learning versus Empirical Models for Monthly Reference Evapotranspiration Estimation in Uttar Pradesh and Uttarakhand States, India

Rai, Priya; Kumar, Pravendra; Al‐Ansari, Nadhir; Malik, Anurag

doi:10.3390/su14105771

Cited by 12 publications

(4 citation statements)

References 77 publications

(88 reference statements)

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“…Based on the distribution of ET 0 values, our results showed that after processing of the raw data, the processed ET 0 data fell into the −3 to 3 quantiles, suggesting the data conformed to a normal distribution, and were able to be applied to the machine learning algorithms. Tis result was in agreement with the previous studies conducted in China [38], India [39], Turkey [40], and North America [41]. Before using a model to predict target values, it was usually necessary to perform correlation analysis to remove unrelated variables.…”

Section: Normalization and Correlation Analysissupporting

confidence: 90%

Information Entropy-Based Hybrid Models Improve the Accuracy of Reference Evapotranspiration Forecast

Qin,

Fan,

Zhang

2024

Advances in Meteorology

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Accurate forecasting of reference crop evapotranspiration (ET0) is vital for sustainable water resource management. In this study, four popularly used single models were selected to forecast ET0 values, including support vector regression, Bayesian linear regression, ridge regression, and lasso regression models, respectively. They all had advantages of low requirement of data input and good capability of data fitting. However, forecast errors inevitably existed in those forecasting models due to data noise or overfitting. In order to improve the forecast accuracy of models, hybrid models were proposed to integrate the advantages of the single models. Before the construction of hybrid models, each single model’s weight was determined based on two weight determination methods, namely, the variance reciprocal and information entropy weighting methods. To validate the accuracy of the proposed hybrid models, 1–30 d forecast data from January 2 to February 1, 2022, were used as a test set in Xinxiang, North China Plain. The results confirmed the feasibility of the information entropy-based hybrid model. In detail, the information entropy model generated the mean absolute percentage errors of 11.9% or a decrease by 48.9% compared to the single and variance reciprocal hybrid models. Moreover, the model generated a correlation coefficient of 0.90 for 1–30 d ET0 forecasting or an increase by 13.6% compared to other models. The standard deviation and the root mean square error of the information entropy model were 1.65 mm·d−1 and 0.61 mm·d−1 or had a decrease by 16.4% and 23.7%. The maximum precision and the F1 score were 0.9618 and 0.9742 for the information entropy model. It was concluded that the information entropy-based hybrid model had the best midterm (1–30 d) ET0 forecasting performance in the North China Plain.

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Section: Normalization and Correlation Analysissupporting

confidence: 90%

Information Entropy-Based Hybrid Models Improve the Accuracy of Reference Evapotranspiration Forecast

Qin,

Fan,

Zhang

2024

Advances in Meteorology

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“…For predicting ET 0 , Rai et al. (2022) examined the performance of three ML algorithms, namely, SVM, M5P model tree (M5P), and RF. The study's findings revealed that SVM performed better as compared to M5P and RF.…”

Section: Discussionmentioning

confidence: 99%

Reference evapotranspiration prediction using machine learning models: An empirical study from minimal climate data

Shaloo,

Kumar,

Bisht

et al. 2023

Agronomy Journal

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The scarcity of climatic data is the biggest challenge for developing countries and the development of models for reference evapotranspiration (ET0) estimation with limited datasets is crucial. Therefore, the current investigation assessed the efficacy of four machine learning (ML) models viz., Linear Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Neural Networks (NN) to predict ET0 based on minimal climate data in comparison with the standard FAO‐56 Penman‐Monteith (PM) method. The data on daily climate parameters were collected for the period 2000−2021, including maximum and minimum temperature (Tmax, Tmin), mean relative humidity (RH), wind speed (WS) and sunshine hours (SSH). The performance of the developed models considering different input combinations was evaluated by using several statistical performance measures. The results showed that the SVM model performed better than the other ML models during training (R2 = 0.985; mean absolute error [MAE] = 0.170 mm/d; mean square error [MSE] = 0.052 mm/d; root mean square error [RMSE] = 0.229 mm/d; mean absolute percentage error [MAPE] = 5.72%,) and testing stages (R2 = 0.985; MAE = 0.168 mm/d; MSE = 0.050 mm/d; RMSE = 0.224 mm/d; MAPE = 5.91%) under full dataset scenario. The best performance of the models to estimate was with Tmax, RH. Ws, SSH and Tmin. The results of the current study are substantial as it offers an approach to estimate ET0 in semi‐arid data scarce region.This article is protected by copyright. All rights reserved

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“…Nitrate leaching measurements and expected values were contrasted throughout the experiment. Statistical measures used to validate the Ml techniques include root mean square error (RMSE) 109 , 110 which measures the average magnitude of the errors between predicted and observed values mean absolute error (MAE) 111 , 112 which is the average of the absolute errors between predicted and observed values, Nash–Sutcliffe efficiency (NSE) 113 – 115 which evaluates the efficiency of a model by comparing the simulated values to the observed values, relative to the mean observed value, Willmott index (WI) 116 – 118 which assesses the agreement between observed and predicted values, considering both bias and variance, and correlation coefficient (r) which measures the linear correlation between predicted and observed values were used in statistical analysis to examine the effectiveness of the applied algorithms (i.e., ANN, SVM, M5P, RF, and REPTree). Additionally, graphical analysis was used to assess qualitative performance.…”

Section: Methodsmentioning

confidence: 99%

Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique

Swain,

Khura,

Sahoo

et al. 2024

Sci Rep

Self Cite

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An accurate assessment of nitrate leaching is important for efficient fertiliser utilisation and groundwater pollution reduction. However, past studies could not efficiently model nitrate leaching due to utilisation of conventional algorithms. To address the issue, the current research employed advanced machine learning algorithms, viz., Support Vector Machine, Artificial Neural Network, Random Forest, M5 Tree (M5P), Reduced Error Pruning Tree (REPTree) and Response Surface Methodology (RSM) to predict and optimize nitrate leaching. In this study, Urea Super Granules (USG) with three different coatings were used for the experiment in the soil columns, containing 1 kg soil with fertiliser placed in between. Statistical parameters, namely correlation coefficient, Mean Absolute Error, Willmott index, Root Mean Square Error and Nash–Sutcliffe efficiency were used to evaluate the performance of the ML techniques. In addition, a comparison was made in the test set among the machine learning models in which, RSM outperformed the rest of the models irrespective of coating type. Neem oil/ Acacia oil(ml): clay/sulfer (g): age (days) for minimum nitrate leaching was found to be 2.61: 1.67: 2.4 for coating of USG with bentonite clay and neem oil without heating, 2.18: 2: 1 for bentonite clay and neem oil with heating and 1.69: 1.64: 2.18 for coating USG with sulfer and acacia oil. The research would provide guidelines to researchers and policymakers to select the appropriate tool for precise prediction of nitrate leaching, which would optimise the yield and the benefit–cost ratio.

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Evaluation of Machine Learning versus Empirical Models for Monthly Reference Evapotranspiration Estimation in Uttar Pradesh and Uttarakhand States, India

Cited by 12 publications

References 77 publications

Information Entropy-Based Hybrid Models Improve the Accuracy of Reference Evapotranspiration Forecast

Information Entropy-Based Hybrid Models Improve the Accuracy of Reference Evapotranspiration Forecast

Reference evapotranspiration prediction using machine learning models: An empirical study from minimal climate data

Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique

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