A Contemporary Review on Drought Modeling Using Machine Learning Approaches

Sundararajan, Karpagam; Garg, Lalit; Srinivasan, Kathiravan; Bashir, Ali Kashif; Kaliappan, Jayakumar; Ganapathy, Ganapathy Pattukandan; Selvaraj, Senthil Kumaran; Meena, Tripti

doi:10.32604/cmes.2021.015528

Cited by 39 publications

(34 citation statements)

References 146 publications

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“…The values of micro AUC-ROC of SVC for SPI and SSMI were 0.980 and 0.653, respectively (Table 7). Thus, regardless of the type of the drought prediction models, the employed drought index is influential on the results, which justifies the application of multiple drought indices, as suggested by Sundararajan et al (2021), for testing and comparing the performances of different prediction models and eventually assessing the performance of CBS-SVR developed in this study.…”

Section: Comparison Of the Performances Of Svr And Cbs-svr Using Diff...mentioning

confidence: 79%

“…To assess the prediction skill of a regression model, the regression results are commonly compared with a reference (i.e., actual values). Mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R 2 ) are among the most commonly-used performance measures (Sundararajan et al 2021). Although these performance metrics enhance the preciseness and predictive skills of models, they may cause misclassification (hereafter miscategorization).…”

Section: Introductionmentioning

confidence: 99%

“…Sundararajan et al (2021) reviewed several studies on applications of machine learning for drought prediction and suggested using multiple drought indices for prediction, instead of a single index. Following this suggestion, 10 drought indices were used to evaluate the performance of CBS-SVR.…”

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confidence: 99%

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Development of category-based scoring support vector regression (CBS-SVR) for drought prediction

Bazrkar

Chu

2022

Journal of Hydroinformatics

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Using the existing measures for training numerical (non-categorical) prediction models can cause misclassification of droughts. Thus, developing a drought category-based measure is critical. Moreover, the existing fixed drought category thresholds need to be improved. The objective of this research is to develop a category-based scoring support vector regression (CBS-SVR) model based on an improved drought categorization method to overcome misclassification in drought prediction. To derive variable threshold levels for drought categorization, K-means (KM) and Gaussian mixture (GM) clustering are compared with the traditional drought categorization. For drought prediction, CBS-SVR is performed by using the best categorization method. The new drought model was applied to the Red River of the North Basin (RRB) in the USA. In the model training and testing, precipitation, temperature, and actual evapotranspiration were selected as the predictors, and the target variables consisted of multivariate drought indices, as well as bivariate and univariate standardized drought indices. Results indicated that the drought categorization method, variable threshold levels, and the type of drought index were the major factors that influenced the accuracy of drought prediction. The CBS-SVR outperformed the support vector classification and traditional SVR by avoiding overfitting and miscategorization in drought prediction.

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Section: Comparison Of the Performances Of Svr And Cbs-svr Using Diff...mentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

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Development of category-based scoring support vector regression (CBS-SVR) for drought prediction

Bazrkar

Chu

2022

Journal of Hydroinformatics

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“…Artificial intelligence (AI) has been successful in many fields and facilitates our daily life in various ways (10)(11)(12)(13)(14)(15)(16)(17). The reproduction rate prediction is crucial in successfully establishing public healthcare in the battle against COVID-19.…”

Section: Motivationmentioning

confidence: 99%

Performance Evaluation of Regression Models for the Prediction of the COVID-19 Reproduction Rate

Kaliappan

Srinivasan

Qaisar

et al. 2021

Front. Public Health

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This paper aims to evaluate the performance of multiple non-linear regression techniques, such as support-vector regression (SVR), k-nearest neighbor (KNN), Random Forest Regressor, Gradient Boosting, and XGBOOST for COVID-19 reproduction rate prediction and to study the impact of feature selection algorithms and hyperparameter tuning on prediction. Sixteen features (for example, Total_cases_per_million and Total_deaths_per_million) related to significant factors, such as testing, death, positivity rate, active cases, stringency index, and population density are considered for the COVID-19 reproduction rate prediction. These 16 features are ranked using Random Forest, Gradient Boosting, and XGBOOST feature selection algorithms. Seven features are selected from the 16 features according to the ranks assigned by most of the above mentioned feature-selection algorithms. Predictions by historical statistical models are based solely on the predicted feature and the assumption that future instances resemble past occurrences. However, techniques, such as Random Forest, XGBOOST, Gradient Boosting, KNN, and SVR considered the influence of other significant features for predicting the result. The performance of reproduction rate prediction is measured by mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), R-Squared, relative absolute error (RAE), and root relative squared error (RRSE) metrics. The performances of algorithms with and without feature selection are similar, but a remarkable difference is seen with hyperparameter tuning. The results suggest that the reproduction rate is highly dependent on many features, and the prediction should not be based solely upon past values. In the case without hyperparameter tuning, the minimum value of RAE is 0.117315935 with feature selection and 0.0968989 without feature selection, respectively. The KNN attains a low MAE value of 0.0008 and performs well without feature selection and with hyperparameter tuning. The results show that predictions performed using all features and hyperparameter tuning is more accurate than predictions performed using selected features.

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“…Ali et al [7] developed and evaluated eight machine learning (ML) models to numerically predict the quality of water irrigation parameters used for evaluating their suitability in agricultural purposes using electrical conductivity and pH as input variables in the semi-arid region of Bou Regreg in Morocco. Sundararajan et al [8] proposed a more comprehensive and extensive review of machine learning techniques for drought forecasting, especially reviewing feature selection, feature extraction, and dimensionality reduction methods. Peng et al [9] used the collected environmental data to build a water demand prediction model based on the back propagation (BP) neural network.…”

Section: Introductionmentioning

confidence: 99%

A Novel Prediction and Planning Model for the Benefit of Irrigation Water Allocation Based on Deep Learning and Uncertain Programming

Jia

Wei

Zhang

2022

Water

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Due to population growth and human activities, water shortages have become an increasingly serious concern in the North China Plain, which has become the world’s largest underground water funnel. Because the yield per unit area, planting area of crops, and effective precipitation in the region are uncertain, it is not easy to plan the amount of irrigation water for crops. In order to improve the applicability of the uncertainty programming model, a hybrid LSTM-CPP-FPP-IPP model (long short-term memory, chance-constrained programming, fuzzy possibility programming, interval parameter programming) was developed to plan the irrigation water allocation of irrigation system under uncertainty. The LSTM (long short-term memory) model was used to predict crop yield per unit area, and CPP-FPP-IPP programming (chance-constrained programming, fuzzy possibility programming, interval parameter programming) was used to plan the crop area and the effective precipitation under uncertainty. The hybrid model was used for the crop production profit of winter wheat and summer corn in five cities in the North China Plain. The average absolute error between the model prediction value and the actual value of the yield per unit area of winter wheat and summer maize in four cities in 2020 was controlled within the range of 14.02 to 696.66 kg/hectare. It shows that the model can more accurately predict the yield per unit area of crops. The planning model for the benefit of irrigation water allocation generated three scenarios of rainfall level and four planting intentions, and compared the planned scenarios with the actual production benefits of the two crops in 2020. In a dry year, the possibility of planting areas for winter wheat and summer corn is optimized. Compared with the traditional deterministic planning method, the model takes into account the uncertain parameters, which helps decision makers seek better solutions under uncertain conditions.

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A Contemporary Review on Drought Modeling Using Machine Learning Approaches

Cited by 39 publications

References 146 publications

Development of category-based scoring support vector regression (CBS-SVR) for drought prediction

Development of category-based scoring support vector regression (CBS-SVR) for drought prediction

Performance Evaluation of Regression Models for the Prediction of the COVID-19 Reproduction Rate

A Novel Prediction and Planning Model for the Benefit of Irrigation Water Allocation Based on Deep Learning and Uncertain Programming

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