Evaluating Performance and Applicability of Several Drought Indices in Arid Regions

Moghimi, Mohammad Mehdi; Zarei, Amin

doi:10.1007/s13143-019-00122-z

Cited by 33 publications

(13 citation statements)

References 28 publications

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“…To assess the performance of each machine learning model, Nash-Sutcliffe efficiency (NSE), root mean squared errors (RMSE), the mean absolute error (MAE), correlation coefficient (R), and determination coefficient (R 2 ) were used. Numerous studies indicated the appropriateness of these measures to assess the accuracy of hydrological models [46][47][48]. NSE, RMSE, MAE, R, and R 2 for evaluation of the model accuracy can be calculated from Equations (1)- (5).…”

Section: Model Training Testmentioning

confidence: 99%

Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow

Hong

Lee

Bae

et al. 2020

Water

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Predicting dam inflow is necessary for effective water management. This study created machine learning algorithms to predict the amount of inflow into the Soyang River Dam in South Korea, using weather and dam inflow data for 40 years. A total of six algorithms were used, as follows: decision tree (DT), multilayer perceptron (MLP), random forest (RF), gradient boosting (GB), recurrent neural network–long short-term memory (RNN–LSTM), and convolutional neural network–LSTM (CNN–LSTM). Among these models, the multilayer perceptron model showed the best results in predicting dam inflow, with the Nash–Sutcliffe efficiency (NSE) value of 0.812, root mean squared errors (RMSE) of 77.218 m3/s, mean absolute error (MAE) of 29.034 m3/s, correlation coefficient (R) of 0.924, and determination coefficient (R2) of 0.817. However, when the amount of dam inflow is below 100 m3/s, the ensemble models (random forest and gradient boosting models) performed better than MLP for the prediction of dam inflow. Therefore, two combined machine learning (CombML) models (RF_MLP and GB_MLP) were developed for the prediction of the dam inflow using the ensemble methods (RF and GB) at precipitation below 16 mm, and the MLP at precipitation above 16 mm. The precipitation of 16 mm is the average daily precipitation at the inflow of 100 m3/s or more. The results show the accuracy verification results of NSE 0.857, RMSE 68.417 m3/s, MAE 18.063 m3/s, R 0.927, and R2 0.859 in RF_MLP, and NSE 0.829, RMSE 73.918 m3/s, MAE 18.093 m3/s, R 0.912, and R2 0.831 in GB_MLP, which infers that the combination of the models predicts the dam inflow the most accurately. CombML algorithms showed that it is possible to predict inflow through inflow learning, considering flow characteristics such as flow regimes, by combining several machine learning algorithms.

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Section: Model Training Testmentioning

confidence: 99%

Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow

Hong

Lee

Bae

et al. 2020

Water

View full text Add to dashboard Cite

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“…e long-term record of precipitation in the climatic area observed in the time sequence can be used to compute SPI. e vital feature of SPI is that it can be considered for various time scales and is being widely used to calculate and record drought occurrences [34,35,51,52].…”

Section: Standardized Precipitation Index (Spi)mentioning

confidence: 99%

“…e SPI can assess information related to the water deficiency at a twelve-month time scale. Furthermore, the use of SPI describes the best characteristics in forecasting and analyzing risks as a probabilistic approach [31,35,53]. e SPI has been frequently used for drought monitoring in several aspects, for example, spatiotemporal analysis, forecasting, frequency analysis, and climatic studies [33,35,51,52].…”

Section: Standardized Precipitation Index (Spi)mentioning

confidence: 99%

“…Furthermore, the use of SPI describes the best characteristics in forecasting and analyzing risks as a probabilistic approach [31,35,53]. e SPI has been frequently used for drought monitoring in several aspects, for example, spatiotemporal analysis, forecasting, frequency analysis, and climatic studies [33,35,51,52]. As precipitation is only used to determine the climatic condition for a particular area, it offers spatially reliable interpretations across various climates [32,34,35].…”

Section: Standardized Precipitation Index (Spi)mentioning

confidence: 99%

“…e dynamic characteristic of SPI is that it can be studied for different time scales and used to compare varying climatic areas. erefore, SPI is being used extensively for evaluating and recording drought characteristics [30][31][32][33][34][35]. Furthermore, the drought indices that are mentioned above have been used frequently for drought monitoring in the different studies, although having discrepancies among the indices, to gain consistent interpretation across several regimes and spatial climates.…”

Section: Introductionmentioning

confidence: 99%

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Prediction for Various Drought Classes Using Spatiotemporal Categorical Sequences

et al. 2021

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Drought frequently spreads across large spatial and time scales and is more complicated than other natural disasters that can damage economic and other natural resources worldwide. However, improved drought monitoring and forecasting techniques can help to minimize the vulnerability of society to drought and its consequent influences. This emphasizes the need for improved drought monitoring tools and assessment techniques that provide information more precisely about drought occurrences. Therefore, this study developed a new method, Model-Based Clustering for Spatio-Temporal Categorical Sequences (MBCSTCS), that uses state selection procedures through finite mixture modeling and model-based clustering. The MBCSTCS uses the functional structure of first-order Markov model components for modeling each data group. In MBCSTCS, the suitable order K of the components is selected by Bayesian information criterion (BIC). In MBCSTCS, the estimated mixing proportions and the posterior probabilities are used to compute probability distribution associated with the future steps of transitions. Furthermore, MBCSTCS predicts drought occurrences in future time using spatiotemporal categorical sequences of various drought classes. The MBCSTCS is applied to the six meteorological stations in the northern area of Pakistan. Moreover, it is found that MBCSTCS provides expeditious information for the long-term spatiotemporal categorical sequences. These findings may be helpful to make plans for early warning systems, water resource management, and drought mitigation policies to decrease the severe effects of drought.

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Assessing the Importance of Climate Variables on RDI and SPEI Using Backward Multiple Linear Regression in Arid to Humid Regions Over Iran

Zarei

2022

Pure Appl. Geophys.

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Evaluating Performance and Applicability of Several Drought Indices in Arid Regions

Cited by 33 publications

References 28 publications

Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow

Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow

Prediction for Various Drought Classes Using Spatiotemporal Categorical Sequences

Assessing the Importance of Climate Variables on RDI and SPEI Using Backward Multiple Linear Regression in Arid to Humid Regions Over Iran

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