Application of the Hidden Markov Bayesian Classifier and Propagation Concept for Probabilistic Assessment of Meteorological and Hydrological Droughts in South Korea

Sattar, Muhammad Nouman; Jehanzaib, Muhammad; Kim, Ji Eun; Kwon, Hyun‐Han; Kim, Tae‐Woong

doi:10.3390/atmos11091000

Cited by 19 publications

(2 citation statements)

References 45 publications

(28 reference statements)

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“…The results revealed that ANN (coefficient of determination: R 2 = 0.86) outperformed SVR (R 2 = 0.75) in anticipating temporal drought trends. Sattar et al [16] evaluated the hydrological and meteorological droughts using the Markov Bayesian classifier model. The model was 36 to 76% accurate in predicting meteorological drought, whereas it was 33 to 70% accurate in predicting hydrological drought.…”

Section: Introductionmentioning

confidence: 99%

Hydrological Drought Prediction Based on Hybrid Extreme Learning Machine: Wadi Mina Basin Case Study, Algeria

Achite,

Katipoğlu,

Jehanzaib

et al. 2023

Atmosphere

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Drought is one of the most severe climatic calamities, affecting many aspects of the environment and human existence. Effective planning and decision making in disaster-prone areas require accurate and reliable drought predictions globally. The selection of an effective forecasting model is still challenging due to the lack of information on model performance, even though data-driven models have been widely employed to anticipate droughts. Therefore, this study investigated the application of simple extreme learning machine (ELM) and wavelet-based ELM (W-ELM) algorithms in drought forecasting. Standardized runoff index was used to model hydrological drought at different timescales (1-, 3-, 6-, 9-, and 12-month) at five Wadi Mina Basin (Algeria) hydrological stations. A partial autocorrelation function was adopted to select lagged input combinations for drought prediction. The results suggested that both algorithms predict hydrological drought well. Still, the performance of W-ELM remained superior at most of the hydrological stations with an average coefficient of determination = 0.74, root mean square error = 0.36, and mean absolute error = 0.43. It was also observed that the performance of the models in predicting drought at the 12-month timescale was higher than at the 1-month timescale. The proposed hybrid approach combined ELM’s fast-learning ability and discrete wavelet transform’s ability to decompose into different frequency bands, producing promising outputs in hydrological droughts. The findings indicated that the W-ELM model can be used for reliable drought predictions in Algeria.

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Section: Introductionmentioning

confidence: 99%

Hydrological Drought Prediction Based on Hybrid Extreme Learning Machine: Wadi Mina Basin Case Study, Algeria

Achite,

Katipoğlu,

Jehanzaib

et al. 2023

Atmosphere

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“…Moreover, drought can be categorized as meteorological, hydrological, agricultural, and socio-economic drought. erefore, it requires the investigator to consider individual opinions to define a specific type of drought [1,2,7,8].…”

Section: Introductionmentioning

confidence: 99%

Application of Extreme Learning Machine Algorithm for Drought Forecasting

et al. 2022

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Drought is a complex and frequently occurring natural hazard in many parts of the world. Therefore, accurate drought forecasting is essential to mitigate its adverse impacts. This research has inferred the implication and the appropriateness of the extreme learning machine (ELM) algorithm for drought forecasting. For numerical evaluation, time series data of the Standardized Precipitating Temperature Index (SPTI) are used for nine meteorological stations located in various climatological zones of Pakistan. To assess the performance of ELM, this research includes parallel inferences of multilayer perceptron (MLP) and autoregressive integrated moving average (ARIMA) models. The performance of each model is assessed using root mean square error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), Kling-Gupta efficiency (KGE), Willmott index (WI), and Karl Pearson’s correlation coefficient. Generally, graphical results illustrated an excellent performance of the ELM algorithm over MLP and ARIMA models. For training data of SPTI-1, ELM’s best performance has observed at Chitral station (RMSE = 0.374, KGE = 0.838, WI = 0.960, MAE = 0.272, MAPE = 259.59, R = 0.93). For SPTI-1 at Astore station, the numerical results are (RMSE = 0.688, KGE = 0.988, WI = 0.997, MAE = 0.798, MAPE = 247.35). The overall results indicate that the ELM outperformed by producing the smallest RMSE, MAE, and MAPE values and maximum values for KGE, WI, and correlation coefficient values at almost all the selected meteorological stations for (1, 3, 6, 9, and 12) month time scales. In summary, this research endorses the use of ELM for accurate drought forecasting.

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Drought Proneness Analysis of Southern Saskatchewan Province Using Markov Chain Model

Sumaiya

Ghaith

Hassini

et al. 2022

Lecture Notes in Civil Engineering

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Application of the Hidden Markov Bayesian Classifier and Propagation Concept for Probabilistic Assessment of Meteorological and Hydrological Droughts in South Korea

Cited by 19 publications

References 45 publications

Hydrological Drought Prediction Based on Hybrid Extreme Learning Machine: Wadi Mina Basin Case Study, Algeria

Hydrological Drought Prediction Based on Hybrid Extreme Learning Machine: Wadi Mina Basin Case Study, Algeria

Application of Extreme Learning Machine Algorithm for Drought Forecasting

Drought Proneness Analysis of Southern Saskatchewan Province Using Markov Chain Model

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