Drought risk assessment under climate change impacts utilizing CMIP6 climate models in the coastal zone of the Mekong Delta

Bui, Nghia; Pal, Indrajit; Chollacoop, Nuwong

doi:10.5194/egusphere-egu23-328

Cited by 3 publications

(5 citation statements)

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“…Drought is a prevalent concern in the Mekong Delta because of climate change impacts (Bui et al, 2023). The intensity, duration, and frequency of meteorological droughts in the delta have been studied using various indices (Minh Huynh, et al, 2023).…”

Section: Figure 1 the Geographical Location And Metrological Stations...mentioning

confidence: 99%

“…Tran et al (EDSI) by integrating remote sensing data and spatiotemporal regression methods to assess the severity of agricultural drought severity (Tran et al, 2019). Pal and Juddoo conducted a comprehensive drought risk assessment that considered climate change impacts in the coastal provinces of the Mekong Delta (Bui et al, 2023). Nguyen and Li analyzed the correlation between sea surface temperature anomalies (SSTA) and meteorological droughts in the Vietnam Mekong Delta (L. Nguyen & Li, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Artificial Intelligence (AI) techniques, particularly Machine Learning (ML), have been increasingly used for drought forecasting. ML models, such as Artificial Neural Networks (ANN) and M5P trees, have shown promise in predicting drought characteristics, such as the Standardized Precipitation Index (SPI) (Bui et al, 2023). These models have been applied to improve current weather forecasts and as alternatives to conventional predictions of extreme events (Bertini et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Application of Machine Learning to Forecast Drought Index for the Mekong Delta

Duc

2024

Preprint

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Droughts have a substantial effect on water resources, agriculture, and ecosystems on a worldwide scale. In the Mekong Delta of Vietnam, droughts exacerbated by climate change are significantly endangering the region's agricultural sustainability and output. Conventional forecasting techniques frequently do not capture the intricate dynamics of meteorological phenomena associated to drought effectively, prompting the exploration of more advanced methodologies. This work utilises artificial intelligence, particularly machine learning methods like Gradient Boosting and Extreme Gradient Boosting (XGBoost), to enhance drought prediction in the Mekong Delta. The study utilises data from 11 meteorological stations spanning from 1990 to 2022 to create and evaluate Machine Learning models based on several climatic factors. We utilise Gradient Boosting and XGBoost algorithms to estimate the Standardised Precipitation-Evapotranspiration Index (SPEI) and evaluate their effectiveness in comparison to conventional forecasting techniques. The results show that Machine Learning, particularly XGBoost, surpasses traditional approaches in predicting SPEI accuracy at various time scales. XGBoost demonstrates skill in understanding the complex relationships between climatic factors, with R² values falling between 0.90 and 0.94 for 1-month forecasts. The progress highlights the potential of Machine Learning in improving drought management and adaptation tactics, proposing the incorporation of Machine Learning forecasting models into decision-making processes to enhance drought resistance in susceptible areas.

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Section: Figure 1 the Geographical Location And Metrological Stations...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Machine Learning to Forecast Drought Index for the Mekong Delta

Duc

2024

Preprint

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“…Drought is a prevalent concern in the Mekong Delta because of climate change impacts [18]. The intensity, duration, and frequency of meteorological droughts in the delta have been studied using various indices [46].…”

Section: Study Areamentioning

confidence: 99%

“…Tran et al (EDSI) by integrating remote sensing data and spatiotemporal regression methods to assess the severity of agricultural drought severity [17]. Pal and Juddoo conducted a comprehensive drought risk assessment that considered climate change impacts in the coastal provinces of the Mekong Delta [18]. Nguyen and Li analyzed the correlation between sea surface temperature anomalies (SSTA) and meteorological droughts in the Vietnam Mekong Delta [19].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

Duong,

Phong,

et al. 2024

Preprint

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Droughts have a substantial effect on water resources, agriculture, and ecosystems on a worldwide scale. In the Mekong Delta of Vietnam, droughts exacerbated by climate change are significantly endangering the region's agricultural sustainability and output. Conventional forecasting techniques frequently do not capture the intricate dynamics of meteorological phenomena associated to drought effectively, prompting the exploration of more advanced methodologies. This work utilises artificial intelligence, par-ticularly machine learning methods like Gradient Boosting and Extreme Gradient Boosting (XGBoost), to enhance drought pre-diction in the Mekong Delta. The study utilises data from 11 meteorological stations spanning from 1990 to 2022 to create and evaluate Machine Learning models based on several climatic factors. We utilise Gradient Boosting and XGBoost algorithms to estimate the Standardised Precipitation-Evapotranspiration Index (SPEI) and evaluate their effectiveness in comparison to con-ventional forecasting techniques. The results show that Machine Learning, particularly XGBoost, surpasses traditional approaches in predicting SPEI accuracy at various time scales. XGBoost demonstrates skill in understanding the complex relationships between climatic factors, with R² values falling between 0.90 and 0.94 for 1-month forecasts. The progress highlights the potential of Machine Learning in improving drought management and adaptation tactics, proposing the incorporation of Machine Learning forecasting models into decision-making processes to enhance drought resistance in susceptible areas.

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Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta

Ha,

Duc,

Luong

et al. 2024

Applied Sciences

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Droughts have a substantial impact on water supplies, agriculture, and ecosystems worldwide. Agricultural sustainability and production in the Mekong Delta of Vietnam are being jeopardized by droughts caused by climate change. Conventional forecasting methods frequently struggle to comprehend the intricate dynamics of meteorological occurrences connected to drought, necessitating the use of sophisticated prediction techniques. This study assesses the effectiveness of various statistical models (ARIMA), machine learning, and deep learning models (Gradient Boosting, XGBoost, RNN, and LSTM) in forecasting the SPEI over different time periods (1, 3, 6, and 12 months) across six prediction intervals. The models were developed and evaluated using data from 11 meteorological stations spanning from 1985 to 2022. These models incorporated various climatic variables, including precipitation, temperature, humidity, potential evapotranspiration (PET), Southern Oscillation Index (SOI) Anomaly, and sea surface temperature in the NINO4 region (SST_NINO4). The results demonstrate that XGBoost and LSTM models exhibit outstanding performance, showcasing lower error metrics and higher R² values compared to Gradient Boosting and RNN. The performance of the model fluctuated depending on the forecast step, with error metrics often increasing with longer prediction horizons. The use of climatic indices improved the accuracy of the model. These findings are consistent with earlier research on drought episodes in the Mekong Delta and support studies from other areas that show the effectiveness of advanced modeling tools for predicting droughts. The work emphasizes the capacity of machine learning and deep learning models to enhance the precision of drought forecasting, which is vital for efficient water resource management and agricultural planning in places prone to drought.

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Drought risk assessment under climate change impacts utilizing CMIP6 climate models in the coastal zone of the Mekong Delta

Cited by 3 publications

References 0 publications

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta

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