Drought index time series forecasting via three-in-one machine learning concept for the Euphrates basin

Latifoğlu, Levent; Bayram, Savaş; Aktürk, Gaye; Citakoglu, Hatice

doi:10.1007/s12145-024-01471-8

Cited by 3 publications

References 108 publications

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SPEI and SPI correlation in the study of drought phenomena in Umbria region (central Italy)

Venturi,

Dunea,

Mateescu

et al. 2024

Environ Sci Pollut Res

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This study aims to analyze the performances and correlation of the standardized precipitation index (SPI) and standardized precipitation evaporation index (SPEI) from the perspective of supplying effective indicators for drought risk management prevention. Indices have been evaluated using long time series of precipitation and temperature data (from 1961 to 2020) gauged and validated in the land monitoring system of the Umbria region (central Italy). Results show how SPEI can evaluate better the drought phenomena, both in terms of occurred events and in terms of trends. In particular, SPEI can appreciate the effects of the increase in temperatures, which in the next years could be predominant in c limate change. Currently, the high correlation between SPEI and SPI (R2 > 0.8 and r > 0.9) makes possible the use of SPI instead of SPEI in drought analysis; however, this correlation, evaluated on the two times series 1961–1990 and 1991–2020, shows a decreasing tendency; then, it could be no longer reliable in the future. These results should lead to increasingly synergistic monitoring of rainfall and temperature data, limiting as much as possible the lack of temporal overlap of the two sensors at the gauging stations. The possibility of using ERA 5 reanalysis data was also explored for the years that presented severe droughts by comparing them to the station-based observations. While the temperature data is reliable, the total precipitation parameter seems less affordable, and then, other available gridded datasets, e.g., CHIRPS, MERRA2, and Terraclimate, should have to be considered to improve the modeled precipitation’s suitability. Graphical abstract

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SPEI and SPI correlation in the study of drought phenomena in Umbria region (central Italy)

Venturi,

Dunea,

Mateescu

et al. 2024

Environ Sci Pollut Res

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STAT-LSTM: A multivariate spatiotemporal feature aggregation model for SPEI-based drought prediction

Chen,

Wu,

Xie

et al. 2024

Preprint

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In recent decades, shifts in the spatiotemporal patterns of precipitation and extreme temperatures have contributed to more frequent droughts. These changes impact not only agricultural production but also food security, ecological sys- tems, and social stability. Advanced techniques such as machine learning and deep learning models outperform traditional models by improving meteorolog- ical drought prediction. Specifically, this study proposes a novel model named the multivariate feature aggregation-based temporal convolutional network for meteorological drought spatiotemporal prediction (STAT-LSTM). The method consists of three parts: a feature aggregation module, which aggregates multi- variate features to extract initial features; a self-attention-temporal convolutional network (SA-TCN), which extracts time series features and uses the self-attention module’s weighting mechanism to automatically capture global dependencies in the sequential data; and a long short-term memory network (LSTM), which cap- tures long-term dependencies. The performance of the STAT-LSTM model was assessed and compared via performance indicators (i.e., MAE, RMSE, and R2 ). The results indicated that STAT-LSTM provided the most accurate SPEI pre- diction (MAE = 0.474, RMSE = 0.63, and R2 = 0.613 for SPEI-3; MAE = 0.356, RMSE = 0.468, and R2 = 0.748 for SPEI-6; MAE = 0.284, RMSE = 0.437, and R2 = 0.813 for SPEI-9; and MAE = 0.182, RMSE = 0.267, and R2 = 0.934 for SPEI-12).

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Evaluating Performances of LSTM, SVM, GPR, and RF for Drought Prediction in Norway: A Wavelet Decomposition Approach on Regional Forecasting

Oruc,

Hinis,

Tugrul

2024

Water

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A serious natural disaster that poses a threat to people and their living spaces is drought, which is difficult to notice at first and can quickly spread to wide areas through subtle progression. Numerous methods are being explored to identify, prevent, and mitigate drought, and distinct metrics have been developed. In order to contribute to the research on measures to be taken against drought, the Standard Precipitation Evaporation Index (SPEI), one of the drought indices that has been developed and accepted in recent years and includes a more comprehensive drought definition, was chosen in this study. Machine learning and deep learning algorithms, including support vector machine (SVM), random forest (RF), long short-term memory (LSTM), and Gaussian process regression (GPR), were used to model the droughts in six regions of Norway: Bodø, Karasjok, Oslo, Tromsø, Trondheim, and Vadsø. Four distinct model architectures were employed for this goal, and as a novel approach, the models’ output was enhanced by using discrete wavelet decomposition/transformation (WT). The model outputs were evaluated using the correlation coefficient (r), Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE) as performance evaluation criteria. When the findings were analyzed, the GPR model (W-GPR), which was acquired after WT, typically produced the best results. Furthermore, it was discovered that, out of all the recognized models, M04 had the most effective model structure. Consequently, the most successful outcomes were obtained with W-SVM-M04 for Bodø and W-GPR-M04 for Karasjok, Oslo, Tromsø, Trondheim, and Vadsø. Furthermore, W-GPR-M04 in the Oslo region had the best results across all regions (r: 0.9983, NSE: 0.9966 and RMSE:0.0539).

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Drought index time series forecasting via three-in-one machine learning concept for the Euphrates basin

Cited by 3 publications

References 108 publications

SPEI and SPI correlation in the study of drought phenomena in Umbria region (central Italy)

SPEI and SPI correlation in the study of drought phenomena in Umbria region (central Italy)

STAT-LSTM: A multivariate spatiotemporal feature aggregation model for SPEI-based drought prediction

Evaluating Performances of LSTM, SVM, GPR, and RF for Drought Prediction in Norway: A Wavelet Decomposition Approach on Regional Forecasting

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