A New Evolutionary Hybrid Random Forest Model for SPEI Forecasting

Mehr, Ali Danandeh; Haghighi, Ali Torabi; Jabarnejad, Masood; Safari, Mir Jafar Sadegh; Nourani, Vahid

doi:10.3390/w14050755

Cited by 14 publications

(4 citation statements)

References 42 publications

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“…The authors reported the NSE values of their best models equal to 0.49 and 0.53, which are significantly lower than those in this study. Similarly, the VMD-GP model provided more accurate predictions than the RF model optimized by the genetic algorithm developed by Danandeh Mehr et al [53] for SPEI-3 prediction at Beypazari (NSE = 0.50) and Nallihan (NSE = 0.61) cities in Turkey. Despite the greater performance of the VMD-GP over GP and GEP, our study showed noticeable limitations in attaining an ideal forecast (i.e., NSE > 0.9).…”

Section: Discussionmentioning

confidence: 87%

VMD-GP: A New Evolutionary Explicit Model for Meteorological Drought Prediction at Ungauged Catchments

Danandeh Mehr,

Reihanifar,

Alee

et al. 2023

Water

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Meteorological drought is a common hydrological hazard that affects human life. It is one of the significant factors leading to water and food scarcity. Early detection of drought events is necessary for sustainable agricultural and water resources management. For the catchments with scarce meteorological observatory stations, the lack of observed data is the main leading cause of unfeasible sustainable watershed management plans. However, various earth science and environmental databases are available that can be used for hydrological studies, even at a catchment scale. In this study, the Global Drought Monitoring (GDM) data repository that provides real-time monthly Standardized Precipitation and Evapotranspiration Index (SPEI) across the globe was used to develop a new explicit evolutionary model for SPEI prediction at ungauged catchments. The proposed model, called VMD-GP, uses an inverse distance weighting technique to transfer the GDM data to the desired area. Then, the variational mode decomposition (VMD), in conjunction with state-of-the-art genetic programming, is implemented to map the intrinsic mode functions of the GMD series to the subsequent SPEI values in the study area. The suggested model was applied for the month-ahead prediction of the SPEI series at Erbil, Iraq. The results showed a significant improvement in the prediction accuracy over the classic GP and gene expression programming models developed as the benchmarks.

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Section: Discussionmentioning

confidence: 87%

VMD-GP: A New Evolutionary Explicit Model for Meteorological Drought Prediction at Ungauged Catchments

Danandeh Mehr,

Reihanifar,

Alee

et al. 2023

Water

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“…More recently, Danandeh Mehr et al [103] presented a new forecasting model referred to as GARF, which is the integration of genetic algorithm and RF. SPEI values with 3-and 6-month timescales, which were calculated for Nallihan and Beypazari stations, were used to examine the GARF model forecast capabilities.…”

Section: Review Of Drought Forecasting Studiesmentioning

confidence: 99%

Drought Monitoring and Forecasting across Turkey: A Contemporary Review

et al. 2023

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One of the critical consequences of climate change at both local and regional scales is a change in the patterns of extreme climate events such as droughts. Focusing on the different types of droughts, their quantifying indices, associated indicators, and sources of data (remote sensing (RS)/in situ measurements), this article reviewed the recent studies (from 2010 to 2022) that have explored drought features in Turkey. To this end, a total of 71 articles were selected from the Web of Science (WoS) and Scopus databases. The selected papers were clustered into two categories: (i) drought monitoring studies and (ii) drought forecasting articles. Then, the representative papers were reviewed in detail regarding the implemented indices, models (techniques), case study area, and source of the indicators used to derive drought indices. The review results showed that most of the studies aimed at meteorological drought monitoring and forecasting. An increasing trend was also observed in the use of machine learning for short-term meteorological and hydrological drought prediction. On the other hand, the emerging RS technology and satellite-driven indicators were rarely used in the country. The review showed that there is room for more research on agricultural and hydrological drought monitoring, forecasting, and pattern detection in Turkey.

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“…Due to the complexity and non-linearity of the drought process, simulations using non-linear time series data are necessary. Consequently, machine learning (ML) systems for drought forecasting have attracted considerable interest [14,15]. In addition, several types of research have shown that AI algorithms outperform conventional approaches [16][17][18][19], such as artificial neural network (ANN) [20], support vector machines (SVMs) [21], random forests [22], and the adaptive neuro-fuzzy inference system (ANFIS) [23], which are examples of these ML systems.…”

Section: Introductionmentioning

confidence: 99%

Modeling Various Drought Time Scales via a Merged Artificial Neural Network with a Firefly Algorithm

Mohammadi

2023

Hydrology

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Drought monitoring and prediction have important roles in various aspects of hydrological studies. In the current research, the standardized precipitation index (SPI) was monitored and predicted in Peru between 1990 and 2015. The current study proposed a hybrid model, called ANN-FA, for SPI prediction in various time scales (SPI3, SPI6, SPI18, and SPI24). A state-of-the-art firefly algorithm (FA) has been documented as a powerful tool to support hydrological modeling issues. The ANN-FA uses an artificial neural network (ANN) which is coupled with FA for Lima SPI prediction via other stations. Through the intelligent utilization of SPI series from neighbors’ stations as model inputs, the suggested approach might be used to forecast SPI at various time scales in a meteorological station with insufficient data. To conduct this, the SPI3, SPI6, SPI18, and SPI24 were modeled in Lima meteorological station using other meteorological stations’ datasets in Peru. Various error criteria were employed to investigate the performance of the ANN-FA model. Results showed that the ANN-FA is an effective and promising approach for drought prediction and also a multi-station strategy is an effective strategy for SPI prediction in the meteorological station with a lack of data. The results of the current study showed that the ANN-FA approach can help to predict drought with the mean absolute error = 0.22, root mean square error = 0.29, the Pearson correlation coefficient = 0.94, and index of agreement = 0.97 at the testing phase of best estimation (SPI3).

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A New Evolutionary Hybrid Random Forest Model for SPEI Forecasting

Cited by 14 publications

References 42 publications

VMD-GP: A New Evolutionary Explicit Model for Meteorological Drought Prediction at Ungauged Catchments

VMD-GP: A New Evolutionary Explicit Model for Meteorological Drought Prediction at Ungauged Catchments

Drought Monitoring and Forecasting across Turkey: A Contemporary Review

Modeling Various Drought Time Scales via a Merged Artificial Neural Network with a Firefly Algorithm

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