Advanced Machine Learning Model for Prediction of Drought Indices using Hybrid SVR-RSM

Piri, Jamshid; Abdolahipour, M.; Keshtegar, Behrooz

doi:10.1007/s11269-022-03395-8

Cited by 40 publications

(7 citation statements)

References 58 publications

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“…In this study, the FFA approach was used as an optimization technique to tune the ANN weights and biases. This type of optimization-based hybridization is a time-consuming method; this finding is in the same direction as Piri et al [67]. The scatter plots between the actual and predicted drought for all SPI time scales by the ANN-FA model during the training and testing stages are showed in Figure 4.…”

Section: Generation Of Multi-station Scenariossupporting

confidence: 54%

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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Section: Generation Of Multi-station Scenariossupporting

confidence: 54%

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

Mohammadi

2023

Hydrology

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“…The relationship between process parameters and deposited layer dimensions is non-linear, rendering traditional methods ineffective in establishing correlations between process parameters and deposited layer dimensions and in accurately predicting the deposited layer. Support Vector Regression (SVR) is a machine learning method that relies on statistical theory and minimization of structural risk principles [16]. This method demonstrates outstanding performance in resolving small sample high-dimensional non-linear problems by constructing non-linear mapping relationships between input and output variables in an efficient and precise manner [17].…”

Section: Prediction Modelling 41 Support Vector Regression Modelmentioning

confidence: 99%

Prediction of Deposition Layer Morphology Dimensions Based on PSO-SVR for Laser–arc Hybrid Additive Manufacturing

Wang

Liu

et al. 2023

Coatings

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Laser–arc composite additive manufacturing holds significant potential for a wide range of industrial applications, and the control of morphological dimensions in the deposited layer is a critical aspect of this technology. The width and height dimensions within the deposited layer of laser–arc hybrid additive manufacturing serve as essential indicators of its morphological characteristics, directly influencing the shape quality of the deposited layer. Accurate prediction of the shape dimensions becomes crucial in providing effective guidance for size control. To achieve precise prediction of shape dimensions in laser–arc composite additive manufacturing and ensure effective regulation of the deposited layer’s shape quality, this study introduces a novel approach that combines a particle swarm algorithm (PSO) with an optimized support vector regression (SVR) technique. By optimizing the SVR parameters through the PSO algorithm, the SVR model is enhanced and fine-tuned to accurately predict the shape dimensions of the deposited layers. In this study, a series of 25 laser–arc hybrid additive manufacturing experiments were conducted to compare different approaches. Specifically, the SVR model was built using selected radial basis function (rbf) kernel functions. Furthermore, the penalty factors and kernel parameters of the SVR model were optimized using the particle swarm optimization (PSO) algorithm, leading to the development of a PSO-SVR prediction model for the morphological dimensions of the deposited layers. The performance of the PSO-SVR model was compared with that of the SVR, BPNN, and LightGBM models. Model accuracy was evaluated using a test set, revealing average relative errors of 2.39%, 7.719%, 9.46%, and 5.356% for the PSO-SVR, SVR, BPNN, and LightGBM models, respectively. The PSO-SVR model exhibited excellent prediction accuracy with minimal fluctuations in prediction error. This performance demonstrates the model’s ability to effectively capture the intricate and non-linear relationship between process parameters and deposition layer dimensions. Consequently, the PSO-SVR model can provide a foundation for the control of morphological dimensions in the deposition layer, offering an effective guide for deposition layer morphology dimension control in laser–arc composite additive manufacturing.

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“…There are several review articles in the international literature presenting and comparing various drought indices [30][31][32][33][34][35][36][37][38]. There are also many extensive studies on drought forecasting based on such indices [39][40][41][42][43][44][45][46][47].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Drought Assessment Based on Gridded Standardized Precipitation Index (SPI) in Vulnerable Agroecosystems

Sakellariou,

Spiliotopoulos,

Alpanakis

et al. 2024

Sustainability

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Drought is one of the most critical environmental hazards for the viability and productive development of crops, especially in a climate change environment. To this end, drought assessment is a process of paramount importance to make vulnerable agricultural regions more resilient. The primary aim of this paper is an integrated drought assessment through time and space in one of the most susceptible (in terms of water availability limitations) and agriculturally productive regions in Greece and the Mediterranean, namely, the Thessaly region. Supplementary objectives consist of the determination of the two most extreme years in terms of drought and wetness, so that we may reveal any potential climatological cycles/patterns from 1981 to 2020. Additionally, the methodology includes the annual and seasonal analysis using one of the most widely used drought indices, namely, the Standardized Precipitation Index (SPI), so that consistent measurements are available across a large study area, avoiding the possible scarcity/deficiency of data coming from a sparse land weather network. The innovative element of this paper is the integrated spatiotemporal drought assessment in multiple time scales through the estimation of the SPI making use of remotely sensed data, such as CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data). The outcomes highlight that the study area faced two severe years of drought in 1988 and 1989, which led to moderate and extreme drought conditions, respectively. In contrast, extremely wet conditions were observed in 2002–2003, whereas 2009–2010 experienced moderately wet conditions. The central and western part of the region tends to suffer the most in terms of drought severity, especially at the most extreme years. The validity of the results has been confirmed by the adoption of R2 where the index is approaching 0.67 despite the large size of the pixels (5 × 5 km). In this context, the mapping of spatial and seasonal variability across the study area permits more targeted measures (e.g., precision farming) instead of horizontal policies.

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Advanced Machine Learning Model for Prediction of Drought Indices using Hybrid SVR-RSM

Cited by 40 publications

References 58 publications

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

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

Prediction of Deposition Layer Morphology Dimensions Based on PSO-SVR for Laser–arc Hybrid Additive Manufacturing

Spatiotemporal Drought Assessment Based on Gridded Standardized Precipitation Index (SPI) in Vulnerable Agroecosystems

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