Support vector regression integrated with novel meta-heuristic algorithms for meteorological drought prediction

Malik, Anurag; Tikhamarine, Yazid; Souag-Gamane, Doudja; Rai, Priya; Sammen, Saad Sh.; Kişi, Özgür

doi:10.1007/s00703-021-00787-0

Cited by 50 publications

(16 citation statements)

References 94 publications

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“…Model performance was evaluated using R 2 (coefficient of determination), root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (COC) and Willmott index (WI) values of the testing sets. The R 2 (−∞ < R 2 < 1), RMSE (0 < RMSE < ∞), MAE (0 < MAE < ∞), COC (−1 < COC < 1) and WI (0 < WI ≤ 1) can be expressed as: R 2

R^{2} = 1 ‐ (][, \frac{\sum_{i = 1}^{N} {()(, y_{obs, i} - y_{s i m, i})}^{2}}{\sum_{i = 1}^{N} {()(, y_{obs, i} - {\overset{true¯}{y}}_{obs})}^{2}})

RMSE (Malik, Tikhamarine, Al‐Ansari, et al, 2021; Malik, Tikhamarine, Sammen, et al, 2021; Malik, Tikhamarine, Souag‐Gamane, et al, 2021)

RMSE goodbreak= \sqrt{\frac{1}{N} \sum_{i = 1}^{N} {(y_{obs, i} - y_{sim, i})}^{2}}

MAE (Malik, Tikhamarine, Al‐Ansari, et al, 2021; Malik, Tikhamarine, Sammen, et al, 2021; Malik, Tikhamarine, Souag‐Gamane, et al, 2021)

MAE goodbreak= \frac{1}{N} \sum_{i = 1}^{N} (||, y_{sim, i} goodbreak- y_{obs, i})

COC (Malik, Tikhamarine, Sammen, et al, 2021; Malik, Tikhamarine, Souag‐Gamane, et al, 2021)

COC goodbreak= \frac{\sum_{i = 1}^{N} ()(, y_{obs, i} goodbreak- {\overset{true¯}{y}}_{obs}) ()(, y_{sim, i} goodbreak- {\overset{true¯}{y}}_{sim})}{\sqrt{\sum_{i = 1}^{N}}}

…”

Section: Methodsmentioning

confidence: 99%

Simulation of site‐scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China

Xie

Luo

Hellwich

et al. 2021

Hydrological Processes

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The study of water fluxes is important to better understand hydrological cycles in arid regions. Data‐driven machine learning models have been recently applied to water flux simulation. Previous studies have built site‐scale simulation models of water fluxes for individual sites separately, requiring a large amount of data from each site and significant computation time. For arid areas, there is no consensus as to the optimal model and variable selection method to simulate water fluxes. Using data from seven flux observation sites in the arid region of Northwest China, this study compared the performance of random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and multiple linear regression (MLR) models in simulating water fluxes. Additionally, the study investigated inter‐annual and seasonal variation in water fluxes and the dominant drivers of this variation at different sites. A universal simulation model for water flux was built using the RF approach and key variables as determined by MLR, incorporating data from all sites. Model performance of the SVM algorithm (R2 = 0.25–0.90) was slightly worse than that of the RF algorithm (R2 = 0.41–0.91); the BPNN algorithm performed poorly in most cases (R2 = 0.15–0.88). Similarly, the MLR results were limited and unreliable (R2 = 0.00–0.66). Using the universal RF model, annual water fluxes were found to be much higher than the precipitation received at each site, and natural oases showed higher fluxes than desert ecosystems. Water fluxes were highest during the growing season (May–September) and lowest during the non‐growing season (October–April). Furthermore, the dominant drivers of water flux variation were various among different sites, but the normalized difference vegetation index (NDVI), soil moisture and soil temperature were important at most sites. This study provides useful insights for simulating water fluxes in desert and oasis ecosystems, understanding patterns of variation and the underlying mechanisms. Besides, these results can make a contribution as the decision‐making basis to the water management in desert and oasis ecosystems.

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R^{2} = 1 ‐ (][, \frac{\sum_{i = 1}^{N} {()(, y_{obs, i} - y_{s i m, i})}^{2}}{\sum_{i = 1}^{N} {()(, y_{obs, i} - {\overset{true¯}{y}}_{obs})}^{2}})

RMSE (Malik, Tikhamarine, Al‐Ansari, et al, 2021; Malik, Tikhamarine, Sammen, et al, 2021; Malik, Tikhamarine, Souag‐Gamane, et al, 2021)

RMSE goodbreak= \sqrt{\frac{1}{N} \sum_{i = 1}^{N} {(y_{obs, i} - y_{sim, i})}^{2}}

MAE (Malik, Tikhamarine, Al‐Ansari, et al, 2021; Malik, Tikhamarine, Sammen, et al, 2021; Malik, Tikhamarine, Souag‐Gamane, et al, 2021)

MAE goodbreak= \frac{1}{N} \sum_{i = 1}^{N} (||, y_{sim, i} goodbreak- y_{obs, i})

COC (Malik, Tikhamarine, Sammen, et al, 2021; Malik, Tikhamarine, Souag‐Gamane, et al, 2021)

COC goodbreak= \frac{\sum_{i = 1}^{N} ()(, y_{obs, i} goodbreak- {\overset{true¯}{y}}_{obs}) ()(, y_{sim, i} goodbreak- {\overset{true¯}{y}}_{sim})}{\sqrt{\sum_{i = 1}^{N}}}

…”

Section: Methodsmentioning

confidence: 99%

Simulation of site‐scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China

Xie

Luo

Hellwich

et al. 2021

Hydrological Processes

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“…The SVR models are widely used to solve various problems in earth sciences, such as real-time flood stage forecasting, snow-depth retrieval, drought prediction, and landuse/landcover change analysis. [68][69][70][71][72][73][74][75][76]. The SVR has an excellent generalisation capability with optimal accuracy that makes it applicable to the solution of various problems in earth sciences, image processing, wireless sensor networks, and blockchain [77][78][79][80].…”

Section: Support Vector Regressionmentioning

confidence: 99%

Machine Learning to Estimate Surface Roughness from Satellite Images

et al. 2021

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We apply the Support Vector Regression (SVR) machine learning model to estimate surface roughness on a large alluvial fan of the Kosi River in the Himalayan Foreland from satellite images. To train the model, we used input features such as radar backscatter values in Vertical–Vertical (VV) and Vertical–Horizontal (VH) polarisation, incidence angle from Sentinel-1, Normalised Difference Vegetation Index (NDVI) from Sentinel-2, and surface elevation from Shuttle Radar Topographic Mission (SRTM). We generated additional features (VH/VV and VH–VV) through a linear data fusion of the existing features. For the training and validation of our model, we conducted a field campaign during 11–20 December 2019. We measured surface roughness at 78 different locations over the entire fan surface using an in-house-developed mechanical pin-profiler. We used the regression tree ensemble approach to assess the relative importance of individual input feature to predict the surface soil roughness from SVR model. We eliminated the irrelevant input features using an iterative backward elimination approach. We then performed feature sensitivity to evaluate the riskiness of the selected features. Finally, we applied the dimension reduction and scaling to minimise the data redundancy and bring them to a similar level. Based on these, we proposed five SVR methods (PCA-NS-SVR, PCA-CM-SVR, PCA-ZM-SVR, PCA-MM-SVR, and PCA-S-SVR). We trained and evaluated the performance of all variants of SVR with a 60:40 ratio using the input features and the in-situ surface roughness. We compared the performance of SVR models with six different benchmark machine learning models (i.e., Gaussian Process Regression (GPR), Generalised Regression Neural Network (GRNN), Binary Decision Tree (BDT), Bragging Ensemble Learning, Boosting Ensemble Learning, and Automated Machine Learning (AutoML)). We observed that the PCA-MM-SVR perform better with a coefficient of correlation (R = 0.74), Root Mean Square Error (RMSE = 0.16 cm), and Mean Square Error (MSE = 0.025 cm2). To ensure a fair selection of the machine learning model, we evaluated the Akaike’s Information Criterion (AIC), corrected AIC (AICc), and Bayesian Information Criterion (BIC). We observed that SVR exhibits the lowest values of AIC, corrected AIC, and BIC of all the other methods; this indicates the best goodness-of-fit. Eventually, we also compared the result of PCA-MM-SVR with the surface roughness estimated from different empirical and semi-empirical radar backscatter models. The accuracy of the PCA-MM-SVR model is better than the backscatter models. This study provides a robust approach to measure surface roughness at high spatial and temporal resolutions solely from the satellite data.

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“…As the name indicates, it is the square root of the mean square error. A zero value indicates the best prediction and can be expressed as (Elbeltagi et al 2020;Malik et al 2021c…”

Section: Performance Evaluation Indicatorsmentioning

confidence: 99%

Prediction of aeration efficiency of Parshall and Modified Venturi flumes: application of soft computing versus regression models

et al. 2021

Self Cite

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In this study, the potential of soft computing techniques namely Random Forest (RF), M5P, Multivariate Adaptive Regression Splines (MARS), and Group Method of Data Handling (GMDH) was evaluated to predict the aeration efficiency (AE20) at Parshall and Modified Venturi flumes. Experiments were conducted for 26 various Modified Venturi flumes and one Parshall flume. A total of 99 observations were obtained from experiments. The results of soft computing models were compared with regression-based models (i.e., MLR: multiple linear regression, and MNLR: multiple nonlinear regression). Results of the analysis revealed that the MARS model outperformed other soft computing and regression-based models for predicting the AE20 at Parshall and Modified Venturi flumes with Pearson's correlation coefficient (CC) = 0.9997, and 0.9992, and root mean square error (RMSE) = 0.0015, and 0.0045 during calibration and validation periods. Sensitivity analysis was also carried out by using the best executing MARS model to assess the effect of individual input variables on AE20 of both flumes. Obtained results on sensitivity examination indicate that the oxygen deficit ratio (r) was the most effective input variable in predicting the AE20 at Parshall and Modified Venturi flumes.

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Support vector regression integrated with novel meta-heuristic algorithms for meteorological drought prediction

Cited by 50 publications

References 94 publications

Simulation of site‐scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China

Simulation of site‐scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China

Machine Learning to Estimate Surface Roughness from Satellite Images

Prediction of aeration efficiency of Parshall and Modified Venturi flumes: application of soft computing versus regression models

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