Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point

Ghorbani, Mohammad Ali; Shamshirband, Shahaboddin; Haghi, Davoud Zare; Azani, Atefe; Bonakdari, Hossein; Ebtehaj, Isa

doi:10.1016/j.still.2017.04.009

Cited by 118 publications

(45 citation statements)

References 24 publications

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“…Meanwhile, the performance of SVM models to predict and map SOC was second to that of ANN. In reality, the SVM method was a feasible means for regressing and forecasting in agriculture, hydrology, meteorology, and environmental research [67][68][69]. Furthermore, Were et al [66] confirmed the close performance of ANN and SVM models for spatially predicting and mapping the pattern of SOC stocks.…”

Section: Comparisons Of Model Performance In Soc Predictionmentioning

confidence: 96%

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Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

et al. 2019

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High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSE P ) and coefficient of determination of prediction (R 2 P ) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (−0.23, −0.27, and −0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSE P of 3.718 and highest R 2 P of 0.391, followed by SVM (RMSE P = 3.753, R 2 P = 0.361) and PLSR (RMSE P = 4.087, R 2 P = 0.283). The SLR model was the worst model, with the lowest R 2 P of 0.281 and highest RMSE P of 3.930. ANN and SVM were better than OK (RMSE P = 3.727, R 2 P = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains.

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Section: Comparisons Of Model Performance In Soc Predictionmentioning

confidence: 96%

“…Figure 3 illustrates the operating principle of the SVM model. This method has been widely applied in regression and forecasting in various fields, such as agriculture, meteorology, and environmental monitoring studies [67][68][69]. The SVM model exhibits several parameters.…”

Section: Support Vector Machine (Svm) Modelmentioning

confidence: 99%

Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

et al. 2019

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“…On one hand, more accurate soil datasets could be added to the ensemble to enhance its robustness and reduce the uncertainty in the estimation of the field capacity. On the other hand, besides the PTFs and the regression equation, new modeling methods, e.g., a support vector machine (SVM), can also be used to improve the ensemble members [14,56]. Focusing on a specific small region as the study area in order to calibrate the parameters of the integration approach would help to improve the simulation capability.…”

Section: Researchermentioning

confidence: 99%

“…For field measurements, PTFs are used to transfer most basic soil information, such as soil texture, structure, and pH into other more laborious and expensively determined soil properties, such as hydraulic conductivity and field capacity [12]. Multiple linear regression (MLR) and artificial neural networks (ANNs) are usually used as techniques to build up PTFs [13][14][15][16]. For large scale research, PTFs are also used to map the distribution of laboriously measured soil properties based on basic soil information maps.…”

Section: Introductionmentioning

confidence: 99%

An Integration Approach for Mapping Field Capacity of China Based on Multi-Source Soil Datasets

et al. 2018

Water

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Field capacity is one of the most important soil hydraulic properties in water cycle, agricultural irrigation, and drought monitoring. It is difficult to obtain the distribution of field capacity on a large scale using manual measurements that are both time-consuming and labor-intensive. In this study, the field capacity ensemble members were established using existing pedotransfer functions (PTFs) and multiple linear regression (MLR) based on three soil datasets and 2388 in situ field capacity measurements in China. After evaluating the accuracy of each ensemble member, an integration approach was proposed for estimating the field capacity distribution and development of a 250 m gridded field capacity dataset in China. The spatial correlation coefficient (R) and root mean square error (RMSE) between the in situ field capacity and ensemble field capacity were 0.73 and 0.048 m 3 ·m −3 in region scale, respectively. The ensemble field capacity shows great consistency with practical distribution of field capacity, and the deviation is revised when compared with field capacity datasets provided by previous researchers. It is a potential product for estimating field capacity in hydrological and agricultural practices on both large and fine scales, especially in ungauged regions.

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“…In terms of combustion state recognition, artificial intelligence (AI) technologies have been widely used for flame image recognition [16][17][18][19]. Owing to the non-parametric characteristic, AI technologies have a major advantage of requiring no priori concept for the relationships between the input variables and output data [20]. Support vector machine (SVM) was first proposed by Vapnik [21], in accordance with the foundation of statistical theory and the principle of structural risk minimization.…”

Section: Introductionmentioning

confidence: 99%

Combustion State Recognition of Flame Images Using Radial Chebyshev Moment Invariants Coupled with an IFA-WSVM Model

et al. 2018

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Accurate combustion state recognition of flame images not only plays an important role in social security, but also contributes to increasing thermal efficiency and product quality. To improve the accuracy of feature extraction and achieve the combustion state recognition, a novel method based on radial Chebyshev moment invariants (RCMIs) and an improved firefly algorithm-wavelet support vector machine (IFA-WSVM) model is proposed. Firstly, the potential flame pixels and the potential flame contour are obtained in the pre-processing phase. Then, the rotation, translation and scaling (RTS) invariants of radial Chebyshev moments are derived. Combing the region and contour moments, the RCMIs of pre-processed and edge images are calculated to construct multi-feature vectors. To enhance the recognition performance, an IFA-WSVM model is built, where the IFA is applied to search the best parameters of WSVM. Then, the IFA-WSVM model is used to recognize the combustion state. Finally, the result for case studies show that the proposed method is superior to methods based on HMIs and ZMIs, achieving the highest rate of 99.07% in real time. The IFA algorithm also outperforms other benchmark algorithms. Even for the images transformed by RTS and small size of training sets, the proposed method continues to exhibit the best performance.

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Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point

Cited by 118 publications

References 24 publications

Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

An Integration Approach for Mapping Field Capacity of China Based on Multi-Source Soil Datasets

Combustion State Recognition of Flame Images Using Radial Chebyshev Moment Invariants Coupled with an IFA-WSVM Model

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