Evaluation of effective parameters for predicting the potassium grade of saline water by using support vector machine and random forest algorithms (case study: playa of Khoor and Biabank area city, Isfahan province) | ||
| تحقیقات آب و خاک ایران | ||
| Volume 55, Issue 1, April 2024, Pages 145-161 PDF (2.13 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.22059/ijswr.2023.368909.669610 | ||
| Authors | ||
| Maryam Iraji* 1; Seyed Alireza Movahedi naeini1; Chooghi Bayram Komaki2; Soheila Ebrahimi1; Bamshad Yaghmaei3 | ||
| 1Department of Soil Science and Engineering, Faculty of Water and Soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran. | ||
| 2Department of Desert Management, Faculty of Pasture and Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran | ||
| 3Department of Archaeology, Faculty of Humanities, Higher Education Institute of Architecture and Arts, Tehran, Iran. | ||
| Abstract | ||
| The importance of potassium in agricultural products has increased the demand for potassium fertilizers. Potassium grade in aquifers ensures its extraction. The purpose of this research is to use RF and SVM algorithms in order to prioritize the effective parameters on the potassium grade of saline water groundwater in playa Khoor and Biabank in Isfahan province. For this purpose, 55 parameters were measured in 12 drilling holes.The parameters measured as independent variables include the percentage of saturated moisture, the apparent specific gravity and the porosity of the core at 15 different depths, the area polygon, the depth of the underground water, the depth of the salt layer, the potassium of the surface layer, the density of the brine and the amount of Elements of calcium, magnesium, sodium, chlorine and grade potassium were included in the model as dependent variables. In the RF model, the (PFI) and (RFE) were used for prioritization. In the different kernels of the SVM algorithm, in order to prevent the collinearity of the independent parameters, all the combinations of the independent variables, considering the variance inflation factor less than 8 and the highest coefficient of determination and the lowest MSE error, were examined and selected as the best combination. The effective parameters in predicting the grade potassium of the brine in the RF algorithm and the linear function of the SVM algorithm are sp, ap, duw, slp, SAR and n, sp, duw, and SAR respectively, which led to the best results. The coefficient of determination for both models is 0.99 and 0.97, respectively, which indicates the good accuracy of both algorithms. | ||
| Keywords | ||
| Keywords: grade prediction; random forest; saline water; support vector machine | ||
| References | ||
|
Amini Khoei, Z., & Abdullah Puri, A. (2017). Network traffic classification using improved random forest algorithm. Journal of Computer Science, 2 (2): 24 38. (In Persian). Baudron, P., Alonso-Sarría, F., García-Aróstegui, J. L., Cánovas-García, F., Martínez-Vicente, D., & Moreno-Brotóns, J. (2013). Identifying the origin of groundwater samples in a multi-layer aquifer system with Random Forest classification. Journal of Hydrology, 499, 303-315. Chatterjee, S., & Bandopadhyay, S. (2011). Goodnews Bay Platinum resource estimation using least squares support vector regression with selection of input space dimension and hyperparameters. Natural Resources Research, 20, 117-129. Chen, H., Huang, J. J., & McBean, E. (2020). Partitioning of daily evapotranspiration using a modified shuttleworth-wallace model, random Forest and support vector regression, for a cabbage farmland. Agricultural Water Management, 228, 105923. Devore, J. L. (2015). Probability and Statistics for Engineering and the Sciences. Cengage Learning. Dutta, S. (2006). Predictive performance of machine learning algorithms for ore reserve estimation in sparse and imprecise data. University of Alaska Fairbanks. Dutta, S., Bandopadhyay, S., Ganguli, R., & Misra, D. (2010). Machine learning algorithms and their application to ore reserve estimation of sparse and imprecise data. Journal of Intelligent Learning Systems and Applications, 2(02), 86-96. Estefan, G., Sommer, R., & Ryan, J. (2013). Methods of soil, plant, and water analysis. A manual for the West Asia and North Africa region, 3, 65-119. García-Gil, D., Ramírez-Gallego, S., García, S., & Herrera, F. (2018). Principal components analysis random discretization ensemble for big data. Knowledge-Based Systems, 150, 166-174. Ghorbanzadeh, O., Rostamzadeh, H., Blaschke, T., Gholaminia, K., & Aryal, J. (2018). A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping. Natural Hazards, 94, 497-517. Hasni Pak, A (2005). Exploratory data analysis. second edition. Tehran: Tehran University Press. (In Persian). Ioffe, S., & Szegedy, C. (2015, June). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning (pp. 448-456). pmlr. Jafrasteh, B., Fathianpour, N., & Suárez, A. (2018). Comparison of machine learning methods for copper ore grade estimation. Computational Geosciences, 22, 1371-1388. Jalloh, A. B., Kyuro, S., Jalloh, Y., & Barrie, A. K. (2016). Integrating artificial neural networks and geostatistics for optimum 3D geological block modeling in mineral reserve estimation: A case study. International Journal of Mining Science and Technology, 26(4), 581-585. Jeon, H., & Oh, S. (2020). Hybrid-recursive feature elimination for efficient feature selection. Applied Sciences, 10(9), 3211. Kaneko, H. (2022). Cross‐validated permutation feature importance considering correlation between features. Analytical Science Advances, 3(9-10), 278-287. Kisi, O., Karahan, M. E., & Şen, Z. (2006). River suspended sediment modelling using a fuzzy logic approach. Hydrological Processes: An International Journal, 20(20), 4351-4362. Li, X. L., Li, L. H., Zhang, B. L., & Guo, Q. J. (2013). Hybrid self-adaptive learning based particle swarm optimization and support vector regression model for grade estimation. Neurocomputing, 118, 179-190. Maleki, S., Ramazia, H. R., & Moradi, S. (2014). Estimation of Iron concentration by using a support vector machineand an artificial neural network-the case study of the Choghart deposit southeast of Yazd, Yazd, Iran. Geopersia, 4(2), 201-212. Manouchehri, Sh. (2003) Potash, Encyclopaedia of Mineral Materials and Industries of Iran, Iran Mineral Industries Research and Development Company. (In Persian). Matías, J. M., Vaamonde, A., Taboada, J., & Gonzalez-Manteiga, W. (2004). Support vector machines and gradient boosting for graphical estimation of a slate deposit. Stochastic Environmental Research and Risk Assessment, 18, 309-323. McKay, G., & Harris, J. R. (2016). Comparison of the data-driven random forests model and a knowledge-driven method for mineral prospectivity mapping: A case study for gold deposits around the Huritz Group and Nueltin Suite, Nunavut, Canada. Natural Resources Research, 25(2), 125-143. Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018). Foundations of machine learning. MIT press. Moorthi, S. M., Misra, I., Kaur, R., Darji, N. P., & Ramakrishnan, R. (2011). Kernel based learning approach for satellite image classification using support vector machine. In 2011 IEEE Recent Advances in Intelligent Computational Systems (pp. 107-110). IEEE. Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885-900. Mousavi, S.A., RanjbarFardoi, A., Mousavi, S. H. (2022). Modeling soil erodibility in Khoor and Biabank region using remote sensing indicators. Desert Ecosystem Engineering, 5(13): 67 80. (In Persian). Naghibi, S. A., Pourghasemi, H. R., & Dixon, B. (2016). GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environmental monitoring and assessment, 188, 1-27. Nitze, I., Schulthess, U., & Asche, H. (2012). Comparison of machine learning algorithms random forest, artificial neural network and support vector machine to maximum likelihood for supervised crop type classification. Proceedings of the 4th GEOBIA, Rio de Janeiro, Brazil, 79, 3540. Oke, J., Akinkunmi, W. B., & Etebefia, S. O. (2019). Use of correlation, tolerance and variance inflation factor for multicollinearity test. GSJ, 7(5). Pozdnoukhov, A. (2005). Support vector regression for automated robust spatial mapping of natural radioactivity. automatic mapping algorithms, 57. Ray, S. (2019). A quick review of machine learning algorithms. International conference on machine learning, big data, cloud and parallel computing (COMITCon) (pp. 35-39). Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J. P. (2012). An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS journal of photogrammetry and remote sensing, 67, 93-104. Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., & Chica-Rivas, M. J. O. G. R. (2015). Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews, 71, 804-818. Schnitzler, N., Ross, P. S., & Gloaguen, E. (2019). Using machine learning to estimate a key missing geochemical variable in mining exploration: Application of the Random Forest algorithm to multi-sensor core logging data. Journal of Geochemical Exploration, 205, 106344. Shaw, P. A., & Bryant, R. G. (2011). Pans, playas and salt lakes. Arid zone geomorphology: process, form and change in drylands, 373-401. Sheng, L., Zhang, T., Niu, G., Wang, K., Tang, H., Duan, Y., & Li, H. (2015). Classification of iron ores by laser-induced breakdown spectroscopy (LIBS) combined with random forest (RF). Journal of Analytical Atomic Spectrometry, 30(2), 453-458. Soliman, O. S., & Mahmoud, A. S. (2012, May). A classification system for remote sensing satellite images using support vector machine with non-linear kernel functions. In 2012 8th International Conference on Informatics and Systems (INFOS) (pp. BIO-181). IEEE. Tenorio, V. O., Bandopadhyay, S., Misra, D., Naidu, S., & Kelley, J. (2015). Support vector machines applied for resource estimation of underwater glacier-type platinum deposits. Application Of Computers and Operations Research in the Mineral Industry, 889-902. Tiwari, S., Babbar, R., & Kaur, G. (2018). Performance evaluation of two ANFIS models for predicting water quality index of River Satluj (India). Advances in Civil Engineering, 2018. Twarakavi, N. K., Misra, D., & Bandopadhyay, S. (2006). Prediction of arsenic in bedrock derived stream sediments at a gold mine site under conditions of sparse data. Natural Resources Research, 15, 15-26. Wang, C., Pan, Y., Chen, J., Ouyang, Y., Rao, J., & Jiang, Q. (2020). Indicator element selection and geochemical anomaly mapping using recursive feature elimination and random forest methods in the Jingdezhen region of Jiangxi Province, South China. Applied Geochemistry, 122, 104760. Yang, Q., Li, X., & Shi, X. (2008). Cellular automata for simulating land use changes based on support vector machines. Computers & geosciences, 34(6), 592-602. Zhang, S., Xiao, K., Carranza, E. J. M., & Yang, F. (2019). Maximum entropy and random forest modeling of mineral potential: Analysis of gold prospectivity in the Hezuo–Meiwu district, west Qinling Orogen, China. Natural Resources Research, 28, 645-664. Zörb, C., Senbayram, M., & Peiter, E. (2014). Potassium in agriculture–status and perspectives. Journal of plant physiology, 171(9), 656-669. | ||
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