Evaluation of Intelligence Models to Estimate the Least Limiting Water Range Using Conveniently Measurable Soil Properties
| Autor: | Elham Chavoshi, I. Esfandiarpour Boroujeni, R. Soleimani, Hossein Shirani |
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| Rok vydání: | 2021 |
| Předmět: |
Multivariate statistics
Coefficient of determination Mean squared error Artificial neural network Computer Science::Neural and Evolutionary Computation Soil Science Sampling (statistics) Soil science 04 agricultural and veterinary sciences Limiting 010501 environmental sciences equipment and supplies 01 natural sciences body regions 040103 agronomy & agriculture Range (statistics) 0401 agriculture forestry and fisheries Soil properties 0105 earth and related environmental sciences Earth-Surface Processes Mathematics |
| Zdroj: | Eurasian Soil Science. 54:389-398 |
| ISSN: | 1556-195X 1064-2293 |
| DOI: | 10.1134/s1064229321030145 |
| Popis: | Direct measurement of the least limiting water range (LLWR) is costly and time-consuming. In this study, genetic algorithm-based neural network (ANN-GA), artificial neural network (ANN) and stepwise multivariate regression (SMR) were used to estimate the LLWR of soil using easily measurable soil properties in the Khanmirza Plain. Then, depending on the location of each area, a total of 250 points were randomly identified as approximate sampling sites. Results showed that the accuracy of the SMR model with the percentage of clay, organic carbon and fine sand had a coefficient of determination of 0.42. The ANN-GA and ANN models with the highest coefficient of determination (R2 = 0.98) and mean square error (MAE = 0.0538) were suitable for estimating the least limiting water range. Therefore, the efficiency of models showed that the ANN and ANN-GA predicted the LLWR more accurate compared to the SMR and their results were close to the measured ones. |
| Databáze: | OpenAIRE |
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