XSoil depth is critical for eco-hydrological modeling, carbon storage calculation and land evaluation. How to estimate soil depth over a large area of complex terrain with a limited number of sparse samples remains a challenge. The main aim of this study is to comprehensively compare machine learning algorithms to predict soil depth based on the relationship between soil properties. For this purpose, various models were created and compared in experiments using well-known performance measures such as accuracy, sensitivity and specificity. This study soil depth distribution map is thought to be useful for future applications, especially for places where soil depth data is not available. Classification of soil horizons has successfully performed the classification using these features for this problem.
@article{2024,title={Comparison of Different Machine Learning Methods for Classification of Soil Horizons},abstractNode={XSoil depth is critical for eco-hydrological modeling, carbon storage calculation and land evaluation. How to estimate soil depth over a large area of complex terrain with a limited number of sparse samples remains a challenge. The main aim of this study is to comprehensively compare machine learning algorithms to predict soil depth based on the relationship between soil properties. For this purpose, various models were created and compared in experiments using well-known performance measures such as accuracy, sensitivity and specificity. This study soil depth distribution map is thought to be useful for future applications, especially for places where soil depth data is not available. Classification of soil horizons has successfully performed the classification using these features for this problem. },author={Zülküf Güman-Zülküf Güman -Hakan Tekin -Berhan Aksakal -Yasemin Gültepe },year={2024},journal={European Journal of Science and Technology}}
Zülküf Güman-Zülküf Güman -Hakan Tekin -Berhan Aksakal -Yasemin Gültepe . 2024 . Comparison of Different Machine Learning Methods for Classification of Soil Horizons . European Journal of Science and Technology.DOI:10.5281/zenodo.1417587
Zülküf Güman-Zülküf Güman -Hakan Tekin -Berhan Aksakal -Yasemin Gültepe .(2024).Comparison of Different Machine Learning Methods for Classification of Soil Horizons.European Journal of Science and Technology
Zülküf Güman-Zülküf Güman -Hakan Tekin -Berhan Aksakal -Yasemin Gültepe ,"Comparison of Different Machine Learning Methods for Classification of Soil Horizons" , European Journal of Science and Technology (2024)
Zülküf Güman-Zülküf Güman -Hakan Tekin -Berhan Aksakal -Yasemin Gültepe . 2024 . Comparison of Different Machine Learning Methods for Classification of Soil Horizons . European Journal of Science and Technology . 2024. DOI:10.5281/zenodo.1417587
Zülküf Güman-Zülküf Güman -Hakan Tekin -Berhan Aksakal -Yasemin Gültepe .Comparison of Different Machine Learning Methods for Classification of Soil Horizons. European Journal of Science and Technology (2024)
Zülküf Güman-Zülküf Güman -Hakan Tekin -Berhan Aksakal -Yasemin Gültepe .Comparison of Different Machine Learning Methods for Classification of Soil Horizons. European Journal of Science and Technology (2024)
Format:
Zülküf Güman-Zülküf Güman -Hakan Tekin -Berhan Aksakal -Yasemin Gültepe . (2024) .Comparison of Different Machine Learning Methods for Classification of Soil Horizons European Journal of Science and Technology
Zülküf Güman-Zülküf Güman -Hakan Tekin -Berhan Aksakal -Yasemin Gültepe . Comparison of Different Machine Learning Methods for Classification of Soil Horizons . European Journal of Science and Technology . 2024 doi:10.5281/zenodo.1417587
Zülküf Güman-Zülküf Güman -Hakan Tekin -Berhan Aksakal -Yasemin Gültepe ."Comparison of Different Machine Learning Methods for Classification of Soil Horizons",European Journal of Science and Technology(2024)
