Ultrasound Penetration-Based Digital Soil Texture Analyzer

Orhan, Umut; Kilinc, Emre; Albayrak, Ferhat; Aydın, Ahmet; Torun, Ayfer Alkan

doi:10.1007/s13369-022-06766-w

Cited by 1 publication

(1 citation statement)

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“…Due to the presence of Aceria alhagi and Alhagi maurorum at various locations, the samples of soil were analyzed for a thorough investigation of the chemical and physical characteristics of the soil. A hydrometer was used to test the soil's physical characteristics (its percentages of sand, silt, and clay) 45 . A pH meter was used to measure the pH of the soil.…”

Section: Creating Study Layersmentioning

confidence: 99%

Evaluating the habitat suitability modeling of Aceria alhagi and Alhagi maurorum in their native range using machine learning techniques

Dastres

Bijani

Naderi

et al. 2023

Preprint

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Spatial locational modeling techniques are increasingly used in species distribution modeling. However, the implemented techniques differ in their modeling performance. In this study, we tested the predictive accuracy of three algorithms, namely "random forest (RF)," "support vector machine (SVM)," and "boosted regression trees (BRT)" to prepare habitat suitability mapping of an invasive species, Alhagi maurorum, and its potential biological control agent, Aceria alhagi. Location of this study was in Fars Province, southwest of Iran. The spatial distributions of the species were forecasted using GPS devices and GIS software. The probability values of occurrence were then checked using three algorithms. The predictive accuracy of the machine learning (ML) techniques was assessed by computing the “area under the curve (AUC)” of the “receiver-operating characteristic” plot. When the Aceria alhagi was modeled, the AUC values of RF, BRT and SVM were 0.89, 0.81, and 0.79, respectively. However, in habitat suitability models (HSMs) of Alhagi maurorum the AUC values of RF, BRT and SVM were 0.89, 0.80, and 0.73, respectively. The RF model provided significantly more accurate predictions than other algorithms. The importance of factors on the growth and development of Alhagi maurorum and Aceria alhagi was also determined using the partial least squares (PLS) algorithm, and the most crucial factors were the road and slope. Habitat suitability modeling based on algorithms may significantly increase the accuracy of species distribution forecasts, and thus it shows considerable promise for different conservation biological and biogeographical applications.

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Section: Creating Study Layersmentioning

confidence: 99%