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Evaluation algorithm of alhagi sparsifolia desertification control under different irrigation amounts
Algoritmo de evaluación para el control de la desertificación del alhagi sparsifolia bajo diferentes cantidades de riego
DOI:
https://doi.org/10.15446/esrj.v24n4.91626Keywords:
Irrigation amount, Alhagi sparsifolia, Desertification, Control, Regression analysis (en)Cantidad de riego, alhagi sparsifolia, desertificación, análisis de regresión (es)
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Desertification control is an important issue that must be considered in modern society. In order to effectively improve the accuracy and practicability of the evaluation algorithm of desertification control effect, the Alhagi sparsifolia index under different irrigation amount was taken as the research object, and the evaluation algorithm of desertification control effect was proposed. In the “vegetation-sandstorm-soil” index system, a number of indexes were selected according to the core environmental parameters of Alhagi sparsifolia and grassland desertification. And the analytic hierarchy process, remote sensing, geographic information system, and landscape technology were used to assign index weights of desertification control capacity, which were calculated by multiple discriminant matrices. Finally, the data regression analysis was performed based on remote sensing and computer image information screening and processing to determine the final evaluation results. The experimental data show that the true positive rate of the algorithm in this paper is between 160 and 200, which is within a large range of advantages, indicating that the overall evaluation accuracy of the algorithm is high and the evaluation effect is perfect.
El control de la desertificación es un tema importante que debe considerarse en la sociedad moderna. Con el fin de mejorar efectivamente la precisión y la viabilidad de un algoritmo de evaluación del efecto de control de la desertificación se tomó como objeto de investigación el índice del alhagi sparsifolia con diferentes cantidades de riego. En el sistema de índices “vegetación-tormenta de arena-suelo” se seleccionaron varios índices de acuerdo con los parámetros ambientales básicos del alhagi sparsifolia y la desertificación de los pastizales. Se utilizó el proceso de jerarquía analítica, la teledetección, el sistema de información geográfica y la tecnología del paisaje para asignar los índices de ponderación de la capacidad de control de la desertificación, los cuales se calcularon mediante matrices discriminantes múltiples. Finalmente, el análisis de regresión de datos se realizó con base en la detección y el procesamiento de información de imágenes de computadora y detección remota para determinar los resultados finales de la evaluación. Los datos experimentales muestran que la tasa de verdaderos positivos del algoritmo en este artículo está entre 160 y 200, lo que se encuentra dentro de una amplia gama de ventajas y que indica que la precisión de evaluación general del algoritmo es alta y el efecto de evaluación es perfecto.
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1. Huishi Du, Jingfa Wang, Cheng Han. (2022). High-precision remote sensing mapping of aeolian sand landforms based on deep learning algorithms. Open Geosciences, 14(1), p.224. https://doi.org/10.1515/geo-2022-0351.
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