Urmia University Forest Research and Development 2476-3551 8 4 2022 11 22 Estimation of carbon sequestration and forest soil respiration using machine learning ‎‎models in Eastern Forests of Mazandaran Province Estimation of carbon sequestration and forest soil respiration using machine learning ‎‎models in Eastern Forests of Mazandaran Province 371 388 121240 10.30466/jfrd.2022.54304.1613 FA Seyyed Mohammad Hojati Professor, Department of Forest science and engineering, Sari Agricultural Sciences and Natural Resources ‎‎University, Sari, I. R. Iran‎ 0000-0001-7518-4391 Mahya Tafazoli PhD in Forest Soil Science, Department of Forest science and engineering, Sari Agricultural Sciences and ‎‎Natural Resources University, Sari, I. R. Iran‎ 0000-0003-4790-1706 Maryam Asadian PhD Student in Forestry, Department of Forest science and engineering, Sari Agricultural Sciences and ‎‎Natural Resources University, Sari, I.R. Iran‎ 0000-0001-8329-6908 Ali Baluee MSc Student in Forestry, Department of Forest science and engineering, Sari Agricultural Sciences and ‎‎Natural Resources University, Sari, I. R. Iran‎ Journal Article 2022 08 01 In this study, machine learning based models (linear regression, k-nearest neighbor, support vector regression, random forest) were evaluated to estimate carbon sequestration and soil respiration in the forests of eastern Mazandaran province. After identifying the sampling points, in each plot, the diameter and height of the trees were measured and the above-ground biomass of the trees was calculated using allometric models of Hyrcanian forest. Soil samples were taken from 0-20 cm depth and soil respiration was measured by CO2-port. Soil respiration was estimated using bulk density, moisture content, texture, soil temperature, total nitrogen, available phosphorus and potassium, organic carbon and above-ground trees biomass. Soil carbon sequestration was estimated using soil temperature and moisture and above-ground tree biomass. Random forest model (RMSE = 10.47 and R² = 0.82) and support vector machine (RMSE = 0.77 and R² = 0.90) had the highest performance in estimating carbon sequestration and soil respiration, respectively. Soil moisture had the highest relative importance in estimating carbon sequestration (random forest model) and soil respiration (support vector machine model). According to the obtained results, it is possible to estimate the amount of carbon sequestration and soil respiration in the forest with appropriate accuracy by having the above-ground biomass of trees and basic soil characteristics. In this study, machine learning based models (linear regression, k-nearest neighbor, support vector regression, random forest) were evaluated to estimate carbon sequestration and soil respiration in the forests of eastern Mazandaran province. After identifying the sampling points, in each plot, the diameter and height of the trees were measured and the above-ground biomass of the trees was calculated using allometric models of Hyrcanian forest. Soil samples were taken from 0-20 cm depth and soil respiration was measured by CO2-port. Soil respiration was estimated using bulk density, moisture content, texture, soil temperature, total nitrogen, available phosphorus and potassium, organic carbon and above-ground trees biomass. Soil carbon sequestration was estimated using soil temperature and moisture and above-ground tree biomass. Random forest model (RMSE = 10.47 and R² = 0.82) and support vector machine (RMSE = 0.77 and R² = 0.90) had the highest performance in estimating carbon sequestration and soil respiration, respectively. Soil moisture had the highest relative importance in estimating carbon sequestration (random forest model) and soil respiration (support vector machine model). According to the obtained results, it is possible to estimate the amount of carbon sequestration and soil respiration in the forest with appropriate accuracy by having the above-ground biomass of trees and basic soil characteristics. https://jfrd.urmia.ac.ir/article_121240_20ec838251cc2b650f9f1e1bf28bca09.pdf