Spatial spectrum estimation for three algorithms. by Chuanxi Xing (20141665)

“…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”

A new approach in soil organic carbon estimation using machine learning algorithms: a study in a tropical forest in Vietnam by Thuy Phuong Nguyen (11769999)

“…This study aimed to evaluate the ability of SOC estimation using a multiple linear regression model (MLR) and four machine learning algorithms: artificial neural networks (ANN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) with satellite data sources and soil nutrient indicator data to find the optimal method. …”

Comparison of the performance of ML algorithms in predicting maize yield using 5-fold CV. by Oumnia Ennaji (20442244)

Schematic diagram of random forest algorithm. by Imee V. Necesito (12749885)

Confusion Matrix for Subspace algorithm (3000 Learning Cycles) providing insight into the performance evaluation of Subspace algorithm. by Sadia Nazim (21440115)

Schematic diagram of the SARIMA algorithm flow. by Yongxin Dai (13796385)

Subjects:

Noise reduction by the sliding average algorithm. by Yongxin Dai (13796385)

Subjects:

Flow chart of the six hybrid ET models and a pure machine learning model using random forest (RF) algorithms. by Yeonuk Kim (4589488)

Flowchart of the SSA-based optimization of the BPNN algorithm. by Yeping Shi (14279118)

Subjects:

Comparison of detection results of the SSA optimization algorithm. by Yeping Shi (14279118)

Subjects:

K-means++ clustering algorithm. by Zhen Zhao (159931)

“…Subsequently, the feature factors corresponding to the model with the highest accuracy were selected as the optimal feature subsets and used in the model construction as input data. Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

Explained variance ration of the PCA algorithm. by Abeer Aljohani (18497914)

“…These classification algorithms often requires conversion of a medical data to another space in which the original data is reduced to important values or moments. …”

Image 9_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

“…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”

Table 1_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.docx by Junfei Zhang (7547975)

“…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”

Image 10_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

“…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”

Image 3_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

“…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”

Image 4_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

“…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”

Image 8_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

“…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”

Image 2_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

“…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”

Image 6_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

“…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”