Analytical determination of interwell connectivity based on interwell influence

Yuan, Jiangru; Zeng, Xingjie; Wu, Haiyun; Zhang, Weishan; Zhou, Jiehan; Chen, Bingyang

doi:10.26599/tst.2020.9010039

Cited by 7 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, swarm intelligence optimization algorithms were employed to select the parameters of the models, providing better solutions even in the face of difficult problems [ 42 ]. Common swarm intelligence optimization algorithms are PSO, WOA, and SSA, and have been widely used by researchers for machine learning [ 43 , 44 , 45 ], as well as having advantages in hyperparameter search for CatBoost, converge quickly, and improve CatBoost’s performance [ 46 , 47 , 48 ]. Therefore, in this study, PSO, WOA, and SSA were adopted to optimize the four hyperparameters of the CatBoost model, and the results were compared.…”

Section: Resultsmentioning

confidence: 99%

An Improved CatBoost-Based Classification Model for Ecological Suitability of Blueberries

Chang

Wang

Yang

et al. 2023

Sensors

View full text Add to dashboard Cite

Selecting the best planting area for blueberries is an essential issue in agriculture. To better improve the effectiveness of blueberry cultivation, a machine learning-based classification model for blueberry ecological suitability was proposed for the first time and its validation was conducted by using multi-source environmental features data in this paper. The sparrow search algorithm (SSA) was adopted to optimize the CatBoost model and classify the ecological suitability of blueberries based on the selection of data features. Firstly, the Borderline-SMOTE algorithm was used to balance the number of positive and negative samples. The Variance Inflation Factor and information gain methods were applied to filter out the factors affecting the growth of blueberries. Subsequently, the processed data were fed into the CatBoost for training, and the parameters of the CatBoost were optimized to obtain the optimal model using SSA. Finally, the SSA-CatBoost model was adopted to classify the ecological suitability of blueberries and output the suitability types. Taking a study on a blueberry plantation in Majiang County, Guizhou Province, China as an example, the findings demonstrate that the AUC value of the SSA-CatBoost-based blueberry ecological suitability model is 0.921, which is 2.68% higher than that of the CatBoost (AUC = 0.897) and is significantly higher than Logistic Regression (AUC = 0.855), Support Vector Machine (AUC = 0.864), and Random Forest (AUC = 0.875). Furthermore, the ecological suitability of blueberries in Majiang County is mapped according to the classification results of different models. When comparing the actual blueberry cultivation situation in Majiang County, the classification results of the SSA-CatBoost model proposed in this paper matches best with the real blueberry cultivation situation in Majiang County, which is of a high reference value for the selection of blueberry cultivation sites.

show abstract