Th is study empirically analyses the impact of family life cycles on the family farm scale of rural households in Southern China. Th e ordered Probit modelling is applied to examine the survey data that comprise 2040 valid questionnaires distributed in 88 villages of the Fujian province in China. Th e family life cycle has a remarkable infl uence on the family farm scale as a whole. Th e numbers of children and farming people in a family have a positive signifi cant eff ects on the family farm scale. In addition, the individual characteristics of female householders have signifi cant eff ects on the family farm scale. Meanwhile, the family characteristics diff er at fi ve defi ned stages of the family life cycle. Th e study covers the gap in the literature on the eff ects of family structure on the rural household economic behaviour, in particular, on the impact of the family life cycles on the family farm scale.
In the mapping and assessment of mountain hazard susceptibility using machine learning models, the selection of model parameters plays a critical role in the accuracy of predicting models. In this study, we present a novel approach for developing a prediction model based on random forest (RF) by incorporating ensembles of hyperparameter optimization. The performance of the RF model is enhanced by employing a Bayesian optimization (Bayes) method and a genetic algorithm (GA) and verified in the Wudu section of the Bailong River basin, China, which is a typical hazard-prone, mountainous area. We identified fourteen influential factors based on field measurements to describe the “avalanche–landslide–debris flow” hazard chains in the study area. We constructed training (80%) and validation (20%) datasets for 378 hazard sites. The performance of the models was assessed using standard statistical metrics, including recall, confusion matrix, accuracy, F1, precision, and area under the operating characteristic curve (AUC), based on a multicollinearity analysis and Relief-F two-step evaluation. The results indicate that all three models, i.e., RF, GA-RF, and Bayes-RF, achieved good performance (AUC: 0.89~0.92). The Bayes-RF model outperformed the other two models (AUC = 0.92). Therefore, this model is highly accurate and robust for mountain hazard susceptibility assessment and is useful for the study area as well as other regions. Additionally, stakeholders can use the susceptibility map produced to guide mountain hazard prevention and control measures in the region.
In mountain hazard susceptibility mapping and assessment using machine learning models, the choice of model parameters is a significant factor in determining the accuracy of the model prediction. This work provides a novel method for developing a random forest (RF)-based prediction model by embedding hyperparametric optimization ensembles. The performance of the RF model is enhanced by employing a Bayesian optimization (Bayes) method and a Genetic algorithm (GA) and verified in the Wudu section of the Bailong River basin, China, a typical mountainous hazard-prone area. Fourteen influencing factors were selected in conjunction with field measurements to characterize the cascading ''collapse-landslide-debris flow'' hazard chains in the study area, and datasets for training (80%) and validation (20%) models were constructed for 378 hazard sites. Based on multicollinearity analysis and Relief-F two-step evaluation, typical statistical performance metrics such as the confusion matrix, recall, precision, accuracy, F1 and area under the operating characteristic curve (AUC) of individuals were used to evaluate model performance. Our results revealed that all the 3 models (i.e., RF, GA-RF, and Bayes-RF) performed well (AUC: 0.89 ~ 0.92), but the Bayes-RF model performed the best (AUC = 0.92), which can be used as a highly accurate and robust mountain hazard susceptibility assessment model applicable in the study area and other regions. Meanwhile, the generated susceptibility map can guide stakeholders in making appropriate mountain hazard prevention and control measurements in the region.
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