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Despite yielding considerable degrees of accuracy in landslide predictions, the outcomes of different landslide susceptibility models are prone to spatial disagreement; and therefore, uncertainties. Uncertainties in the results of various landslide susceptibility models create challenges in selecting the most suitable method to manage this complex natural phenomenon. This study aimed to propose an approach to reduce uncertainties in landslide prediction, diagnosing spatial agreement in machine learning-based landslide susceptibility maps. It first developed landslide susceptibility maps of Cox’s Bazar district of Bangladesh, applying four machine learning algorithms: K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM), featuring hyperparameter optimization of 12 landslide conditioning factors. The results of all the four models yielded very high prediction accuracy, with the area under the curve (AUC) values range between 0.93 to 0.96. The assessment of spatial agreement of landslide predictions showed that the pixel-wise correlation coefficients of landslide probability between various models range from 0.69 to 0.85, indicating the uncertainty in predicted landslides by various models, despite their considerable prediction accuracy. The uncertainty was addressed by establishing a Logistic Regression (LR) model, incorporating the binary landslide inventory data as the dependent variable and the results of the four landslide susceptibility models as independent variables. The outcomes indicated that the RF model had the highest influence in predicting the observed landslide locations, followed by the MLP, SVM, and KNN models. Finally, a combined landslide susceptibility map was developed by integrating the results of the four machine learning-based landslide predictions. The combined map resulted in better spatial agreement (correlation coefficients range between 0.88 and 0.92) and greater prediction accuracy (0.97) compared to the individual models. The modelling approach followed in this study would be useful in minimizing uncertainties of various methods and improving landslide predictions.

This paper deals with changes in wage and working conditions in the garment industry in Bangladesh. It focuses on economic upgrading of the industry brought about by functional upgrading. Such upgrading and changes in market conditions have increased firm revenues per worker. The paper then discusses the local situation related to workers' organization and struggle, which recently resulted in increases in wages and improvements in working conditions. It deals with the impact of a high geographic concentration of workers and high growth of the industry in terms of increasing the voice of workers and forcing government and the industry to improve wages and working conditions. These improvements were the result of changes in horizontal relations within the garment global production network. The paper then looks at the role of vertical relations, between buyers and suppliers, in constraining such improvements. The paper ends with a consideration of the way in which combining vertical and horizontal relations could be brought into the tripartite industrial relations structure.

Since the dawn of human civilization, forced migration scenarios have been witnessed in different regions and populations, and is still present in the twenty-first century. The current largest population of stateless refugees in the world, the Rohingya people, reside in the southeastern border region of Bangladesh. Due to rapid expansion of refugee camps and lack of suitable locations, a large proportion of the infrastructure are at risk of landslides. This study aims to use machine learning for predicting landslide risk of camp infrastructure using geospatial features. Four supervised classification algorithms have been employed viz., (i) Logistic Regression (LR), (ii) Multi-Layer Perceptron (MLP), (iii) Gradient Boosted Trees (GBT) and (iv) Random Forest (RF) and applied on preprocessing varied versions of features. Results show that RF achieves accuracy of 76.19% and AUC of 0.76 on un-scaled features which is higher than all other algorithms. The applications of the study reside in refugee management and landslide susceptibility mapping of Rohingya camps, which can both potentially save refugee lives and serve as a case study for global applications.