Currently, large quantities of municipal solid waste (MSW) in many cities of the developing countries are being dumped in informal or formal but unregulated dumpsites that threaten the ecological environment and general public health. The situation in Dar es Salaam, Tanzania is of particular concern and is further challenged by a rapidly growing population and urbanization without adequate waste management systems. Current MSW treatment options have been selected based on the judgment and the experience of individuals with authority while underestimating the role of scientifically derived techniques. This study analyzes the most efficient waste treatment options, particularly scenarios with the lowest economic and environmental costs (EcC and EnC, respectively). It uses 12 years (2006–2017) of MSW management data and compares potential waste treatment options for the identified waste streams. A total of 108 different scenarios were designed, and a multi-criteria analysis method was applied to enable the identification of 11 scenarios with acceptable EcCs and EnCs. These formed an initial decision matrix of aggregation dominance that was then categorized into four groups, each represented by the most ideal point. Finally, the dominant scenario that formed the core for all considered options was found. It costs around $274,100 USD while saving about 1585 metric tons (MT) of CO₂ emissions daily. This suggests that after all the MSW generated in the city is collected and segregated, organic waste should be composted whilst plastic, paper, glass, and ferrous metal should be recycled. After treatment, other waste will go to some form of landfill. Sustainable management of MSW in this city and others with similar conditions should consider particular local conditions and could use the methods and the findings of this study as a starting point.

Debris flow events often pose significant damage and are a threat to infrastructure and even livelihoods. Recent studies have mainly focused on determining the susceptibility of debris flow using deterministic or heuristic/probabilistic models. However, each type of model has its own significant advantages with some irreparable disadvantages. The random forest model, which is sensitive to the region where the terrain conditions are suitable for the occurrence of debris flow, was applied along with the steady-state infinite slope method, which is capable of describing the initiation mechanism of debris flow. In this manner, a random-forest-based steady-state infinite slope method was used to conduct susceptibility assessment of debris-flow at Changbai mountain area. Results showed that the assessment accuracy of the proposed random-forest-based steady-state infinite slope method reached 90.88%; however, the accuracy of just the random forest model or steady-state infinite slope method was only 88.48% or 60.45%, respectively. Compared with the single-model assessment results, the assessment accuracy of the proposed method improved by 2.4% and 30.43%, respectively. Meanwhile, the debris-flow-prone area of the proposed method was reduced. The random-forest-based steady-state infinite slope method inherited the excellent diagnostic performance of the random-forest models in the region where the debris flow disaster already occurred; meanwhile, this method further refined the debris-flow-prone area from the suitable terrain area based on physico-mechanical properties; thus, the performance of this method was better than those of the other two models.