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This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

The spread of fake news on Instagram is still a problem that needs to be solved. Teenagers are a generation that is vulnerable to fake news, for example, high school students. Students need media literacy to help them protect against fake news. The media literacy skills possessed by students influence the behavior of spreading fake news that they do. This study aims to examine the effect of student media literacy on the behavior of spreading fake news on Instagram among students at public high schools in Surabaya.

This study used an online survey to100 students at five public high school in Surabaya to get the data on their ability to respond to the fake news on social media Instagram.

It was found that there is a media literacy that has a significant effect on the behavior of spreading fake news on Instagram. Based on these findings, it can be concluded that media literacy influences the behavior of spreading fake news on Instagram, and other factors influence the rest. There are seven media literacy skills, and the high category are grouping, deduction, synthesis and abstraction abilities. Meanwhile, the abilities included in the medium category are analysis and evaluation abilities.

This paper will provide insight of the media literacy levels on teenagers in metropolitan city. This result can be used as guide to add the media literacy subject at high schools and can be used to strengthen the media literacy skills among teenagers.