There has been much effort to provide eco-friendly and biodegradable materials for the next generation of composite products owing to global environmental concerns and increased awareness of renewable green resources. This review article uniquely highlights the use of green composites from natural fiber, particularly with regard to the development and characterization of chitosan, natural-fiber-reinforced chitosan biopolymer, chitosan blends, and chitosan nanocomposites. Natural fiber composites have a number of advantages such as durability, low cost, low weight, high specific strength, non-abrasiveness, equitably good mechanical properties, environmental friendliness, and biodegradability. Findings revealed that chitosan is a natural fiber that falls to the animal fiber category. As it has a biomaterial form, chitosan can be presented as hydrogels, sponges, film, and porous membrane. There are different processing methods in the preparation of chitosan composites such as solution and solvent casting, dipping and spray coating, freeze casting and drying, layer-by-layer preparation, and extrusion. It was also reported that the developed chitosan-based composites possess high thermal stability, as well as good chemical and physical properties. In these regards, chitosan-based “green” composites have wide applicability and potential in the industry of biomedicine, cosmetology, papermaking, wastewater treatment, agriculture, and pharmaceuticals.

Landslides are a natural hazard that can endanger human life and cause severe environmental damage. A landslide susceptibility map is essential for planning, managing, and preventing landslides occurrences to minimize losses. A variety of techniques are employed to map landslide susceptibility; however, their capability differs depending on the studies. The aim of the research is to produce a landslide susceptibility map for the Langat River Basin in Selangor, Malaysia, using an Artificial Neural Network (ANN). A landslide inventory map contained a total of 140 landslide locations which were randomly separated into training and testing with ratio 70:30. Nine landslide conditioning factors were selected as model input, including: elevation, slope, aspect, curvature, Topographic Wetness Index (TWI), distance to road, distance to river, lithology, and rainfall. The area under the curve (AUC) and several statistical measures of analyses (sensitivity, specificity, accuracy, positive predictive value, and negative predictive value) were used to validate the landslide predictive model. The ANN predictive model was considered and achieved very good results on validation assessment, with an AUC value of 0.940 for both training and testing datasets. This study found rainfall to be the most crucial factor affecting landslide occurrence in the Langat River Basin, with a 0.248 weight index, followed by distance to road (0.200) and elevation (0.136). The results showed that the most susceptible area is located in the north-east of the Langat River Basin. This map might be useful for development planning and management to prevent landslide occurrences in Langat River Basin.

Education for the environment and sustainable development is the one important thing for being studied. At formal school in Indonesia, it was integrated into the subject matter like social science, natural science, geography, or biology. The study about geography education for promoting sustainability had not been thoroughly done in Indonesia. Therefore, this study examined and investigated articles about geography learning and sustainability in Indonesia from 2010 to 2020. The materials were searched through the Garuda and ERIC application, by using keywords related to geography education. It was selected to published articles in journals indexed by Scopus and Sinta. The data were analyzed by a qualitative method with a content analysis approach. The foci of analysis are topics of sustainability elements, learning methods and models, learning media and resources, and students’ assessment in geography learning. All of these were investigated in concern to their relationships and achievements to sustainable development goals (SDGs). As a result, the topics of geography education in Indonesia have contained three main elements of sustainability, namely environment which the most stated, then social, and economic. The learning model and method that was mostly used was outdoor learning with contextual method. The learning media that mostly mentioned were maps, while the learning resources were local wisdom. Aspects of students’ assessment that mostly studied in geography education for promoting sustainability were students’ knowledge, attitude, and skills. Overall, this study has shown that the topics related to sustainability elements contained in the articles on geography education in Indonesia includes their learning components.

Malaysia has numerous policies, institutions, and experts with foresight and vision for its development. Nevertheless, river basin management has been lacking due to several factors such as insufficient proactive leadership roles of institutions, as well as locally authorized bodies. Reviewing of stakeholders’ role in the PENTA-HELIX partnership model (i.e., government, business, academia, community, and NGO) reveals that individuals and institutions with proactive and effective leadership roles along with top-down and bottom-up approaches can create a more strategic policy implementation resulting in better outcomes in river basin management. Local authorities with proactive leadership roles should be encouraged to use a creative and innovative key performance indicator system accompanied by mentoring and training, as well as education, to inspire a passive to active attitude change. A local authority with sound leadership roles can develop proper partnerships with its many stakeholders to improve awareness with more multitasking activities. These can be achieved by motivating all the related stakeholders towards more commitment to creating a sustainable environment. Identifying and recognizing local authorities to manage the rivers will result in more powerful actions in river management. It is essential to ensure quality control and quality assurance at various levels to bring sustainability science at the multi-stakeholders’ platforms towards an integrated river basin management to achieve a better living quality for everyone.

Landslides have been classified as the most dangerous threat around the world, causing huge damage to properties and loss of life. Increased human activity in landslide-prone areas has been a major contributor to the risk of landslide occurrences. Therefore, machine learning has been used in landslide studies to develop a landslide predictive model. The main objective of this study is to evaluate the most suitable sampling ratio for the predictive landslide model in the Langat River Basin (LRB) using Artificial Neural Networks (ANNs). The landslide inventory was divided randomly into training and testing datasets using four sampling ratios (50:50, 60:40, 70:30, and 80:20). A total of 12 landslide conditioning factors were considered in this study, including the elevation, slope, aspect, curvature, topography wetness index (TWI), distance to the road, distance to the river, distance to faults, soil, lithology, land use, and rainfall. The evaluation model was performed using certain statistical measures and area under the curve (AUC). Finally, the most suitable predictive model was chosen based on the model validation results using the compound factor (CF) method. Based on the results, the predictive model with an 80:20 ratio indicates a realistic finding and was classified as the first rank among others. The AUC value for the training dataset is 0.931, while the AUC value for the testing dataset is 0.964. These attempts will help a great deal when it comes to choosing the best ratio of training samples to testing samples to create a reliable and complete landslide prediction model for the LRB.