Climate change has caused vulnerability not only to the forest ecosystem but also to forest-dependent communities. Therefore, its management is essential to increase forest ecosystem services and reduce vulnerability to climate change using an integrated approach. Although many scientific studies examined climate change impact on forest ecosystems, forest vulnerability assessment, including forest sensitivity, adaptability, sustainability and effective management was found to be scant in the existing literature. Through a systematic review from 1990 to 2019, this paper examined forest vulnerability to climate change and its management practices. In this paper, descriptive, mechanism and thematic analyses were carried out to analyze the state of existing research, in order to understand the concept of vulnerability arising from climate change and forest management issues. The present study proposed a framework for integrated forest assessment and management for addressing such issues in future research. The conversion of forest land into other land uses, forest fragmentation, forest disturbance and the effects of climate change on the forest ecosystem are the existing problems. Forest vulnerability, effective adaptation to forest ecosystems and long-term sustainability are priority areas for future research. This study also calls for undertaking researchers at a local scale to involve communities for the effective management of forest ecosystems.

In this paper, we developed highly accurate ensemble machine learning models integrating Reduced Error Pruning Tree (REPT) as a base classifier with the Bagging (B), Decorate (D), and Random Subspace (RSS) ensemble learning techniques for spatial prediction of rainfall-induced landslides in the Uttarkashi district, located in the Himalayan range, India. To do so, a total of 103 historical landslide events were linked to twelve conditioning factors for generating training and validation datasets. Root Mean Square Error (RMSE) and Area Under the receiver operating characteristic Curve (AUC) were used to evaluate the training and validation performances of the models. The results showed that the single REPT model and its derived ensembles provided a satisfactory accuracy for the prediction of landslides. The D-REPT model with RMSE = 0.351 and AUC = 0.907 was identified as the most accurate model, followed by RSS-REPT (RMSE = 0.353 and AUC = 0.898), B-REPT (RMSE = 0.396 and AUC = 0.876), and the single REPT model (RMSE = 0.398 and AUC = 0.836), respectively. The prominent ensemble models proposed and verified in this study provide engineers and modelers with insights for development of more advanced predictive models for different landslide-susceptible areas around the world.

Groundwater is an essential source of water especially in arid and semi-arid regions of the world. The demand for water due to exponential increase in population has created stresses on available groundwater resources. Further, climate change has affected the quantity of water globally. Many parts of Indian cities are experiencing water scarcity. Thus, assessment of groundwater potential is necessary for sustainable utilization and management of water resources. We utilized a novel ensemble approach using artificial neural network multi-layer perceptron (ANN-MLP), random forest (RF), M5 prime (M5P) and support vector machine for regression (SMOReg) models for assessing groundwater potential in the Parbhani district of Maharashtra in India. Ten site-specific influencing factors, elevation, slope, aspect, drainage density, rainfall, water table depth, lineament density, land use land cover, geomorphology, and soil types, were integrated for preparation of groundwater potential zones. The results revealed that the largest area of the district was found under moderate category GWP zone followed by poor, good, very good and very poor. Spatial distribution of GWP zones showed that Poor GWPZs are spread over north, central and southern parts of the district. Very poor GWPZs are mostly found in the north-western and southern parts of the district. The study calls for policy implications to conserve and manage groundwater in these parts. The ensembled model has proved to be effective for assessment of GWP zones. The outcome of the study may help stakeholders efficiently utilize groundwater and devise suitable strategies for its management. Other geographical regions may find the methodology adopted in this study effective for groundwater potential assessment.