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• Landscape ecological vulnerability was assessed using bagging ANN-MLP and RF models. • B-MLP model was found best fit model for LEV assessment based on ROC curve, recall, precision, F1-score and accuracy. • Largest area under very high LEV was recorded in the southern part of the plains. • Rainfall, soil, vegetation and LULC were found influencing factors for LEV. • Plantation, embankments and geotextile technology are suggested to check erosion. Riverbank erosion is one of the most catastrophic hazards that renders floodplains vulnerable across the world vulnerable. It creates a significant negative impact on the environment and socio-economic life. This paper attempts to assess the landscape ecological vulnerability (LEV) to riverbank erosion in the Middle Brahmaputra floodplains of Assam, India by employing two machine learning models, namely artificial neural network-multilayer perceptron (ANN-MLP) and random forest (RF). Bagging ensembles were created for both ANN-MLP and RF (B-MLP and B-RF) to generate LEV zones. A total of eleven site-specific parameters were considered for the study. The receiver operating characteristic (ROC) based area under curve (AUC), accuracy, precision, recall and F1-score were used to validate the models and judge the models' performance. A sensitivity analysis was performed to deduce the most influential LEV parameters. The results revealed that B-MLP was the better-performing model compared to B-RF based on all five validation metrics. The largest area was found under very high vulnerability zone followed by very low, low, high and moderate vulnerability zones, based on both ensemble models. The western part of the floodplains was found to be more vulnerable than the eastern part. Moreover, the southern bank faced more vulnerability in comparison to the northern bank. The factors namely rainfall, soil type, vegetation and land /use land cover (LULC) influenced bank erosion vulnerability. This research provides evidence that the study area is under severe threat to riverbank erosion and urgently requires the implementation of effective mitigation measures. The study might benefit policymakers and local stakeholders to protect the floodplains from bank erosion and reduce vulnerability. [ABSTRACT FROM AUTHOR]

Electricity generation for rural applications through biomethanation process is very promising in countries like India where availability of biomass feedstock is very high. However, sustainability of biomass power plants requires meticulous planning starting from assessment of feedstock availability to collection, storage, transport and conversion in power plant. All these parameters are spatially inter-linked and therefore use of spatial tools such as GIS is helpful in such planning. This paper investigates electrical power generation potential of a newly constructed biomethanation plant in Sonitpur district of Assam, India. Spatial availability of locally available biomass feedstocks for methane generation is assessed using GIS. The GIS is also used for optimal biomass collection and transportation network design. Altogether, 7 different types of biomass feedstock (kitchen waste, vegetable waste, cow dung, pig droppings, goat droppings, poultry droppings and rice straw) are identified in the surrounding villages of the plant within optimum biomass supply radius. Biomass feedstock of the area can produce 37,000 m 3 methane; sufficient for continuous generation of 5.3 MW electricity. Power supply from the proposed plant would improve rural living standard and key areas such as education and health will be particularly benefited. However, biomass assessment and supply logistics would require managerial attention to minimize cost of power generation. [ABSTRACT FROM AUTHOR]