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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]

The agriculture sector of Assam, a state in the northeast part of India, supports more than 75% of its population. Agriculture in Assam is highly susceptible to flood risk, not only through reduced time availability for cropping but also through higher production risks due to varying flood timing, frequency and intensity. Farmers however have few institutional safeguards to hedge that risk. This paper explores the role of crop diversification as a means for the farmers in the flood plains of Assam to cope with flood induced production risks and limits. An application of a generalized linear model to original farm level survey data reveals that farmers in the plains of Assam who are confronted with greater flood risk tend to diversify their cropping pattern more. This diversification is also shown to be a strategy that extracts higher returns from farming. [ABSTRACT FROM AUTHOR]