Comparison between deep learning and tree‐based machine learning approaches for landslide susceptibility mapping

The efficiency of deep learning and tree‐based machine learning approaches has gained immense popularity in various fields. One deep learning model viz. convolution neural network (CNN), artificial neural network (ANN) and four tree‐based machine learning models, namely, alternative decision tree (ADTree), classification and regression tree (CART), functional tree and logistic model tree (LMT), were used for landslide susceptibility mapping in the East Sikkim Himalaya region of India, and the results were compared. Landslide areas were delimited and mapped as landslide inventory (LIM) after gathering information from historical records and periodic field investigations. In LIM, 91 landslides were plotted and classified into training (64 landslides) and testing (27 landslides) subsets randomly to train and validate the models. A total of 21 landslide conditioning factors (LCFs) were considered as model inputs, and the results of each model were categorised under five susceptibility classes. The receiver operating characteristics curve and 21 statistical measures were used to evaluate and prioritise the models. The CNN deep learning model achieved the priority rank 1 with area under the curve of 0.918 and 0.933 by using the training and testing data, quantifying 23.02% and 14.40% area as very high and highly susceptible followed by ANN, ADtree, CART, FTree and LMT models. This research might be useful in landslide studies, especially in locations with comparable geophysical and climatological characteristics, to aid in decision making for land use planning

Between China and South Asia: A Middle Asian corridor of crop dispersal and agricultural innovation in the Bronze Age

© The Author(s) 2016. The period from the late third millennium BC to the start of the first millennium AD witnesses the first steps towards food globalization in which a significant number of important crops and animals, independently domesticated within China, India, Africa and West Asia, traversed Central Asia greatly increasing Eurasian agricultural diversity. This paper utilizes an archaeobotanical database (AsCAD), to explore evidence for these crop translocations along southern and northern routes of interaction between east and west. To begin, crop translocations from the Near East across India and Central Asia are examined for wheat (Triticum aestivum) and barley (Hordeum vulgare) from the eighth to the second millennia BC when they reach China. The case of pulses and flax (Linum usitatissimum) that only complete this journey in Han times (206 BC–AD 220), often never fully adopted, is also addressed. The discussion then turns to the Chinese millets, Panicum miliaceum and Setaria italica, peaches (Amygdalus persica) and apricots (Armeniaca vulgaris), tracing their movement from the fifth millennium to the second millennium BC when the Panicum miliaceum reaches Europe and Setaria italica Northern India, with peaches and apricots present in Kashmir and Swat. Finally, the translocation of japonica rice from China to India that gave rise to indica rice is considered, possibly dating to the second millennium BC. The routes these crops travelled include those to the north via the Inner Asia Mountain Corridor, across Middle Asia, where there is good evidence for wheat, barley and the Chinese millets. The case for japonica rice, apricots and peaches is less clear, and the northern route is contrasted with that through northeast India, Tibet and west China. Not all these journeys were synchronous, and this paper highlights the selective long-distance transport of crops as an alternative to demic-diffusion of farmers with a defined crop package

Spatial assessment of drought vulnerability using fuzzy-analytical hierarchical process: a case study at the Indian state of Odisha

Droughts can be regarded as one of the most spatially complex geohazards, causing a severe impact on socio-economic aspects. Preparing a comprehensive drought management plan is necessary to mitigate drought risks, and the first step towards achieving it is the preparation of drought vulnerability map. The present study integrates geospatial methods with Fuzzy-Analytical Hierarchy Process (Fuzzy-AHP) technique, to prepare a drought vulnerability map for Odisha, India. Total of 24 parameters under 2 separate vulnerability categories, namely physical and socio-economic, was listed. Spatial layers were prepared for each parameter, and fuzzy membership approach was used to fuzzify each layer, and AHP was used to measure the weights of each parameter using pair-wise comparison matrices. Finally, drought vulnerability maps with five drought vulnerability classes (very-high, high, moderate, low, and very-low) were developed using weighted overlay method. The results show that 33.94% of the region falls under high-drought vulnerability category. Further, the approach was validated using statistical metrics, like area under the Receiver Operating Characteristics curves, Accuracy, Root-Mean-Square-Error and Mean-Absolute-Error. The results imply that the applied method is effective for determining drought vulnerability in the region, which would help the planners for formulating drought mitigation strategies