The Western Ghats (WG) of India, one of the hottest biodiversity hotspots in the world, has witnessed major land-use and land-cover (LULC) change in recent times. The present research was aimed at studying the patterns of LULC change in WG during 1985-1995-2005, understanding the major drivers that caused such change, and projecting the future (2025) spatial distribution of forest using coupled logistic regression and Markov model. The International Geosphere Biosphere Program (IGBP) classification scheme was mainly followed in LULC characterization and change analysis. The single-step Markov model was used to project the forest demand. The spatial allocation of such forest demand was based on the predicted probabilities derived through logistic regression model. The R statistical package was used to set the allocation rules. The projection model was selected based on Akaike information criterion (AIC) and area under receiver operating characteristic (ROC) curve. The actual and projected areas of forest in 2005 were compared before making projection for 2025. It was observed that forest degradation has reduced from 1985-1995 to 1995-2005. The study obtained important insights about the drivers and their impacts on LULC simulations. To the best of our knowledge, this is the first attempt where projection of future state of forest in entire WG is made based on decadal LULC and socio-economic datasets at the Taluka (sub-district) level.
One of the long-standing challenges in Artificial Intelligence for learning goaldirected behavior is to build a single agent which can solve multiple tasks. Recent progress in multi-task learning for goal-directed sequential problems has been in the form of distillation based learning wherein a student network learns from multiple task-specific expert networks by mimicking the task-specific policies of the expert networks. While such approaches offer a promising solution to the multi-task learning problem, they require supervision from large expert networks which require extensive data and computation time for training. In this work, we propose an efficient multi-task learning framework which solves multiple goaldirected tasks in an on-line setup without the need for expert supervision. Our work uses active learning principles to achieve multi-task learning by sampling the harder tasks more than the easier ones. We propose three distinct models under our active sampling framework. An adaptive method with extremely competitive multi-tasking performance. A UCB-based meta-learner which casts the problem of picking the next task to train on as a multi-armed bandit problem. A meta-learning method that casts the next-task picking problem as a full Reinforcement Learning problem and uses actor critic methods for optimizing the multi-tasking performance directly. We demonstrate results in the Atari 2600 domain on seven multi-tasking instances: three 6-task instances, one 8-task instance, two 12-task instances and one 21-task instance.
Exploitation of extra heavy oil resource will be the main R & D challenge in the future. In India Mehsana oil field is the one of the extra heavy oil belts. In such type of fields advanced EOR is of primary concern to increase the recovery. Apart from conventional thermal methods this paper discusses about the commercialization and broad application of (MWAGD) as alternative thermal method in the Mehsana oil field. In this study, applicability of microwave heating for heavy oil from Mehsana heavy oil field was experimented and analyzed quantitatively. In this paper a core sample of Mehsana field is taken and heated with the microwave energy provided by a microwave source consisting of magnetron tubes to generate the microwave power. Micro-Wave radiation is non-ionizing so requires high frequency current (3000 MHz) which causes friction by vibration of molecules which results in dielectric heating of the sample. In this experiment heat transfer between microwave source and core is described quantitatively. Temperature and viscosity profile of the gravity drained oil are observed graphically and analytically. Effects of initial oil and water saturations, wettability, porosity, permeability are discussed with respect to the drained oil. Economic evaluation is also done by comparing the costs in USD/barrel between the above proposed method and the method which is being run presently i.e. Steam Assisted Gravity Drainage (SAGD). In this paper authors have also shown the work-how of applicability of MWAGD in the Mehsana field in present scenario. MWAGD is cost effective than the conventional EOR methods. It is also more efficient and less time consuming as there is speedy heat transfer by dielectric heating. MWAGD does not cause any consequential damage and provides greater oil displacement efficiencies. Also, the heavy deposit of residual coke or carbon is rooted out.
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