Adaptive neuro-fuzzy inference system to forecast peak gust speed during thunderstorms

The aim of the present study is to develop an adaptive neuro-fuzzy inference system (ANFIS) to forecast the peak gust speed associated with thunderstorms during the pre-monsoon season (April?CMay) over Kolkata (22°32??N, 88°20??E), India. The pre-monsoon thunderstorms during 1997?C2008 are considered in this study to train the model. The input parameters are selected from various stability indices using statistical skill score analysis. The most useful and relevant stability indices are taken to form the input matrix of the model. The forecast through the hybrid ANFIS model is compared with non-hybrid radial basis function network (RBFN), multi layer perceptron (MLP) and multiple linear regression (MLR) models. The forecast error analyses of the models in the test cases reveal that ANFIS provides the best forecast of the peak gust speed with 3.52% error, whereas the errors with RBFN, MLP, and MLR models are 10.48, 11.57, and 12.51%, respectively. During the validation with the 2009 observations of the India Meteorological Department (IMD), the ANFIS model confirms its superiority over other comparative models. The forecast error during the validation of the ANFIS model is observed to be 3.69%, with a lead time of <12?h, whereas the errors with RBFN, MLP, and MLR are 12.25, 13.19, and 14.86%, respectively. The ANFIS model may, therefore, be used as an operational model for forecasting the peak gust speed associated with thunderstorms over Kolkata during the pre-monsoon season.     

Deep resistivity sounding studies for probing deep fresh aquifers in the coastal area of Orissa, India

Exploration and exploitation of groundwater in sedimentary areas are reasonably simple. However, the problem of salinity in coastal areas makes the job very difficult, especially when the freshwater aquifers are not extensive and are entrapped between saline aquifers. States along the eastern coast of India, particularly Orissa with respect to the Mahanadi basin, have acute problems with groundwater salinity. It has been possible to locate horizons of fresh groundwater entrapped between deep saline aquifers in the southwestern part of Mahanadi delta, with the help of deep resistivity soundings along the Delang-Puri profile. This finding has been validated through boreholes and checked with electrical logs of this region. Three freshwater aquifers have been detected: one at shallow depth between 20 and 60?m, the second in the depth range of 90??60?m, and the third in the fractured/weathered basement. The second freshwater aquifer has the most potential; it has a thickness range of 20??0?m and it could be exploited to overcome problematic salinity issues. In general, the depth to basement is variable and it increases seaward.     

On some characteristics of severe rainstorms of India

Summary Detailed examination of daily rainfall data for India (and other relevant publications regarding severe rainstorms) show that during the last 110 years, the Indian region recorded 231 severe rainstorms. Of these, 27 were of 2-day duration and the rest (204) were of 3-day duration. Within a rainstorm, maximum rain fell on the 2nd day on 63% of occasions. It was also observed that the average raindepths obtained from slow moving rainstorms were greater in magnitude than those of fast moving rainstorms. All these rainstorms, during their movement through the country, had their rain centres at about 308 locations. This study has shown that only 19 locations recorded severe rainstorm centres on five or more occasions. Maximum number of rain centres recorded for one location was 13.With 2 Figures     

Envelope Depth-Area-Duration raindepths for different homogeneous rainstorm zones of the Indian region

Summary About one hundred severe rainstorms occurred in the Indian region during the period between 1880 and 1990. The centres of these severe rainstorms occur mostly in four preferred zones. The two major zones where the bulk of rainstorm centres are located are in north and central India. These two zones have been further sub-divided into four homogeneous subzones for the purpose of rainstorm transposition. For each of these homogeneous sub-zones and zones envelope Depth-Area-Duration (DAD) raindepths have been calculated. These are based on the DAD data of severe rainstorms that have occurred within each zone in order to obtain probable maximum precipitation (PMP) estimates.With 4 Figures     

SIFT-FANN: An efficient framework for spatio-spectral fusion of satellite images

Image fusion techniques are widely used for remote sensing data. A special application is for using low resolution multi-spectral image with high resolution panchromatic image to obtain an image having both spectral and spatial information. Alignment of images to be fused is a step prior to image fusion. This is achieved by registering the images. This paper proposes the methods involving Fast Approximate Nearest Neighbor (FANN) for automatic registration of satellite image (reference image) prior to fusion of low spatial resolution multi-spectral QuickBird satellite image (sensed image) with high spatial resolution panchromatic QuickBird satellite image. In the registration steps, Scale Invariant Feature Transform (SIFT) is used to extract key points from both images. The keypoints are then matched using the automatic tuning algorithm, namely, FANN. This algorithm automatically selects the most appropriate indexing algorithm for the dataset. The indexed features are then matched using approximate nearest neighbor. Further, Random Sample Consensus (RanSAC) is used for further filtering to obtain only the inliers and co-register the images. The images are then fused using Intensity Hue Saturation (IHS) transform based technique to obtain a high spatial resolution multi-spectral image. The results show that the quality of fused images obtained using this algorithm is computationally efficient.     

Potential CO<Subscript>2</Subscript> Emission Due to Loss of Above Ground Biomass from the Indian Sundarban Mangroves During the Last Four Decades

Sundarban, the largest single patch of mangrove forest of the world is shared by Bangladesh (~ 60 %) and India (~ 40 %). Loss of mangrove biomass and subsequent potential emission of carbon dioxide is reported from different parts of the world. We estimated the loss of above ground mangrove biomass and subsequent potential emission of carbon dioxide in the Indian part of the Sundarban during the last four decades. The loss of mangrove area has been estimated with the help of remotely sensed data and potential emission of carbon dioxide has been evaluated with the help of published above ground biomass data of Indian Sundarban. Total loss of mangrove area was found to be 107 km² between the year 1975 and 2013. Amongst the total loss ~60 % was washed away in the water by erosion, ~ 23 % was converted into barren lands and the rest were anthropogenically transformed into other landforms. The potential carbon dioxide emission due to the degradation of above ground biomass was estimated to be 1567.98 ± 551.69 Gg during this period, which may account to 64.29 million $ in terms of the social cost of carbon. About three-forth of the total mangrove loss was found in the peripheral islands which are much more prone to erosion. Climate induced changes and anthropogenic land use change could be the major driving force behind this loss of ‘blue carbon’.     

Predictability of landfall location and surge height of tropical cyclones over North Indian Ocean (NIO)

Natural Hazards - Forecasting, with precision, the location of landfall and the height of surge of cyclonic storms prevailing over any ocean basin is very important to cope with the associated...