Artificial Neural Network (ANN) Based Inversion of Benthic Substrate Bottom Type and Bathymetry in Optically Shallow Waters - Initial Model Results

Ocean-colour remote sensing in optically shallow waters is influenced by contribution from the water column depth as well as by the substrate type. Therefore, it is required to include the contribution from the water column and substrate bottom type for bathymetry estimation. In this report we demonstrate the use of Artificial Neural Network (ANN) based approach to spectrally distinguish various benthic bottom types and estimate depth of substrate bottom simultaneously in optically shallow waters. We have used in-water radiative transfer simulation modeling to generate simulated top-of-the-water column reflectance the four major benthic bottom types viz. sea grass, coral sand, green algae and red algae using Hydrolight simulation model. The simulated remote sensing reflectance, for the four benthic bottom types having benthic bottom depth up to 30 m were generated for moderately clear waters. A multi-layer perceptron (MLP) type neural network was trained using the simulated data. ANN based approach was used for classification of the benthic bottom type and simultaneous inversion of bathymetry. Simulated data was inverted to yield benthic bottom type classification with an accuracy of ~98% for the four benthic substrate types and the substrate depth were estimated with an error of 0% for sea grass, 1% for coral sand and 1–3% for green and red algae up to 25 m, whereas for substrate bottom deeper than 25 m depth the classification errors increased by 2–5% for three substrate bottom types except sea grass bottom type. The initial results are promising which needs validation using the in-situ measured remote sensing reflectance spectra for implementing further on satellite data.     

Vulnerability and adaptation to climate variability and water stress in Uttarakhand State, India

This paper presents a participatory approach to investigate vulnerability and adaptive capacity to climate variability and water stress in the Lakhwar watershed in Uttarakhand State, India. Highly water stressed microwatersheds were identified by modelling surface runoff, soil moisture development, lateral runoff, and groundwater recharge. The modelling results were shared with communities in two villages, and timeline exercises were carried out to allow them to trace past developments that have impacted their lives and livelihoods, and stimulate discussion about future changes and possible adaptation interventions.     

Wind-driven estuarine turbidity maxima in Mandovi Estuary,central west coast of India

Systematic studies on the suspended particulate matter (SPM) measured on a seasonal cycle in the Mandovi Estuary, Goa indicate that the average concentrations of SPM at the regular station are ∼20mg/l, 5mg/l, 19mg/l and 5mg/l for June–September, October–January, February–April and May, respectively. SPM exhibits low-to-moderate correlation with rainfall indicating that SPM is also influenced by other processes. Transect stations reveal that the SPM at sea-end stations of the estuary are at least two orders of magnitude greater than those at the river-end during the monsoon. Estuarine turbidity maximum (ETM) of nearly similar magnitude occurs at the same location in two periods, interrupted by a period with very low SPM concentrations. The ETM occurring in June–September is associated with low salinities; its formation is attributed to the interactions between strong southwesterly winds (5.1–5.6ms⁻¹) and wind-induced waves and tidal currents and, dominant easterly river flow at the mouth of the estuary. The ETM occurring in February–April is associated with high salinity and is conspicuous. The strong NW and SW winds (3.2–3.7ms⁻¹) and wind-driven waves and currents seem to have acted effectively at the mouth of the estuary in developing turbidity maximum. The impact of sea breeze appears nearly same as that of trade winds and cannot be underestimated in sediment resuspension and deposition     

The Armagh Observatory Meteor Camera Cluster: Overview and Status

Armagh Observatory installed a sky monitoring system consisting of two wide angle (90° × 52°) and one medium angle (52° × 35°) cameras in July 2005. The medium angle camera is part of a double station setup with a similar camera in Bangor, ∼73 km ENE of Armagh. All cameras use UFOCapture to record meteors automatically; software for off-line photometry, astrometry and double station calculations is currently being developed. The specifications of the cameras and cluster configuration are described in detail. 2425 single station meteors (1167, 861 and 806 by the medium-angle and the wide-angle cameras respectively) and 547 double station meteors were recorded during the months July 2005 to Dec 2006. About 212 double station meteors were recorded by more than one camera in the cluster. The effects of weather conditions on camera productivity are discussed. The distribution of single and double station meteor counts observed for the years 2005 and 2006 and calibrated for weather conditions are presented.     

Applications of generalized additive model (GAM) to satellite-derived variables and fishery data for prediction of fishery resources distributions in the Arabian Sea

This study aims to illustrate how remotely sensed oceanic variables and fishing operations data can be used to predict suitable habitat of fishery resources in Geographic Information System. We used sea surface height anomaly (SSHa), sea surface temperature (SST), chlorophyll concentration (CC), photosynthetically active radiation (PAR) and fishing depth as predictor variables. Fishery data of Indian squid (Loligo spp.) and catfish (Tachysurus spp.) for study period (1998–2004) were segregated randomly to create training and validation. Catch was normalized into Catch per unit Effort (kg h^?1). Generalized additive modelling was performed on training data and then tested on validation data. Suitable ranges of SST, CC, SSHa and PAR for different species distributions were derived and integrated to predict their spatial distributions. Results indicated good match between predicted and actual catch. Monthly probability maps of predicted habitat areas coincide with high catch of the particular month for the study period.     

A Technique of Rainfall Assimilation for Dynamic Extended Range Monsoon Prediction

Summary ?A methodology has been developed to assimilate satellite-measured rainfall during the initial phase of model integration for extended range monsoon prediction. The vertical profiles of latent heating corresponding to different rain rates have been derived from the model statistics. These heating rates have been assimilated through nudging in the thermodynamics equation of the model. This procedure of assimilating observed heating has corrected the simulation of heating location in the model and consequently removed the anomalous sinking motion over Indian landmass. With the correction of vertical circulation, both mean July rainfall over India and the distribution have improved. Interannual variability has been brought out for the years 1987 and 1988. In view of the availability of rainfall profile from Tropical Rain Measuring Mission (TRMM) there is a scope of adopting this method of assimilating observed rainfall, for extended range monsoon prediction. Received February 12, 1999/Revised May 4, 1999     

Impact of Indian ocean dipole on the coastal upwelling features off the southwest coast of India

A three-dimensional regional ocean model is used to examine the impact of positive Indian ocean dipole (pIOD) events on the coastal upwelling features at the southwest coast of India (SWCI). Two model experiments are carried out with different surface boundary conditions that prevailed in the normal and pIOD years from 1982 to 2010. Model experiments demonstrate the weakening of coastal upwelling at the SWCI in the pIOD years. The reduced southward meridional wind stress off the SWCI leads to comparatively lower offshore Ekman transport during August–October in the pIOD years to that in normal years. The suppressed coastal upwelling results in warmer sea surface temperature and deeper thermocline in the pIOD years during June–September. The offshore spatial extent of upwelled colder (<?22 °C) water was up to 75.5° E in August–September in normal years that was limited up to 76.2° E in pIOD years. The heat budget analysis reveals the decreased contribution of vertical entrainment process to the mixed layer cooling in pIOD years which is almost half of that of normal years in October. The net heat flux term shows warming tendency during May–November with a higher magnitude (+?0.4 °C day^?1) in normal years than pIOD years (+?0.28 °C day^?1). The biological productivity is found to reduce during the pIOD years as the concentration of phytoplankton and zooplankton decreases over the region of coastal upwelling at SWCI. Nitrate concentration in the pIOD years dropped by half during August–September and dropped by an order of magnitude in October as compared to its ambient concentration of 13 μmol L^?1 in normal years.     

Atmospheric Correction Over Coastal Turbid Waters of Bay of Bengal Using OCEANSAT-I Ocean Colour Monitor (OCM) Data

Space-borne ocean-colour remote sensor-detected radiance is heavily contaminated by solar radiation backscattered by the atmospheric air molecules and aerosols. Hence, the first step in ocean-colour data processing is the removal of this atmospheric contribution from the sensor-detected radiance to enable detection of optically active oceanic constituents e.g. chlorophyll-a, suspended sediment etc. In standard atmospheric correction procedure for OCEANSAT-1 Ocean Colour Monitor (OCM) data, NIR bands centered at 765 and 865 nm wavelengths were used for aerosol characterization. Due to high absorption by water molecules, ocean surface in these two wavelengths acts as dark background, therefore, sensor detected radiance can be assumed to have major contribution from atmospheric scattering. For coastal turbid waters this assumption of dark surface fails due to the presence of highly scattering sediments which causes sufficient water-leaving radiance in NIR bands and lead to over-estimation of aerosol radiance resulting in negative water leaving radiance for λ < 700 nm. In the present study, for the turbid coastal waters in the northern Bay of Bengal, the concept of spatial homogeneity of aerosol and water leaving reflectance has been applied to perform atmospheric correction of OCAEANSAT-1 OCM data. The results of the turbid water atmospheric correction have also been validated using in-situ measured water-leaving radiance. Comparison of satellite derived water-leaving radiance for five coastal stations with in-situ measured radiance spectra, indicates an improvement over the standard atmospheric correction algorithm giving physically realistic and positive values. Root Mean Square Error (RMSE) between the in-situ measured and satellite derived water leaving radiance for wavelengths 412 nm, 443 nm, 490 nm, 512 nm and 555 nm was found to be 1.11, 0.718, 0.575, 0.611 and 0.651%, respectively, using standard atmospheric correction procedure. By the use of spatial homogeneity concept, this error was reduced to 0.125, 0.173, 0.176, 0.225, and 0.290 and the correlation coefficient arrived at 0.945, which is an improvement over the standard atmospheric correction procedure.     

A comparative study of sequential minimal optimization-based support vector machines,vote feature intervals,and logistic regression in landslide susceptibility assessment using GIS

Landslide susceptibility assessment using GIS has been done for part of Uttarakhand region of Himalaya (India) with the objective of comparing the predictive capability of three different machine learning methods, namely sequential minimal optimization-based support vector machines (SMOSVM), vote feature intervals (VFI), and logistic regression (LR) for spatial prediction of landslide occurrence. Out of these three methods, the SMOSVM and VFI are state-of-the-art methods for binary classification problems but have not been applied for landslide prediction, whereas the LR is known as a popular method for landslide susceptibility assessment. In the study, a total of 430 historical landslide polygons and 11 landslide affecting factors such as slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to rivers, distance to lineaments, and rainfall were selected for landslide analysis. For validation and comparison, statistical index-based methods and the receiver operating characteristic curve have been used. Analysis results show that all these models have good performance for landslide spatial prediction but the SMOSVM model has the highest predictive capability, followed by the VFI model, and the LR model, respectively. Thus, SMOSVM is a better model for landslide prediction and can be used for landslide susceptibility mapping of landslide-prone areas.     

Establishing a predictive performance indicator for real‐time hybrid simulation

Real‐time hybrid simulation (RTHS) is increasingly being recognized as a powerful cyber‐physical technique that offers the opportunity for system evaluation of civil structures subject to extreme dynamic loading. Advances in this field are enabling researchers to evaluate new structural components/systems in cost‐effective and efficient ways, under more realistic conditions. For RTHS, performance metric clearly needs to be developed to predict and evaluate the accuracy of various partitioning choices while incorporating the dynamics of the transfer system, and computational/communication delays. In addition, because of the dynamics of the transfer system, communication delays, and computation delays, the RTHS equilibrium force at the interface between numerical and physical substructures is subject to phase discrepancy. Thus, the transfer system dynamics must be accommodated by appropriate actuator controllers. In this paper, a new performance indicator, predictive performance indicator (PPI), is proposed to assess the sensitivity of an RTHS configuration to any phase discrepancy resulting from transfer system dynamics and computational/communication delays. The predictive performance indicator provides a structural engineer with two sets of information as follows: (i) in the absence of a reference response, what is the level of fidelity of the RTHS response? and (ii) if needed, what partitioning adjustments can be made to effectively enhance the fidelity of the response? Moreover, along with the RTHS stability switch criterion, this performance metric may be used as an acceptance criteria for conducting single‐degree‐of‐freedom RTHS. Copyright © 2014 John Wiley & Sons, Ltd.