Causality analysis of groundwater dynamics based on a Vector Autoregressive model in the semi-arid basin of Gundal (South India)

Causal relationships existing between observed levels of groundwater in a semi-arid sub-basin of the Kabini River basin (Karnataka state, India) are investigated in this study. A Vector Auto Regressive model is used for this purpose. Its structure is built on an upstream/downstream interaction network based on observed hydro-physical properties. Exogenous climatic forcing is used as an input based on cumulated rainfall departure. Optimal models are obtained thanks to a trial approach and are used as a proxy of the dynamics to derive causal networks. It appears to be an interesting tool for analysing the causal relationships existing inside the basin. The causal network reveals 3 main regions: the Northeastern part of the Gundal basin is closely coupled to the outlet dynamics. The Northwestern part is mainly controlled by the climatic forcing and only marginally linked to the outlet dynamic. Finally, the upper part of the basin plays as a forcing rather than a coupling with the lower part of the basin allowing for a separate analysis of this local behaviour. The analysis also reveals differential time scales at work inside the basin when comparing upstream oriented with downstream oriented causalities. In the upper part of the basin, time delays are close to 2 months in the upward direction and lower than 1 month in the downward direction. These time scales are likely to be good indicators of the hydraulic response time of the basin which is a parameter usually difficult to estimate practically. This suggests that, at the sub-basin scale, intra-annual time scales would be more relevant scales for analysing or modelling tropical basin dynamics in hard rock (granitic and gneissic) aquifers ubiquitous in south India. (c) 2012 Elsevier B.V. All rights reserved.

Impact of pit-toilet leachate on groundwater chemistry and role of vadose zone in removal of nitrate and E-coli pollutants in Kolar District, Karnataka, India

Assessment of chemistry of groundwater infiltrated by pit-toilet leachate and contaminant removal by vadose zone form the focus of this study. The study area is Mulbagal Town in Karnataka State, India. Groundwater level measurements and estimation of unsaturated permeability indicated that the leachate recharged the groundwater inside the town at the rate of 1 m/day. The average nitrate concentration of groundwater inside the town (148 mg/L) was three times larger than the permissible limit (45 mg/L), while the average nitrate concentration of groundwater outside the town (30 mg/L) was below the permissible limit. The groundwater inside the town exhibited E. coli contamination, while groundwater outside the town was free of pathogen contamination. Infiltration of alkalis (Na+, K+) and strong acids (Cl-, SO4 (2-)) caused the mixed Ca-Mg-Cl type (60 %) and Na-Cl type (28 %) facies to predominate groundwater inside the town, while, Ca-HCO3 (35 %), mixed Ca-Mg-Cl type (35 %) and mixed Ca-Na-HCO3 type (28 %) facies predominated groundwater outside/periphery of town. Reductions in E. coli and nitrate concentrations with vadose zone thickness indicated its participation in contaminant removal. A 4-m thickness of unsaturated sand + soft, disintegrated weathered rock deposit facilitates the removal of 1 log of E. coli pathogen. The anoxic conditions prevailing in the deeper layers of the vadose zone (> 19 m thickness) favor denitrification resulting in lower nitrate concentrations (28-96 mg/L) in deeper water tables (located at depths of -29 to -39 m).

Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks

Artificial Neural Networks (ANNs) have been found to be a robust tool to model many non-linear hydrological processes. The present study aims at evaluating the performance of ANN in simulating and predicting ground water levels in the uplands of a tropical coastal riparian wetland. The study involves comparison of two network architectures, Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN) trained under five algorithms namely Levenberg Marquardt algorithm, Resilient Back propagation algorithm, BFGS Quasi Newton algorithm, Scaled Conjugate Gradient algorithm, and Fletcher Reeves Conjugate Gradient algorithm by simulating the water levels in a well in the study area. The study is analyzed in two cases-one with four inputs to the networks and two with eight inputs to the networks. The two networks-five algorithms in both the cases are compared to determine the best performing combination that could simulate and predict the process satisfactorily. Ad Hoc (Trial and Error) method is followed in optimizing network structure in all cases. On the whole, it is noticed from the results that the Artificial Neural Networks have simulated and predicted the water levels in the well with fair accuracy. This is evident from low values of Normalized Root Mean Square Error and Relative Root Mean Square Error and high values of Nash-Sutcliffe Efficiency Index and Correlation Coefficient (which are taken as the performance measures to calibrate the networks) calculated after the analysis. On comparison of ground water levels predicted with those at the observation well, FFNN trained with Fletcher Reeves Conjugate Gradient algorithm taken four inputs has outperformed all other combinations.

An Optimal Fuzzy Logic Controller tuned with Artificial Immune System

In this paper, a method for the tuning the membership functions of a Mamdani type Fuzzy Logic Controller (FLC) using the Clonal Selection Algorithm(CSA) a model of the Artificial Immune System(AIS) paradigm is examined. FLC's are designed for two problems, firstly the linear cart centering problem and secondly the highly nonlinear inverted pendulum problem. The FLC tuned by AIS is compared with FLC tuned by GA. In order to check the robustness of the designed PLC's white noise was added to the system, further, the masses of the cart and the length and mass of the pendulum are changed. The PLC's were also tested in the presence of faulty rules. Finally, Kruskal Wallis test was performed to compare the performance of the GA and AIS. An insight into the algorithms are also given by studying the effect of the important parameters of GA and AIS.

Hierarchical artificial immune system for crop stage classification

This paper presents a new hierarchical clustering algorithm for crop stage classification using hyperspectral satellite image. Amongst the multiple benefits and uses of remote sensing, one of the important application is to solve the problem of crop stage classification. Modern commercial imaging satellites, owing to their large volume of satellite imagery, offer greater opportunities for automated image analysis. Hence, we propose a unsupervised algorithm namely Hierarchical Artificial Immune System (HAIS) of two steps: splitting the cluster centers and merging them. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The classification results have been compared with K-means and Artificial Immune System algorithms. From the results obtained, we conclude that the proposed hierarchical clustering algorithm is accurate.

Influence of anthropogenic contamination on groundwater chemistry in Mulbagal town, Kolar District, India

Geochemical studies are performed to examine the impact of leachate infiltration from on-site sewage disposal systems on the groundwater chemistry in Mulbagal town, Kolar District, Karnataka State, India. The leachate infiltration imposed nitrate concentrations ranging from 4 mg/L to 388 mg/L in the groundwater samples; it was observed that 79% of the samples exhibited nitrate concentrations in excess of drinking water permissible limit (45 mg/L). The average (of 43 measurements) E. coli levels in the groundwater samples corresponded to 189 MPN/100 mL and 55% of the samples tested exhibit pathogen contamination. Results also showed that the groundwater in the study area is characterized by acidic pH, large calcium + magnesium ion and Na/Cl ratios of < unity causing majority of the ground water samples to classify as Ca-Mg-Cl type and Na-Cl type. Saturation index (SI) computation using Visual MINTEQ program showed that the groundwater samples are under-saturated with respect to calcite. The theoretical SI values (of calcite) however suggested that the groundwater samples ought to be over-saturated with calcite. Under-saturation of the groundwater samples with calcite is attributed to increased dissolution of the mineral in the acidic environment of the groundwater.

Bearing Capacity of Foundations with Inclined Groundwater Seepage

The ultimate bearing capacity of strip foundations in the presence of inclined groundwater flow, considering both upward and downward flow directions, has been determined by using the lower bound finite-element limit analysis. A numerical solution has been generated for both smooth and rough footings placed on frictional soils. A correction factor (f gamma), which needs to be multiplied with the N gamma-term, has been computed to account for groundwater seepage. The variation of f gamma has been obtained as a function of the hydraulic gradient (i) for various inclinations of groundwater flow. For a given magnitude of i, there exists a certain critical inclination of the flow for which the value of f gamma is minimized. With an upward flow, for all flow inclinations, the magnitude of f gamma always reduces with an increase in the value of i. An example has also been provided to illustrate the application of the obtained results when designing foundations in the presence of groundwater seepage.

Constraining complex aquifer geometry with geophysics (2-D ERT and MRS measurements) for stochastic modelling of groundwater flow

Stochastic modelling is a useful way of simulating complex hard-rock aquifers as hydrological properties (permeability, porosity etc.) can be described using random variables with known statistics. However, very few studies have assessed the influence of topological uncertainty (i.e. the variability of thickness of conductive zones in the aquifer), probably because it is not easy to retrieve accurate statistics of the aquifer geometry, especially in hard rock context. In this paper, we assessed the potential of using geophysical surveys to describe the geometry of a hard rock-aquifer in a stochastic modelling framework. The study site was a small experimental watershed in South India, where the aquifer consisted of a clayey to loamy-sandy zone (regolith) underlain by a conductive fissured rock layer (protolith) and the unweathered gneiss (bedrock) at the bottom. The spatial variability of the thickness of the regolith and fissured layers was estimated by electrical resistivity tomography (ERT) profiles, which were performed along a few cross sections in the watershed. For stochastic analysis using Monte Carlo simulation, the generated random layer thickness was made conditional to the available data from the geophysics. In order to simulate steady state flow in the irregular domain with variable geometry, we used an isoparametric finite element method to discretize the flow equation over an unstructured grid with irregular hexahedral elements. The results indicated that the spatial variability of the layer thickness had a significant effect on reducing the simulated effective steady seepage flux and that using the conditional simulations reduced the uncertainty of the simulated seepage flux. As a conclusion, combining information on the aquifer geometry obtained from geophysical surveys with stochastic modelling is a promising methodology to improve the simulation of groundwater flow in complex hard-rock aquifers. (C) 2013 Elsevier B.V. All rights reserved.

Modelling the impact of extensive irrigation on the groundwater resources

Drastic groundwater resource depletion due to excessive extraction for irrigation is a major concern in many parts of India. In this study, an attempt was made to simulate the groundwater scenario of the catchment using ArcSWAT. Due to the restriction on the maximum initial storage, the deep aquifer component in ArcSWAT was found to be insufficient to represent the excessive groundwater depletion scenario. Hence, a separate water balance model was used for simulating the deep aquifer water table. This approach is demonstrated through a case study for the Malaprabha catchment in India. Multi-site rainfall data was used to represent the spatial variation in the catchment climatology. Model parameters were calibrated using observed monthly stream flow data. Groundwater table simulation was validated using the qualitative information available from the field. The stream flow was found to be well simulated in the model. The simulated groundwater table fluctuation is also matching reasonably well with the field observations. From the model simulations, deep aquifer water table fluctuation was found very severe in the semi-arid lower parts of the catchment, with some areas showing around 60m depletion over a period of eight years. Copyright (c) 2012 John Wiley & Sons, Ltd.

Filling-in Void and Sparse Regions in Protein Sequence Space by Protein-Like Artificial Sequences Enables Remarkable Enhancement in Remote Homology Detection Capability

Protein functional annotation relies on the identification of accurate relationships, sequence divergence being a key factor. This is especially evident when distant protein relationships are demonstrated only with three-dimensional structures. To address this challenge, we describe a computational approach to purposefully bridge gaps between related protein families through directed design of protein-like ``linker'' sequences. For this, we represented SCOP domain families, integrated with sequence homologues, as multiple profiles and performed HMM-HMM alignments between related domain families. Where convincing alignments were achieved, we applied a roulette wheel-based method to design 3,611,010 protein-like sequences corresponding to 374 SCOP folds. To analyze their ability to link proteins in homology searches, we used 3024 queries to search two databases, one containing only natural sequences and another one additionally containing designed sequences. Our results showed that augmented database searches showed up to 30% improvement in fold coverage for over 74% of the folds, with 52 folds achieving all theoretically possible connections. Although sequences could not be designed between some families, the availability of designed sequences between other families within the fold established the sequence continuum to demonstrate 373 difficult relationships. Ultimately, as a practical and realistic extension, we demonstrate that such protein-like sequences can be ``plugged-into'' routine and generic sequence database searches to empower not only remote homology detection but also fold recognition. Our richly statistically supported findings show that complementary searches in both databases will increase the effectiveness of sequence-based searches in recognizing all homologues sharing a common fold. (C) 2013 Elsevier Ltd. All rights reserved.

Bearing capacity of foundations subjected to groundwater flow

The ultimate bearing capacity of strip foundations subjected to horizontal groundwater flow has been computed by making use of the stress characteristics method which is well known for its capability in solving quite accurately different stability problems in geotechnical engineering. The numerical solution has been generated both for smooth and rough footings placed on frictional soils. A correction factor (fγ) associated with Nγ term, to account for the existence of ground water flow, has been introduced. The variation of fγ has been obtained as a function of hydraulic gradient (i) for different values of soil frictional angle. The magnitude of fγ reduces continuously with an increase in the value of i.

Artificial photosynthesis and the splitting of water to generate hydrogen

It is no exaggeration to state that the energy crisis is the most serious challenge that we face today. Among the strategies to gain access to reliable, renewable energy, the use of solar energy has clearly emerged as the most viable option. A promising direction in this context is artificial photosynthesis. In this article, we briefly describe the essential features of artificial photosynthesis in comparison with natural photosynthesis and point out the modest success that we have had in splitting water to produce oxygen and hydrogen, specially the latter.

Influence of anthropogenic contamination on fluoride concentration in groundwater: A study of Mulbagal town, Kolar district, Karnataka

Groundwater contamination is a serious concern in India. Major geogenic contaminants include fluoride, arsenic and iron, while common anthropogenic contaminants include nitrate, metals, organics and microbial contamination. Besides, known point and diffuse sources, groundwater c ontamination from inf iltration of pit to ilet leachate is an emerging concern. The study area of this paper is Kolar district in Karnataka that is hot spot of fluoride contamination. The absence of fluoride contamination in Mulbagal town and the alterations in groundwater chemistry from infiltration of pit toilet leachate motivated the author to examine the possible linkages between anthropogenic contamination and fluoride concentration in groundwater of Mulbagal town. Analysis of the groundwater chemistry revealed that the groundwater in Mulbagal town is under saturated with respect to calcite that suppresses the dissolution of fluorite and the fluoride concentration in the groundwater. The slightly acidic pH of the groundwater is considered responsible to facilitate calcite dissolution under saturation.

Effect of Groundwater Seepage on Uplift Resistance of Buried Pipelines

The uplift resistance of pipelines buried in sands, in the presence of inclined groundwater flow, considering both upward and downward flow directions, has been determined by using the lower bound finite elements limit analysis in conjunction with nonlinear optimization. A correction factor (f (gamma) ), which needs to be multiplied with the uplift factor (F (gamma) ), has been computed to account for groundwater seepage. The variation of f (gamma) has been obtained as a function of i(gamma (w) /gamma (sub) ) for different horizontal inclinations (theta) of groundwater flow; where i = absolute magnitude of hydraulic gradient along the direction of flow, gamma (w) is the unit weight of water and gamma (sub) is the submerged unit weight of soil mass. For a given magnitude of i, there exists a certain critical value of theta for which the magnitude of f (gamma) becomes the minimum. An example has also been presented to illustrate the application of the results obtained for designing pipelines in presence of groundwater seepage.

Experimental Characterization and Control of Miniaturized Pneumatic Artificial Muscle

Robotic surgical tools used in minimally invasive surgeries (MIS) require miniaturized and reliable actuators for precise positioning and control of the end-effector. Miniature pneumatic artificial muscles (MPAMs) are a good choice due to their inert nature, high force to weight ratio, and fast actuation. In this paper, we present the development of miniaturized braided pneumatic muscles with an outer diameter of similar to 1.2 mm, a high contraction ratio of about 18%, and capable of providing a pull force in excess of 4 N at a supply pressure of 0.8 MPa. We present the details of the developed experimental setup, experimental data on contraction and force as a function of applied pressure, and characterization of the MPAM. We also present a simple kinematics and experimental data based model of the braided pneumatic muscle and show that the model predicts contraction in length to within 20% of the measured value. Finally, a robust controller for the MPAMs is developed and validated with experiments and it is shown that the MPAMs have a time constant of similar to 10 ms thereby making them suitable for actuating endoscopic and robotic surgical tools.