Geophysical and Geochemical Approach for Seawater Intrusion Assessment in the Godavari Delta Basin, A.P., India

Coastal lands around Bay of Bengal in Central Godavari Delta are mainly agriculture fields and two times annually paddy crops putting in the study area. Canals of Godavari River are the main source of water for irrigation. Geophysical and geochemical investigations were carried out in the study area to decipher subsurface geologic formation and assessing seawater intrusion. Electrical resistivity tomographic surveys carried out in the watershed-indicated low resistivity formation in the upstream area due to the presence of thick marine clays up to thickness of 20–25 m from the surface. Secondly, the lowering of resistivity may be due to the encroachment of seawater in to freshwater zones and infiltration during tidal fluctuation through mainly the Pikaleru drain, and to some extent rarely through Kannvaram and Vasalatippa drains in the downstream area. Groundwater quality analyses were made for major ions revealed brackish nature of groundwater water at shallow depth. The in situ salinity of groundwater is around 5,000 mg/l and there is no groundwater withdrawal for irrigation or drinking purpose in this area except Cairn energy pumping wells which is using for inject brackish water into the oil wells for easy exploration of oil. Chemical analyses of groundwater samples have indicated the range of salt concentrations and correlation of geophysical and borehole litholog data in the study area predicting seawater-contaminated zones and influence of in situ salinity in the upstream of study area. The article suggested further studies and research work that can lead to sustainable exploitation/use and management of groundwater resources in coastal areas

Performance and impacts of managed aquifer recharge interventions for agricultural water security : a framework for evaluation

To minimize and counter decline of groundwater levels and improve the availability of water for crop production, Managed Aquifer Recharge (MAR) interventions are widely adopted across India, often initiated or supported by, local communities, state and central governments to improve the availability of water for irrigation. While the literature on MAR in India is vast, the science of their construction is lacking. Furthermore, there is an absence of a structured approach to evaluate the performance and impact of MAR interventions. Often, performance and impacts of MAR have been commented upon together, without distinguishing the two.In this article, we aim to propose that performance and impact are different from each other, and that the evaluation of MAR interventions should take into account such differences between them. A framework for performance and impact analysis, based on three levels, viz. primary, secondary and tertiary, is outlined. It is then applied to seven selected MAR interventions in India, Adarsha watershed - Andhra Pradesh, Gokulpura-Goverdhanpura watershed - Rajasthan, Kodangipalayam watershed - Tamil Nadu, Chikalgaon watershed - Maharashtra, Rajasamadhiyala watershed - Gujarat, Satlasana watershed - Gujarat and Sujalam Sufalam Yojana - Gujarat. Although, the evaluations of these case studies reported were not categorized into performance and impact, most of them have addressed both. However, none of them could explicitly demonstrate that reported impacts were uniquely related to MAR interventions. If impacts are used as a surrogate for performance, it must be shown that impacts are uniquely linked to MAR interventions

Hydrogeochemical parameters for assessment of groundwater quality in a river sub-basin

Hydrogeochemical studies were carried out in the Ghataprabha River sub-basin to assess the quality and suitability of groundwater for domestic and irrigation purposes. In the present study, an integrated, geophysical and chemical investigation was carried out in the basaltic terrain. Groundwater samples were collected covering the entire major hydrogeological environment for one hydrological cycle. Comparison of the groundwater quality in relation to drinking water quality standards proves that most of the water samples are not suitable for drinking. Chemical indices such as sodium percentage, sodium adsorption ratio and chloroalkaline indices used for evaluating the water quality for irrigation suggest that the majority of the groundwater samples were good for irrigation. Positive values of 74% of groundwater samples indicated the absence of base exchange reaction (chloroalkaline disequilibrium) and negative ratio of 26% samples indicated a base exchange reaction (chloroalkaline equilibrium). Resistivity tomography studies revealed that the high concentration of total dissolved solids, chloride and sodium were due to the local anthropogenic activities and weathering of basalt rocks

Cognitive Network Inference through Bayesian Network Analysis

Cognitive networking deals with applying cognition to the entire network protocol stack for achieving stack-wide as well as network-wide performance goals, unlike cognitive radios that apply cognition only at the physical layer. Designing a cognitive network is challenging since learning the relationship between network protocol parameters in an automated fashion is very complex. We propose to use Bayesian Network (BN) models for creating a representation of the dependence relationships among network protocol parameters. BN is a unique tool for modeling the network protocol stack as it not only learns the probabilistic dependence of network protocol parameters but also provides an opportunity to tune some of the cognitive network parameters to achieve desired performance. To the best of our knowledge, this is the first work to explore the use of BNs for cognitive networks. Creating a BN model for network parameters involves the following steps: sampling the network protocol parameters (Observe), learning the structure of the BN and its parameters from the data (Learn), using a Bayesian Network inference engine (Plan and Decide) to make decisions, and finally effecting the decisions (Act). We have proved the feasibility of achieving a BN-based cognitive network system using the ns-3 simulation platform. From the early results obtained from our cognitive network approach, we provide interesting insights on predicting the network behavior, including the performance of the TCP throughput inference engine based on other observed parameters

Using Bayesian Networks for Cognitive Control of Multi-hop Wireless Networks

Tactical communication networking faces diverse operational scenarios where network optimization is a very challenging task. Learning from the network environment, in order to optimally adapt the network settings, is an essential requirement for providing efficient communication services in such environments. Cognitive networking deals with the application of cognition to the entire protocol stack for achieving network-wide performance goals. One of the key requirements of a cognitive network is to learn the relationships between network protocol parameters spanning the entire stack in relation with the operating network environment. In this paper, we use a probabilistic graphical modeling approach, Bayesian Networks (BNs), in order to create a representation of the dependence relationships between significant parameters spanning transport and medium access control (MAC) layers in multi-hop wireless network environments. We exploit this model to face one of the problems of the TCP protocol, that does not have any mechanism to infer when congestion is about to occur in the network and therefore waits till some packets are lost for reacting to congestion in the network. Such a reactive nature of TCP leads to wastage of precious network resources like bandwidth and power. In this paper we show how to infer in advance the congestion state of the network. We constructed BNs for different network environments by sampling network parameters on-the-fly in the ns-3 simulation platform. We found that it is possible to predict the congestion state of the network with quite good accuracy given sufficient training samples and the current value of the TCP congestion window