Non-linear optimization for parameter estimation for flood forecasting

Floods are the response of a catchment area to severe rainfall events. Each catchment will have its unique response which is dependent on its own characteristics and the temporal and spatial distribution of the oncoming rainfall event. A non linear optimization technique has been applied to historical data for rainfall and river flows of the Kakanui catchment in North Otago, New Zealand, to estimate the parameters of a model based on the transfer function concept. The non linear optimization is based on Powell algorithm. Powell algorithm has been widely used in the literature, and it is more efficient and faster than the Simplex method (Press et al., 1989) Observed rainfall events at two locations in the Kakanui catchment, along with the corresponding observed flows of the river have been utilized to estimate the transfer function which represents the response of the Kakanui catchment to rainfall events. An adjusted form of Philip’s equation for infiltration was used to estimate the abstraction of the rainfall event and obtain the effective rainfall which will contribute to the river flow. Weighing factors were assigned to each of the rainfall sites to obtain the best fit between observed and forecasted flows. Nine flood events were used for the calibration process, while two events were utilized for the validation of the derived model. The model has 19 parameters for the transfer function, 2 parameters for the hydrologic abstractions model, and 2 parameters for the weighing factors of the rainfall sites. This results in a total of 23 parameters for the developed model. The ratio of observed cumulative rainfall at Clifton Falls to the corresponding rainfall at the Dasher for historical events is not consistent, and varies significantly from one event to another. This indicates the high variability of the spatial distribution of rainfall events over the Kakanui catchment. As these rainfall events were used in the model calibration, it was difficult to obtain the correct transfer function without proper accounting for the spatial distribution of rainfall over the whole watershed. However, the model, in general, performed satisfactory, given the difficulty in representing the spatial variability of the rainfall events. The model was capable of simulating the flood hydrographs of several events which were incorporated in its calibration, but did not perform well with others. The model was able to simulate well the flows of a flood event which was not included in its calibration. Moreover, in applying the derived model for a real case event which occurred most recently on 30 July 2007, the model was able to forecast very closely the peak flow, but the whole flow hydrograph was not forecasted as good

International center for monitoring cloud computing providers (ICMCCP) for ensuring trusted clouds

Cloud computing offers flexible and scalable IT (Information Technology) services for which many organizations are now interested in harnessing its benefit. In spite of the enthusiasm and great interest, Cloud computing has not yet earned full trust of the individual customers, banks, armed forces, governments, and companies who do sensitive computing tasks. While it is not the goal to persuade everyone to use the technology and some types of tasks are to be done with secrecy, for the general works done by the companies and customers, Cloud could be heavily used. However, it is very difficult to convince people that the Cloud Providers (CPs) would keep their data protected. To address this issue, what required is to employ some mechanism that can establish the trust of the users. With this motivation, in this paper, we present a novel concept for trust assurance in Cloud services with the proposal of implementing a Global Central Bank-like regulatory authority. Our initiative is termed International Center for Monitoring Cloud Computing Providers (ICMCCP). Various facets of ICMCCP model have been described alongside the policy making issues. Necessary backgrounds of Cloud are also presented

Detecting Zero-day Polymorphic Worms with Jaccard Similarity Algorithm

Zero-day polymorphic worms pose a serious threat to the security of Mobile systems and Internet infrastructure. In many cases, it is difficult to detect worm attacks at an early stage. There is typically little or no time to develop a well-constructed solution during such a worm outbreak. This is because the worms act only to spread from node to node and they bring security concerns to everyone using Internet via any static or mobile node. No system is safe from an aggressive worm crisis. However, many of the characteristics of a worm can be used to defeat it, including its predictable behavior and shared signatures. In this paper, we propose an efficient signature generation method based on string similarity algorithms to generate signatures for Zero-day polymorphic worms. Then, these signatures are practically applied to an Intrusion Detection System (IDS) to prevent the network from such attacks. The experimental results show the efficiency of the proposed approach compared to other existing mechanisms

The efficiency of natural plant extracts in improving storage stability, antioxidant activity, sensory evaluation, and physicochemical properties of date juice-based energy drink

Energy drinks (EDs) are a type of sweetened and non-alcoholic beverage with high content of caffeine. EDs were prepared from date juice enriched with different concentrations of ginger rhizomes extract, moringa leaf extract, and caffeine. Physicochemical properties (pH, TSS, and color parameters), total phenolic content (TPC), antioxidant activity, microbiological quality, and sensory characteristics were evaluated for freshly prepared EDs and during storage. The results showed that freshly prepared EDs from date juice and natural extracts revealed lower pH, higher TSS, and lower color parameters than the control. The inclusion of date juice and natural extracts improved the TPC, antioxidant activity, and microbiological quality of freshly prepared EDs. Storage studies exhibited that EDs containing date juice and natural extracts were more stable during storage in terms of all quality parameters estimated. However, the ED made from date juice, ginger extract, moringa extract, and caffeine was the best in terms of all parameters evaluated with higher stability during 3 months of storage at room temperature. In general, by date juice and natural plant extracts addition, a desirable ED can be prepared

efam: an expanded, metaproteome-supported HMM profile database of viral protein families

Motivation: Viruses infect, reprogram and kill microbes, leading to profound ecosystem consequences, from elemental cycling in oceans and soils to microbiome-modulated diseases in plants and animals. Although metagenomic datasets are increasingly available, identifying viruses in them is challenging due to poor representation and annotation of viral sequences in databases. Results: Here, we establish efam, an expanded collection of Hidden Markov Model (HMM) profiles that represent viral protein families conservatively identified from the Global Ocean Virome 2.0 dataset. This resulted in 240 311 HMM profiles, each with at least 2 protein sequences, making efam >7-fold larger than the next largest, panecosystem viral HMM profile database. Adjusting the criteria for viral contig confidence from 'conservative' to 'eXtremely Conservative' resulted in 37 841 HMM profiles in our efam-XC database. To assess the value of this resource, we integrated efam-XC into VirSorter viral discovery software to discover viruses from less-studied, ecologically distinct oxygen minimum zone (OMZ) marine habitats. This expanded database led to an increase in viruses recovered from every tested OMZ virome by similar to 24% on average (up to similar to 42%) and especially improved the recovery of often-missed shorter contigs (<5 kb). Additionally, to help elucidate lesser-known viral protein functions, we annotated the profiles using multiple databases from the DRAM pipeline and virion-associated metaproteomic data, which doubled the number of annotations obtainable by standard, single-database annotation approaches. Together, these marine resources (efam and efam-XC) are provided as searchable, compressed HMM databases that will be updated bi-annually to help maximize viral sequence discovery and study from any ecosystem

Automated signature generation for Zero-day polymorphic worms using a Double-honeynet

Includes bibliographical references.This thesis proposes an accurate system for signature generation for Zero-day polymorphic worms. Thesis consists of two parts: In part one, polymorphic worm instances are collected by designing a novel Double-honeynet system, which is able to detect new worms that have not been seen before. Unlimited honeynet outbound connections are introduced to collect all polymorphic worm instances. Therefore this system produces accurate worm signatures. In part two, signatures are generated for the polymorphic worms that are collected by the Double-honeynet system. Both a Modified Knuth-Morris-Pratt (MKMP) Algorithm, which is string matching based, and a Modified Principal Component Analysis (MPCA), which is statistics based, are used

Simulated annealing for calibrating the Manning’s roughness coefficients for general channel networks on a basin scale

The practical application of simultaneous solutions to the problem of steady state gradually varied flow in a general channel network depends significantly on the reliability of the estimated Manning roughness coefficients based on the calibration of the flow models against observed data. Manning roughness coefficients are needed for all the cross sections of the channel network. Systematic approaches for the calibration of Manning roughness coefficients for such a flow model are very sparse in the literature. This study proposes simulated annealing as an optimizer to the problem of calibrating Manning’s roughness coefficients for a steady state varied flow in a general channel network and presents its application to a case study in Quangnam basin of Vietnam

Flood frequency analysis for a braided river catchment in New Zealand: Comparing annual maximum and partial duration series with varying record lengths

This study addresses technical questions concerning the use of the partial duration series (PDS) within the domain of flood frequency analysis. The recurring questions which often prevent the standardised use of the PDS are peak independence and threshold selection. This paper explores standardised approaches to peak and threshold selection to produce PDS samples with differing average annual exceedances, using six theoretical probability distributions. The availability of historical annual maximum (AMS) data (1930–1966) in addition to systemic AMS data (1967–2015) enables a unique comparison between the performance of the PDS sample and the systemic AMS sample. A recently derived formula for the translation of the PDS into the annual domain, simplifying the use of the PDS, is utilised in an applied case study for the first time. Overall, the study shows that PDS sampling returns flood magnitudes similar to those produced by AMS series utilising historical data and thus the use of the PDS should be preferred in cases where historical flood data is unavailable.Financial support was provided by Lincoln University for the completion of the thesis and subsequent manuscripts