Design of Barrages with Genetic Algorithm Based Embedded Simulation Optimization Approach

Barrages are hydraulic structures constructed across rivers to divert flow into irrigation canals or power generation channels. The most of these structures are founded on permeable foundation. The optimum cost of these structures is nonlinear function of factors that cause the seepage forces under the structure. There is, however, no procedure to ascertain the basic barrage parameters such as depth of sheet piles or cutoffs and the length and thickness of floor in a cost–effective manner. In this paper, a nonlinear optimization formulation (NLOF), which consists of an objective function of minimizing total cost, is solved using genetic algorithm (GA). The mathematical model that represents the subsurface flow is embedded in the NLOF. The applicability of the approach has been illustrated with a typical example of barrage profile. The results obtained in this study shows drastic cost savings when the proposed NLOF is solved using GA than that of using classical optimization technique and conventional method. A parametric analysis has also been performed to study the effect of varying soil and hydrological conditions on design parameters and on over all cost.     

Relationships Between Satellite Observed Lit Area and Water Footprints

Global water resources are vulnerable to depletion due to the increasing demand of an ever-increasing human population. A country’s water footprint is a measure of the total volume of water needed to produce the goods and services consumed by the country, including water originating beyond its own borders. The water footprint can be a critical indicator of global water resource use, but its practical application is hindered by a lack of comparable data across national boundaries. The purpose of this article is to test the applicability of the nighttime imagery products produced by the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) for the assessment of the global water footprint. To accomplish this purpose, the average areal extent of nighttime lighting (lit area) is calculated from 1997 to 2001. Next, lit area is regressed on the total water footprint for each country, as indicated by the Water Footprint Network (WFN), to estimate that country’s total water footprint using nighttime imagery. Model residuals are analyzed at the national scale to understand the appropriateness of nighttime imagery for assessing water consumption. Results indicate strong positive correlations between lit area and total water footprint (TWF), domestic water withdrawal (DWW), and industrial water consumption (IWC) at the national scale. Overall, the analyses reveal that the rate of agricultural water consumption to total water footprint (AWCR) and population density can affect the precision of estimates when lit area is selected as a proxy to estimate water footprints.     

Streamflow Simulation by SWAT Using Different Precipitation Sources in Large Arid Basins with Scarce Raingauges

Streamflow from the mountains is the main source of water for the lower plain in arid regions. Accurate simulation of streamflow is of great importance to the arid ecosystem. However, many large arid drainage basins in northwestern China have low density of precipitation stations, which makes the streamflow modeling and prediction very difficult. Based on raingauge data and Tropical Rainfall Measuring Mission (TRMM) data combined with raingauge data, different approaches were explored for spatializing precipitation in large area with scarce raingauges. Spatialized precipitation was then input into Soil and Water Assessment Tool (SWAT), a semi-distributed hydrological model, to simulate streamflow. Results from a case study in the Manas river basin showed that simulated hydrographs using both the approaches are able to reproduce the watershed hydrological behavior. Moreover, statistical assessment indicated that hydrological model driven by the spatialized precipitation based on radar combined with raingauge data performed better than that based on gauge data. Radar precipitation estimator can provide a practical data source for hydrological modeling at a basin scale where the raingauge network is sparse.     

Surveillance of Drinking Water Quality for Safe Water Supply—A Case Study from Shillong,India

To ascertain the quality of drinking water being supplied and maintained, it is necessary to conduct water quality surveillance for evolving suitable strategy for future planning. In the present investigation, water quality was monitored in treatment plants, service reservoirs, and at consumer ends in three seasons to assess the baseline water quality status at Shillong in Meghalaya. There are three water treatment plants at Shillong namely Umkhen, Mawlai and GSWS with design capacities of 1.5, 3.4 and 34 Million liter per day (MLD) respectively. Each treatment plant is having rapid sand filtration followed by disinfection. The study reveals that the physico-chemical parameters of water quality at consumer end meets Indian drinking water quality standards (BIS 1991) after conventional treatment followed by disinfection, whereas the bacteriological parameters for raw water sources exceed the permissible limit indicating the treatment need for drinking purposes. Throughout year the average feacal coliform contamination at service reservoir and to consumer end were found as 44 to 156 CFU/100 ml which may be attributed to the general management practices for maintenance of service reservoirs and the possibility of en route contamination.     

Effects of Bed Load Movement on Mean Flow Characteristics in Mobile Gravel Beds

The effects of bed-load movement on mean flow characteristics were experimentally investigated using particle image velocimetry (PIV). The gravel beds associated with open-channel flow are hydraulically rough, and two types of grain with different median diameters were used. The flow field and bed load motion images were simultaneously obtained via the particular experimental setup. The mass of sediment particles instantaneously moving on the bed, i.e., the sand-wave, was observed in the experiment. It was found that the sand-waves have an average friction angle of 11.2° and heights ranging from 2.2 d ₅₀ to 3.8 d ₅₀. The mean velocity profiles on the mobile bed can also be represented with logarithmic distribution with deviations occurred in the near wall region. Based on the Schultz–Grunow formula, a modified equation is developed in this study to better express the flow resistance factor in terms of the Reynolds number and the ratio of flow depth to roughness height.     

Drought Severity Assessment Based on Bivariate Probability Analysis

Conventionally drought severity is assessed based on drought indices. Recently the Reconnaissance Drought Index (RDI) was proposed to assess drought severity based on the precipitation to potential evapotranspiration ratio (P/PET). In this paper RDI is studied as a bivariate index under a set of assumptions and simplifications. The paper presents a simple computational procedure for estimating the P/PET ratio for selected reference periods varying from 3 to 12 months, for any return period of drought. Alternatively, based on this procedure, the severity of any drought episode is rationally assessed. A bivariate probability analysis is employed based on the assumption that P and PET values are normally distributed and often negatively correlated. Examples for the application of the proposed procedure are presented using data from several meteorological stations in Greece. It is shown that the assumption of normality of both P and PET holds for long periods at all examined stations.     

Optimally Adaptive Integration of Univariate Lipschitz Functions

We consider the problem of approximately integrating a Lipschitz function f (with a known Lipschitz constant) over an interval. The goal is to achieve an additive error of at most ε using as few samples of f as possible. We use the adaptive framework: on all problem instances an adaptive algorithm should perform almost as well as the best possible algorithm tuned for the particular problem instance. We distinguish between and , the performances of the best possible deterministic and randomized algorithms, respectively. We give a deterministic algorithm that uses samples and show that an asymptotically better algorithm is impossible. However, any deterministic algorithm requires samples on some problem instance. By combining a deterministic adaptive algorithm and Monte Carlo sampling with variance reduction, we give an algorithm that uses at most samples. We also show that any algorithm requires samples in expectation on some problem instance (f,ε), which proves that our algorithm is optimal.     

Path Hitting in Acyclic Graphs

An instance of the path hitting problem consists of two families of paths, and ℋ, in a common undirected graph, where each path in ℋ is associated with a non-negative cost. We refer to and ℋ as the sets of demand and hitting paths, respectively. When p∈ℋ and share at least one mutual edge, we say that p hits q. The objective is to find a minimum cost subset of ℋ whose members collectively hit those of . In this paper we provide constant factor approximation algorithms for path hitting, confined to instances in which the underlying graph is a tree, a spider, or a star. Although such restricted settings may appear to be very simple, we demonstrate that they still capture some of the most basic covering problems in graphs. Our approach combines several novel ideas: We extend the algorithm of Garg, Vazirani and Yannakakis (Algorithmica, 18:3–20, 1997) for approximate multicuts and multicommodity flows in trees to prove new integrality properties; we present a reduction that involves multiple calls to this extended algorithm; and we introduce a polynomial-time solvable variant of the edge cover problem, which may be of independent interest. An extended abstract of this paper appeared in Proceedings of the 14th Annual European Symposium on Algorithms, 2006. This work is part of D. Segev’s Ph.D. thesis prepared at Tel-Aviv University under the supervision of Prof. Refael Hassin.     

Measuring ranked list robustness for query performance prediction

We introduce the notion of ranking robustness, which refers to a property of a ranked list of documents that indicates how stable the ranking is in the presence of uncertainty in the ranked documents. We propose a statistical measure called the robustness score to quantify this notion. Our initial motivation for measuring ranking robustness is to predict topic difficulty for content-based queries in the ad-hoc retrieval task. Our results demonstrate that the robustness score is positively and consistently correlation with average precision of content-based queries across a variety of TREC test collections. Though our focus is on prediction under the ad-hoc retrieval task, we observe an interesting negative correlation with query performance when our technique is applied to named-page finding queries, which are a fundamentally different kind of queries. A side effect of this different behavior of the robustness score between the two types of queries is that the robustness score is also found to be a good feature for query classification.      

A scalable algorithm for mining maximal frequent sequences using a sample

In this paper, we propose an efficient scalable algorithm for mining Maximal Sequential Patterns using Sampling (MSPS). The MSPS algorithm reduces much more search space than other algorithms because both the subsequence infrequency-based pruning and the supersequence frequency-based pruning are applied. In MSPS, a sampling technique is used to identify long frequent sequences earlier, instead of enumerating all their subsequences. We propose how to adjust the user-specified minimum support level for mining a sample of the database to achieve better overall performance. This method makes sampling more efficient when the minimum support is small. A signature-based method and a hash-based method are developed for the subsequence infrequency-based pruning when the seed set of frequent sequences for the candidate generation is too big to be loaded into memory. A prefix tree structure is developed to count the candidate sequences of different sizes during the database scanning, and it also facilitates the customer sequence trimming. Our experiments showed MSPS has very good performance and better scalability than other algorithms. Congnan Luo received the B.E. degree in Computer Science from Tsinghua University, Beijing, P.R. China, in 1997, the M.S. degree in Computer Science from the Institute of Software, Chinese Academy of Sciences, Beijing, P.R. China, in 2000, and the Ph.D. degree in Computer Science and Engineering from Wright State University, Dayton, OH, in 2006. Currently he is a technical staff at the Teradata division of NCR in San Diego, CA, and his research interests include data mining, machine learning, and databases. Soon M. Chung received the B.S. degree in Electronic Engineering from Seoul National University, Korea, in 1979, the M.S. degree in Electrical Engineering from Korea Advanced Institute of Science and Technology, Korea, in 1981, and the Ph.D. degree in Computer Engineering from Syracuse University, Syracuse, New York, in 1990. He is currently a Professor in the Department of Computer Science and Engineering at Wright State University, Dayton, OH. His research interests include database, data mining, Grid computing, text mining, XML, and parallel and distributed processing.