scholarly journals Short-Term, Efficient Sampling Strategies for Estimating Forest Moth Diversity Using Light Traps

2011 ◽  
Vol 104 (4) ◽  
pp. 739-748 ◽  
Author(s):  
Kenichi Ozaki ◽  
Katsuhiko Sayama ◽  
Akira Ueda ◽  
Masato Ito ◽  
Ken Tabuchi ◽  
...  
Keyword(s):  
Sampling Strategies ◽  
Light Traps ◽  
Short Term ◽  
Efficient Sampling

scholarly journals Guidelines for depth data collection in rivers when applying interpolation techniques (kriging) for river restoration

2007 ◽  
Vol 4 (3) ◽  
pp. 1069-1094
Author(s):  
M. Rivas-Casado ◽  
S. White ◽  
P. Bellamy
Keyword(s):  
River Restoration ◽  
Sampling Strategy ◽  
Spatial Dimension ◽  
Sampling Strategies ◽  
Depth Data ◽  
Cyclic Patterns ◽  
Efficient Sampling ◽  
Before And After ◽  
Geostatistical Techniques ◽  
River Site

Abstract. River restoration appraisal requires the implementation of monitoring programmes that assess the river site before and after the restoration project. However, little work has yet been developed to design effective and efficient sampling strategies. Three main variables need to be considered when designing monitoring programmes: space, time and scale. The aim of this paper is to describe the methodology applied to analyse the variation of depth in space, scale and time so more comprehensive monitoring programmes can be developed. Geostatistical techniques were applied to study the spatial dimension (sampling strategy and density), spectral analysis was used to study the scale at which depth shows cyclic patterns, whilst descriptive statistics were used to assess the temporal variation. A brief set of guidelines have been summarised in the conclusion.

scholarly journals Optimal sampling in a noisy genetic algorithm for risk-based remediation design

2003 ◽  
Vol 5 (1) ◽  
pp. 11-25 ◽  
Author(s):  
Gayathri Gopalakrishnan ◽  
Barbara S. Minsker ◽  
David E. Goldberg
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo Simulation ◽  
Genetic Algorithms ◽  
Monte Carlo ◽  
Fitness Function ◽  
Sampling Strategies ◽  
Robust Designs ◽  
Highly Efficient ◽  
Efficient Sampling

A groundwater management model has been developed that predicts human health risks and uses a noisy genetic algorithm to identify promising risk-based corrective action (RBCA) designs. Noisy genetic algorithms are simple genetic algorithms that operate in noisy environments. The noisy genetic algorithm uses a type of noisy fitness function (objective function) called the sampling fitness function, which utilises Monte-Carlo-type sampling to find robust designs. Unlike Monte Carlo simulation modelling, however, the noisy genetic algorithm is highly efficient and can identify robust designs with only a few samples per design. For hydroinformatic problems with complex fitness functions, however, it is important that the sampling be as efficient as possible. In this paper, methods for identifying efficient sampling strategies are investigated and their performance evaluated using a case study of a RBCA design problem. Guidelines for setting the parameter values used in these methods are also developed. Applying these guidelines to the case study resulted in highly efficient sampling strategies that found RBCA designs with 98% reliability using as few as 4 samples per design. Moreover, these designs were identified with fewer simulation runs than would likely be required to identify designs using trial-and-error Monte Carlo simulation. These findings show considerable promise for applying these methods to complex hydroinformatic problems where substantial uncertainty exists but extensive sampling cannot feasibly be done.

scholarly journals Bioluminescence Intensity Modeling and Sampling Strategy Optimization*

2005 ◽  
Vol 22 (8) ◽  
pp. 1267-1281 ◽  
Author(s):  
I. Shulman ◽  
J. C. Kindle ◽  
D. J. McGillicuddy ◽  
M. A. Moline ◽  
S. H. D. Haddock ◽  
...  
Keyword(s):  
Close Agreement ◽  
Circulation Model ◽  
Sampling Strategy ◽  
Diffusion Reaction ◽  
Monterey Bay ◽  
Strong Wind ◽  
Sampling Strategies ◽  
Short Term ◽  
Advection Diffusion ◽  
Sensitivity Maps

Abstract The focus of this paper is on the development of methodology for short-term (1–3 days) oceanic bioluminescence (BL) predictions and the optimization of spatial and temporal bioluminescence sampling strategies. The approach is based on predictions of bioluminescence with an advection–diffusion–reaction (tracer) model with velocities and diffusivities from a circulation model. In previous research, it was shown that short-term changes in some of the salient features in coastal bioluminescence can be explained and predicted by using this approach. At the same time, it was demonstrated that optimization of bioluminescence sampling prior to the forecast is critical for successful short-term BL predictions with the tracer model. In the present paper, the adjoint to the tracer model is used to study the sensitivity of the modeled bioluminescence distributions to the sampling strategies for BL. The locations and times of bioluminescence sampling prior to the forecast are determined by using the adjoint-based sensitivity maps. The approach is tested with bioluminescence observations collected during August 2000 and 2003 in the Monterey Bay, California, area. During August 2000, BL surveys were collected during a strong wind relaxation event, while in August 2003, BL surveys were conducted during an extended (longer than a week) upwelling-favorable event. The numerical bioluminescence predictability experiments demonstrated a close agreement between observed and model-predicted short-term spatial and temporal changes of the coastal bioluminescence.

scholarly journals Elemente tehnologice de utilizare a capcanelor cu lumină pentru monitorizarea şi combaterea insectelor dăunătoare

2020 ◽  
Author(s):  
Victor Gorban ◽  
◽  
Vasile Voineac ◽  
Valentina Maievschi ◽  
◽  
...  
Keyword(s):  
Insect Pests ◽  
Plant Protection ◽  
Primary Data ◽  
Light Traps ◽  
Short Term ◽  
Protection Measures ◽  
Plant Pest ◽  
Electro Optic ◽  
Forecasting System ◽  
Low Efficiency

Low efficiency of plant protection means is explained by the lack of a centralized forecasting system for the terms to carry out protective measures, lack of modern methods of obtaining primary data for making up reliable short-term forecasts for the development and spread of pests in agricultural agrocenoses. During last years investigations cowering the elaboration of new systems of integrated plant protection became more active by utilization biorational means of plant protection, and electro optic installations. A great attention is accords to elaboration and selection of sources- attractants and new electro optic structures whice must show a high attractively due to a specific irradiation spectrum and, thus, provide a maximum trapping of harmful insects. Application of the light traps is a more perfect method for phenology investigation of many important, in an economic aspect plant pest, and results of insects gathering can by used for elaboration short-term prognoses of insect pests development for rendering more precise the terms for craning out of protection measures. Further the light traps for insect can be used as an independent mean for plant pest combating. In combination with other methods to combat the use of light traps significantly reduces the number of flying pests, and thus caused them harm.

scholarly journals Design and Analysis of Elimination Surveys for Neglected Tropical Diseases

2020 ◽  
Vol 221 (Supplement_5) ◽  
pp. S554-S560 ◽  
Author(s):  
Claudio Fronterre ◽  
Benjamin Amoah ◽  
Emanuele Giorgi ◽  
Michelle C Stanton ◽  
Peter J Diggle
Keyword(s):  
Neglected Tropical Diseases ◽  
Survey Design ◽  
Decision Makers ◽  
Tropical Diseases ◽  
Sampling Strategies ◽  
Geostatistical Approach ◽  
Predictive Probability ◽  
Efficient Sampling ◽  
Survey Design And Analysis

Abstract As neglected tropical diseases approach elimination status, there is a need to develop efficient sampling strategies for confirmation (or not) that elimination criteria have been met. This is an inherently difficult task because the relative precision of a prevalence estimate deteriorates as prevalence decreases, and classic survey sampling strategies based on random sampling therefore require increasingly large sample sizes. More efficient strategies for survey design and analysis can be obtained by exploiting any spatial correlation in prevalence within a model-based geostatistics framework. This framework can be used for constructing predictive probability maps that can inform in-country decision makers of the likelihood that their elimination target has been met, and where to invest in additional sampling. We evaluated our methodology using a case study of lymphatic filariasis in Ghana, demonstrating that a geostatistical approach outperforms approaches currently used to determine an evaluation unit’s elimination status.

Efficient Sampling Strategies for Forecasting Pharmacokinetic Parameters of Irinotecan (CPT-11)

1995 ◽  
Vol 17 (3) ◽  
pp. 221-229 ◽  
Author(s):  
Hajime Nakashima ◽  
Ronald Lieberman ◽  
Atsuya Karato ◽  
Hitoshi Arioka ◽  
Hironobu Ohmatsu ◽  
...  
Keyword(s):  
Pharmacokinetic Parameters ◽  
Sampling Strategies ◽  
Efficient Sampling

scholarly journals Efficient sampling strategies for relational database operations

1993 ◽  
Vol 116 (1) ◽  
pp. 195-226 ◽  
Author(s):  
Richard J. Lipton ◽  
Jeffrey F. Naughton ◽  
Donovan A. Schneider ◽  
S. Seshadri
Keyword(s):  
Relational Database ◽  
Sampling Strategies ◽  
Efficient Sampling ◽  
Database Operations
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