scholarly journals Ant Colony Optimization for Data Acquisition Mission Planning

2014 ◽  
Vol 5 (2) ◽  
pp. 3-11 ◽  
Author(s):  
Giancarlo Colmenares ◽  
Fadi Halal ◽  
Marek B. Zaremba
Keyword(s):  
Data Acquisition ◽  
Ant Colony Optimization ◽  
Monitoring System ◽  
Water Quality Monitoring ◽  
Current Data ◽  
Ant Colony ◽  
Continuous Space ◽  
In Situ Data ◽  
Water Pollutant

Abstract The probabilistic Ant Colony Optimization (ACO) approach is presented to solve the problem of designing an optimal trajectory for a mobile data acquisition platform. An ACO algorithm optimizes an objective function defined in terms of the value of the acquired data samples subject to different sets of constraints depending on the current data acquisition strategy. The analysis presented in this paper focuses on an environment monitoring system, which acquires in-situ data for precise calibration of a water quality monitoring system. The value of the sample is determined based on the concentration of the water pollutant, which in turn is obtained through processing of multi-spectral satellite imagery. Since our problem is defined in a continuous space of coordinates, and in some strategies each point is able to connect to any other point in the space, we adopted a hybrid model that involves a connection graph and also a spatial grid.

Particle Swarm Optimization Combined with Ant Colony Optimization for the Multiple Traveling Salesman Problem

2009 ◽  
Vol 626-627 ◽  
pp. 717-722 ◽  
Author(s):  
Hong Kui Feng ◽  
Jin Song Bao ◽  
Jin Ye
Keyword(s):  
Particle Swarm Optimization ◽  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Particle Swarm ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Great Success ◽  
Swarm Optimization ◽  
Continuous Space ◽  
Multiple Traveling Salesman Problem

A lot of practical problem, such as the scheduling of jobs on multiple parallel production lines and the scheduling of multiple vehicles transporting goods in logistics, can be modeled as the multiple traveling salesman problem (MTSP). Due to the combinatorial complexity of the MTSP, it is necessary to use heuristics to solve the problem, and a discrete particle swarm optimization (DPSO) algorithm is employed in this paper. Particle swarm optimization (PSO) in the continuous space has obtained great success in resolving some minimization problems. But when applying PSO for the MTSP, a difficulty rises, which is to find a suitable mapping between sequence and continuous position of particles in particle swarm optimization. For overcoming this difficulty, PSO is combined with ant colony optimization (ACO), and the mapping between sequence and continuous position of particles is established. To verify the efficiency of the DPSO algorithm, it is used to solve the MTSP and its performance is compared with the ACO and some traditional DPSO algorithms. The computational results show that the proposed DPSO algorithm is efficient.

Synergistic Use of Satellite and In-Situ Current Data to Improve the Characterisation of Seasonal Trends

2009 ◽  
Author(s):  
Liam Harrington-Missin ◽  
Mark Calverley ◽  
Gus Jeans
Keyword(s):  
Current Data ◽  
Seasonal Trend ◽  
Measured Signal ◽  
Geostrophic Current ◽  
Geostrophic Currents ◽  
Current Regime ◽  
Long Period ◽  
In Situ Data ◽  
One Year

The synergistic use of measured in-situ current data and altimetry derived geostrophic current data provides improved seasonal characterisation of the current regime, West of Shetland. In September 2007, considerable downtime was experienced by an offshore operator, West of Shetland, as a result of unexpectedly high currents persisting for a number of days. This downtime was unanticipated following conclusions derived from one year of in-situ measured data, which suggested a most favourable current regime during the months August to October. Ten years of altimetry derived geostrophic currents were utilised in conjunction with approximately 3 years of in-situ data to assess the validity of the reported seasonal trend. The altimetry derived geostrophic currents correlated well with the dominating long period signal extracted from the in-situ data. Seasonal comparison between the altimetry derived geostrophic currents and the total measured signal showed the previously available measurement year had a relatively benign September. Based on the 10 years of satellite data, the inter-annual variability of the current regime West of Shetland does not show any clear seasonal trend.

scholarly journals Trophic status assessment of small turbid lakes comparing remote sensing and in situ data: case study at lower Danube floodplain

2020 ◽  
Vol 32 ◽  
pp. 53-63
Author(s):  
Stefan Kazakov ◽  
Valko Biserkov ◽  
Luchezar Pehlivanov ◽  
Stoyan Nedkov
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Satellite Data ◽  
Suspended Matter ◽  
Trophic Status ◽  
Remote Sensing Data ◽  
Water Quality Monitoring ◽  
Floodplain Lakes ◽  
In Situ Data

The aim of the study was to compare in situ and remote sensing data, in order to assess the applicability of satellite images in water quality monitoring of floodplain lakes. Two indicators of trophic status were compared: chlorophyll a and total suspended matter. Two lakes on Lower Danube floodplain were selected: Srebarna and Malak Preslavets. Data were obtained in July and August 2018. Sentinel 2 MSI L1c images were analyzed in SeNtinel Application Platform (SNAP), (v. 6.0). According to in situ data, Srebarna Lake indicated status of eutrophication, while Malak Preslavets experienced hypertrophic conditions. Satellite data indicated eutrophic conditions for both lakes. Comparing the results from in situ and satellite data, chlorophyll a showed higher correlation (r = 0.66) and comparable results. On the other hand, significantly overestimation of suspended matter according to satellite data were found, as well weaker correlation (r = 0.57) between both methods. Remote sensing i.e. Sentinel products are emerging as a powerful tool in environmental observation. Although weather conditions could have significant impact on environmental dynamic especially in floodplain lakes, combining and comparing of different methods could improve the preciseness of the methodology as well as assessment reliability.

scholarly journals The Civitavecchia Coastal Environment Monitoring System (C-CEMS): a new tool to analyse the conflicts between coastal pressures and sensitivity areas

2015 ◽  
Vol 12 (4) ◽  
pp. 1595-1623 ◽  
Author(s):  
S. Bonamano ◽  
V. Piermattei ◽  
A. Madonia ◽  
F. Paladini de Mendoza ◽  
A. Pierattini ◽  
...  
Keyword(s):  
Monitoring System ◽  
Numerical Models ◽  
Water Quality Assessment ◽  
Posidonia Oceanica ◽  
Coastal Environment ◽  
Marine Ecology ◽  
Environmental Research ◽  
Environment Monitoring ◽  
In Situ Data

Abstract. The understanding of the coastal environment is fundamental for efficiently and effectively facing the pollution phenomena, as expected by Marine Strategy Directive, which is focused on the achievement of Good Environmental Status (GES) by all Member States by 2020. To address this, the Laboratory of Experimental Oceanology and Marine Ecology developed a multi-platform observing network that has been in operation since 2005 in the coastal marine area of Civitavecchia, where multiple uses and high ecological values closely coexist. The Civitavecchia Coastal Environment Monitoring System (C-CEMS), implemented in the current configuration, includes various modules that provide integrated information to be used in different fields of the environmental research. The long term observations acquired by the fixed stations are integrated by in situ surveys, periodically carried out for the monitoring of the physical, chemical and biological characteristics of the water column and marine sediments, as well as of the benthic biota. The in situ data, integrated with satellite observations (e.g., temperature, chlorophyll a and TSM), are used to feed and validate the numerical models, which allow analyses and forecasting of the dynamics of conservative and non-conservative particles under different conditions. As examples of C-CEMS applications, two case studies are reported in this work: (1) the analysis of faecal bacteria dispersion for bathing water quality assessment and, (2) the evaluation of the effects of the dredged activities on Posidonia meadows, which make up most of the two sites of community importance located along the Civitavecchia coastal zone. The simulations results are combined with Posidonia oceanica distribution and bathing areas presence in order to resolve the conflicts between coastal uses (in terms of stress produced by anthropic activities) and sensitivity areas management.

Design of Multi Parameter Water Quality Online Monitoring System Base on FPGA

2014 ◽  
Vol 1061-1062 ◽  
pp. 945-949
Author(s):  
Bao Liang Yang ◽  
Jia Yong Ye ◽  
Zheng Fu Cheng
Keyword(s):  
Water Quality ◽  
Data Acquisition ◽  
Monitoring System ◽  
Remote Monitoring ◽  
Online Monitoring ◽  
Data Communication ◽  
Water Quality Monitoring ◽  
Nios Ii ◽  
Online Monitoring System ◽  
Point Data

]:With increasingly serious pollution to surrounding environment,water quality is deteriorating gradually, which on a certain level threats our daily life. In order to detect problems timely, based on FPGA, Multi Parameter Water Quality Online Monitoring System is designed, which, taking Cyclone II FPGA that supports Nios II processor as the core, is responsible for data acquisition, analysis and processing. Data communication adopts General Packet Radio Service (GPRS) to realize the data transmission from acquisition point to the monitoring center. In the analysis of the existing problems of the current water quality monitoring systems, we present the structure and working principle of this system, and then illustrate them from two aspects: hardware and software. Practice shows that the Multi Parameter Water Quality Online Monitoring system with a simple structure is steady, reliable and easy-operated in processing multi-point data acquisition and remote monitoring the whole system, and proves a widely application in the water supply plant of villages and small towns.Key words: FPGA; Water quality; Nios II; remote monitoring

Improving in situ data acquisition using training images and a Bayesian mixture model

2016 ◽  
Vol 91 ◽  
pp. 49-63 ◽  
Author(s):  
Mohammad Javad Abdollahifard ◽  
Gregoire Mariethoz ◽  
Mohammadreza Pourfard
Keyword(s):  
Data Acquisition ◽  
Mixture Model ◽  
Training Images ◽  
In Situ Data ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture

scholarly journals Retrieval and mapping of chlorophyll-a concentration from Sentinel-2 images in an urban river in the semiarid region of Brazil

2020 ◽  
Vol 15 (2) ◽  
pp. 1
Author(s):  
Alessandro Rhadamek Alves Pereira ◽  
João Batista Lopes ◽  
Giovana Mira de Espindola ◽  
Carlos Ernando da Silva
Keyword(s):  
Chlorophyll A ◽  
River Mouth ◽  
Water Quality Monitoring ◽  
Semiarid Region ◽  
Surface Reflectance ◽  
Chlorophyll A Concentration ◽  
In Situ Data ◽  
Sentinel 2A ◽  
Sentinel 2

Recently, the Poti river mouth region has experienced environmental impacts that resulted in a change of landscape in its dry season, highlighting the eutrophication and proliferation of phytoplankton, algae, cyanobacteria and aquatic plants. Considering the aspects related to water-quality monitoring in the semiarid region of Brazil from remote sensing, this study aimed to evaluate the performance of Sentinel-2A satellite data in the retrieval of chlorophyll-a concentration in Poti River in Teresina, Piaui, Brazil. The chlorophyll-a concentration retrieval and mapping methodology involved the study of the water surface reflectance in Sentinel-2A images and their correlation with the chlorophyll-a data collected in situ during the years 2016 and 2017. The results generated by the Chl-1, Ha et al. (2017), Chl-2, Page et al. (2018), and Chl-3, Kuhn et al. (2019) equations show the need for calibrating the algorithms used for the Poti River water components. However, the empirical algorithm Chl-2 shows a correlation has been established to identify the spatiotemporal variation of chlorophyll-a concentration along the Poti River broadly and not punctually. The spatial distribution of this pigment in maps derived from Sentinel-2A is consistent with the pattern of occurrence determined by the in situ data. Therefore, the MSI sensor proved to be a tool suitable for the retrieval and monitoring of chlorophyll-a concentration along the Poti River.

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