Air pollution atmospheric environment detection and global tourism line planning management based on genetic algorithm

2021 ◽  
Vol 14 (17) ◽  
Author(s):  
Li Chen
Keyword(s):  
Genetic Algorithm ◽  
Air Pollution ◽  
Atmospheric Environment ◽  
Line Planning ◽  
Global Tourism ◽  
Environment Detection

scholarly journals A NOVEL APPROACH TO OPTIMIZE THE RIDESHARING PROBLEM USING GENETIC ALGORITHM

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 875-878
Author(s):  
S. Rangriz ◽  
M. Davoodi ◽  
J. Saberian
Keyword(s):  
Genetic Algorithm ◽  
Air Pollution ◽  
Public Transportation ◽  
Noise Pollution ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Traffic Jam ◽  
Novel Approach ◽  
Proposed Model ◽  
Public Transportation Systems

Abstract. The enormous increase in the number of vehicles in the cities makes plenty of problems including air pollution, noise pollution, and traffic jam. Overcoming these annoying issues needs a significant plan in urban management such as using modern techniques in public transportation systems. Sharing either cars or taxies is one of the most interesting ways that has been used in some countries recently. In this phenomenon, 2 or 3 people use other’s car or taxi. In this article, an innovative approach to share taxies is proposed, and it uses a Genetic Algorithm to determine the placement of travelers in taxies. Therefore, some taxis will be switched off, and this helps to decrease urban traffic jam in cities. The results present that the proposed model turns off 69.8 % of taxies, and also 27.8 % of them carry more than one passenger; hence, this confirms the performance of the proposed model.

scholarly journals Hybrid Vector Autoregression Feedforward Neural Network with Genetic Algorithm Model for Forecasting Space-Time Pollution Data

2021 ◽  
Vol 6 (1) ◽  
pp. 243-266 ◽  
Author(s):  
Rezzy Eko Caraka ◽  
Rung Ching Chen ◽  
Hasbi Yasin ◽  
Suhartono Suhartono ◽  
Youngjo Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Air Pollution ◽  
Multivariate Time Series ◽  
Accurate Estimation ◽  
Vector Autoregressive ◽  
Pollution Levels ◽  
Location Data ◽  
Temporal Models ◽  
Life Threatening

The exposure rate to air pollution in most urban cities is really a major concern because it results to a life-threatening consequence for human health and wellbeing. Furthermore, the accurate estimation and continuous forecasting of pollution levels is a very complicated task.  In this paper, one of the space-temporal models, a vector autoregressive (VAR) with neural network (NN) and genetic algorithm (GA) was proposed and enhanced. The VAR could tackle the issue of multivariate time series, NN for nonlinearity, and GA for parameter estimation determination. Therefore, the model could be used to make predictions, such as the information of series and location data. The applied methods were on the pollution data, including NOX, PM2.5, PM10, and SO2 in Taipei, Hsinchu, Taichung, and Kaohsiung. The metaheuristics genetic algorithm was used to enhance the proposed methods during the experiments. In conclusion, the VAR-NN-GA gives a good accuracy when metric evaluation is used. Furthermore, the methods can be used to determine the phenomena of 10 years air pollution in Taiwan.

Air quality management using genetic algorithm based heuristic fuzzy time series model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lalit Bhagat ◽  
Gunjan Goyal ◽  
Dinesh C.S. Bisht ◽  
Mangey Ram ◽  
Yigit Kazancoglu
Keyword(s):  
Genetic Algorithm ◽  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Quality Management ◽  
Fuzzy Time Series ◽  
Selection Algorithm ◽  
Content Type ◽  
Proposed Model ◽  
Adaptive Time

PurposeThe purpose of this paper is to provide a better method for quality management to maintain an essential level of quality in different fields like product quality, service quality, air quality, etc.Design/methodology/approachIn this paper, a hybrid adaptive time-variant fuzzy time series (FTS) model with genetic algorithm (GA) has been applied to predict the air pollution index. Fuzzification of data is optimized by GAs. Heuristic value selection algorithm is used for selecting the window size. Two algorithms are proposed for forecasting. First algorithm is used in training phase to compute forecasted values according to the heuristic value selection algorithm. Thus, obtained sequence of heuristics is used for second algorithm in which forecasted values are selected with the help of defined rules.FindingsThe proposed model is able to predict AQI more accurately when an appropriate heuristic value is chosen for the FTS model. It is tested and evaluated on real time air pollution data of two popular tourism cities of India. In the experimental results, it is observed that the proposed model performs better than the existing models.Practical implicationsThe management and prediction of air quality have become essential in our day-to-day life because air quality affects not only the health of human beings but also the health of monuments. This research predicts the air quality index (AQI) of a place.Originality/valueThe proposed method is an improved version of the adaptive time-variant FTS model. Further, a nature-inspired algorithm has been integrated for the selection and optimization of fuzzy intervals.

scholarly journals A Facility Location Model for Air Pollution Detection

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
G. Lancia ◽  
F. Rinaldi ◽  
P. Serafini
Keyword(s):  
Genetic Algorithm ◽  
Air Pollution ◽  
Air Quality ◽  
Mathematical Models ◽  
Facility Location ◽  
Large City ◽  
Location Model ◽  
Practical Algorithms ◽  
Pollution Detection ◽  
The City

We describe mathematical models and practical algorithms for a problem concerned with monitoring the air pollution in a large city. We have worked on this problem within a project for assessing the air quality in the city of Rome by placing a certain number of sensors on some of the city buses. We cast the problem as a facility location model. By reducing the large number of data variables and constraints, we were able to solve to optimality the resulting MILP model within minutes. Furthermore, we designed a genetic algorithm whose solutions were on average very close to the optimal ones. In our computational experiments we studied the placement of sensors on 187 candidate bus routes. We considered the coverage provided by 10 up to 60 sensors.

Analysis of the Stones of Thousand Hand Buddhism by the Combination of On-Site and Laboratory Techniques

2012 ◽  
Vol 557-559 ◽  
pp. 1461-1466
Author(s):  
Xing Ling Tian ◽  
Nai Sheng Li ◽  
Zhi Guo Zhang ◽  
Yuan Hu ◽  
Chang Fa Zhan
Keyword(s):  
Air Pollution ◽  
Quantitative Analysis ◽  
Surface Morphology ◽  
Ion Chromatography ◽  
Inorganic Anions ◽  
Atmospheric Environment ◽  
Laboratory Techniques ◽  
Chongqing City ◽  
Laboratory Results ◽  
World Cultural Heritage

As world cultural heritage, Thousand Hand Buddhism in Dazu County of Chongqing City has been eroded Seriously after exposure to Wet atmospheric environment for about 800 years. In order to protect it, the reason of erosion should be found first. So the stones of Thousand Hand Buddhism have been analyzed using both on-site techniques(3D video microscopy, portable IR and so on) and laboratory techniques(SEM,AES and Ion chromatography etc.), and the limitations of all the above techniques are discussed. The on site results show that the stones are sandstones which contain montmorillonite, and the surface morphology of stones has changed significantly. The laboratory results show that many acidic inorganic anions can be found. and the content of SO42-is highest among all the inorganic anions, which indicates that the SO2 in the wet air should attend the reaction of the erosion. All the results show that air pollution in the Wet atmospheric environment should be the main reason of the erosion, and that on site techniques have the characteristic of nondestructive, while laboratory techniques can do more Quantitative analysis, only when they are combined can the stone relics be analyzed scientificly.This template explains and demonstrates how to prepare your camera-ready paper for Trans Tech Publications. The best is to read these instructions and follow the outline of this text.

scholarly journals ATMOSPHERIC ENVIRONMENT DETECTION METHOD BASED ON MULTI-ROTOR UAV PLATFORM

2018 ◽  
Vol XLII-3/W5 ◽  
pp. 67-71
Author(s):  
A. Shen ◽  
S. Zhou ◽  
S. Peng
Keyword(s):  
Detection Method ◽  
Center Of Gravity ◽  
Atmospheric Environment ◽  
Detection Accuracy ◽  
Flight Safety ◽  
Rotor Speed ◽  
Gradual Process ◽  
The Stability ◽  
Environment Detection ◽  
Improve Stability

<p><strong>Abstract.</strong> For the method of atmospheric detection based on multi-rotor UAV, the effects of flight safety and airflow on detection accuracy are analyzed. The detector mounted on the multi-rotor drone should be highly symmetrical and the center of gravity should be on the same vertical line as the center of gravity of the drone. Mounting the detector above the drone's fuselage will reduce the stability of the drone, and lowering it will improve stability, but for models with high stability, placing it below will affect the maneuverability of the drone. The airflow interference in the area where the detector can be placed near the fuselage is related to the drone: the faster the rotor speed, and the closer the rotor is to the fuselage, the stronger the airflow interference. At the same time, the influence of airflow disturbance on temperature and humidity has a gradual process. Waiting for a period of time after starting the drone can change the error into a fixed error for post-processing. This method was used to detect a pollution process and achieved good results.</p>

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