Intelligent Planning of Tourism Scenic Routes Based on Genetic Algorithm in Coal WANBEI Mining Subsidence

Li, Xiaofang; Song, Hui

doi:10.12928/telkomnika.v14i2a.4352

Cited by 2 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the results are more random because of the influence of the simulation unit and value parameters of rock formation [16]. In addition, based on a large amount of measured field data, several further methods have been proposed to determine predicted parameters, including neural network method [17], support vector machine [18], and genetic algorithm [19]. These methods provide additional ideas for determining predicted parameters, but none of them consider the essential causes of mining subsidence, i.e., the movement of the subterranean strata, and the influence of the strata distribution on the predicted parameters in the donga areas.…”

Section: Introductionmentioning

confidence: 99%

Prediction Method for Surface Subsidence of Coal Seam Mining in Loess Donga Based on the Probability Integration Model

et al. 2022

View full text Add to dashboard Cite

The accurate prediction of surface subsidence is a significant foundation for the damage assessment of coal seam mining and ecological environment reclamation in loess donga. However, conventional models are very problematic, and the reliability of prediction is usually low. Therefore, we propose a method for predicting surface subsidence of coal seam mining in loess donga that is based on the probability integration model, combined with the movement principle of rock and soil layers in the respective study area, and considering the influence of slope stability and additional mining slip on mining subsidence. The feasibility of our new method was verified by a case study in the N1114 working face of the Ningtiaota coal mine (China) that is situated in an area with abundant loess dongas. The results show that slope slippage is the source of error in the prediction of subsidence in loess donga. The prediction idea of “dividing the surface of loess donga into horizontal strata area and slope sub-area, and predicting the subsidence value of the two areas, respectively” is put forward. A method for predicting the subsidence value of two regions is established. First, based on the theory of probability integral and rock formation movement, the probability integral parameters of the horizontal stratum area are determined, and the subsidence basins in the area are superimposed and calculated. Secondly, according to the slope stability and slip principle, the additional displacement of subsidence in the slope area with mining instability coefficient Gcs > 0.87 is calculated. Finally, combined with the subsidence prediction results of the strata area and the slope sub-area, and the position of the slope, the accurate prediction of the surface subsidence in loess donga is realized. Our results show that the agreement between the curves predicted from our calculations and from the measured data are between 88.7–97.8%. The calculated error of the additional displacement of slope mining slip is between 1.0–9.8%. The excellent correlation between the modelled and measured data documents that our method provides, demonstrated a new efficient and valuable tool for the precise prediction of damages induced by mining of underground coal seams in loess donga.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction Method for Surface Subsidence of Coal Seam Mining in Loess Donga Based on the Probability Integration Model

et al. 2022

View full text Add to dashboard Cite

show abstract

“…unable to consider the potential income of the city transport drivers appropriately. In other areas such as in the determination of tourism scenic routes, the determination of an optimal tourist route is designed by considering the constraints of slope and roughness [3]. Meanwhile in Express Delivery Routing optimization, the value of the transportation costs is a measure for optimal route determination [4].…”

Section: Introductionmentioning

confidence: 99%

The Prediction of Optimal Route of City Transportation Based on Passenger Occupancy using Genetic Algorithm: A Case Study in The City of Bandung

2018

View full text Add to dashboard Cite

Currently, the existence of city transport is increasingly eliminated b y private vehicles such as cars and motorcycles. This situation is further exacerb ated b y the b ehavior of city transport drivers who are less discipline in driving, or in picking up and dropping off their passengers. The b ad b ehavior is partly caused b y the low level of passenger occupancy. The drivers try to search for passengers as much as possib le b ut often ignore the traffic rules. To overcome this prob lem, an optimal transport route with high passenger potential is required. Therefore, this study investigated the optimal route of city transport b ased on the passenger occupancy rate in the city of Bandung as the case study. The method employed for determining the optimal route is Genetic algorithm comb ined with Ordinary Kriging method used for the process of passenger prediction and fitness calculation. The optimal routes are those with higher occupancy rate. The analysis results showed that the use of the Genetic algorithm with a low numb er of generations succeed in creating new optimal routes even though the increase is not too high the maximum only reaches 4%.This result is certainly important enough to b e used in making b etter pub lic transport routes.

show abstract

Intelligent Planning of Tourism Scenic Routes Based on Genetic Algorithm in Coal WANBEI Mining Subsidence

Abstract: Abstract

Cited by 2 publications

References 14 publications

Prediction Method for Surface Subsidence of Coal Seam Mining in Loess Donga Based on the Probability Integration Model

Prediction Method for Surface Subsidence of Coal Seam Mining in Loess Donga Based on the Probability Integration Model

The Prediction of Optimal Route of City Transportation Based on Passenger Occupancy using Genetic Algorithm: A Case Study in The City of Bandung

Contact Info

Product

Resources

About