The major objective of this research paper is to assess the management effectiveness of Sheikh Badin National Park District Dera Ismail Khan Khyber Pakhtunkhwa, Pakistan with respect to tourist’s satisfaction. A sample size of 389 respondents (local community, wildlife staff, tourists) were selected through simple random sampling to conclude respondents’ attitude towards phenomenon investigated through three-level Likert scale as a measurement tool. Association between a dependent variable (management effectiveness) was assessed on the independent variables (tourist satisfaction) through a chi-square test. Association of management effectiveness was highly significant with tourists satisfaction from promos of park (p = 0.000), access to information (p = 0.000), roads network (p = 0.000), residential facilities (p = 0.000), trained guides (p = 0.000), safety from crimes and criminals (p = 0.000), provision of health and security services (p = 0.000), overall satisfaction of tourists (p = 0.000), recommendation of SBNP to other tourists (p = 0.000) and revisit intentions of tourists (p = 0.000). Improvement in security measures, better advertisement and improvement in park infrastructure were major recommendations considering the study.

The purpose of this paper is to explore the performance of ridge regression and the random forest model improved by genetic algorithm in predicting the Boston house price data set and conduct a comparative analysis. To achieve it, the data is divided into training set and test set according to the ratio of 70-30. The RidgeCV library is used to select the best regularization parameter for the Ridge regression model, and for the random forest model, the genetic algorithm is used to optimize the model's hyperparameters. The result shows that compared with ridge regression, the random forest model improved by genetic algorithm can perform better in the regression problem of Boston house prices.

To gain a deep understanding of maintenance and repair planning, investigate the weak points of the distribution network, and discover unusual events, it is necessary to trace the shutdowns that occurred in the network. Many incidents happened due to the failure of thermal equipment in schools. On the other hand, the most important task of electricity distribution companies is to provide reliable and stable electricity, which minimal blackouts and standard voltage should accompany. This research uses seasonal time series and artificial neural network approaches to provide models to predict the failure rate of one of the equipment used in two areas covered by the greater Tehran electricity distribution company. These data were extracted weekly from April 2019 to March 2021 from the ENOX incident registration software. For this purpose, after pre-processing the data, the appropriate final model was presented with the help of Minitab and MATLAB software. Also, average air temperature, rainfall, and wind speed were selected as input variables for the neural network. The mean square error has been used to evaluate the proposed models’ error rate. The results show that the time series models performed better than the multi-layer perceptron neural network in predicting the failure rate of the target equipment and can be used to predict future periods.

In order to promote the application of noise map in high-speed railway noise management, the high-speed railway noise map drawing technology based on the combination of noise prediction model and geographic information system (GIS) is studied. Firstly, according to the distribution characteristics of noise sources and line structure characteristics of high-speed railway, the prediction model of multi equivalent sound sources and the calculation method of sound barrier insertion loss of high-speed railway are optimized; secondly, a three-dimensional geographic information model of a high-speed railway is built in GIS software, and the railway noise prediction technology based on the model is developed again; then, the noise of discrete nodes is calculated, and the continuous noise distribution map is drawn by spatial interpolation. The research results show that the comparison error between the noise map of a high-speed railway drawn by this technology and the measured results is less than 1 dB (A), which verifies the accuracy and practicality of the high-speed railway noise map, and can be used as a reference for the railway noise management department to formulate noise control countermeasures.