CPSO-LSSVM Model-based Risk Assessment for New Energy Project in Power Industry

Nowadays, while cities are often subject to research in terms of their development, especially smart development, studies on rural areas are rare. However, the development of the latter is very important. It is important that rural areas develop economically and socially. Smart villages are a challenge for the modern energy sector. The authors of the article try to answer the question: What are the challenges for the modern energy sector in the context of rural development? The aim of this article is to identify challenges for the modern power industry in the concept of smart countryside development. The article begins with the presentation of the essence of smart villages and the essence of energy policy. The research facilitated the identification of basic challenges that prevent or slow down the development of the smart villages in terms of modern energy solutions, as perceived by experts and residents, and farmers and entrepreneurs operating in rural areas. The article identifies a number of energy challenges in the context of a smart village. They include, among others, a lack of awareness regarding the impact of energy on the environment, a low level of public knowledge about new energy solutions, and a lack of social trust in modern energy solutions in rural areas. The research was conducted in rural areas in the north-western part of Poland. At the end, the article presents a model of rural development in the context of the modern energy sector. The research also allowed the creation of a smart village development model that focuses on smart economy, intelligent environment, intelligent people, and intelligent governance. Since these are universal solutions, they can be used as a proposition for other countries.

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Safety Risk Assessment of Tourism Management System Based on PSO-BP Neural Network

Computational Intelligence and Neuroscience ◽

10.1155/2021/1980037 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Wenru Guo

Keyword(s):

Neural Network ◽

Risk Assessment ◽

Management System ◽

Evaluation Model ◽

Risk Index ◽

Security Risk ◽

Bp Network ◽

Model Based ◽

Tourism Management ◽

Better Than

With the development of science and technology, system management is gradually applied to tourism management. How to correctly assess the security risks of the tourism management system has become an important means to maintain passenger information. The security risk index of the travel management system is input into the PSO-BP network as a sample, and the corresponding risk value of the index is used as the network output. The results show that the error results, accuracy (96.53%), training time (216 s), number of iterations (275 times), and convergence speed are all better than traditional BP network. The relative error of PSO-BP network (0.32%) is better than that of BP network, with 300 iterations, and the error is close to 10–5. The average evaluation accuracy of S based on PSO-BP network is 99.72%, and the average time consumed is 2.512 s. It is superior to the evaluation model based on fuzzy set and entropy weight theory and the evaluation model based on gray correlation analysis and radial basis function neural network. In conclusion, the security risk assessment of the tourism management system based on PSO-BP network can effectively assess the security risk of the tourism management system.

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Development of a machine learning model to estimate biotic ligand model-based predicted no‐effect concentrations for copper in freshwater

10.1101/2021.09.16.460690 ◽

2021 ◽

Author(s):

Jiwoong Chung ◽

Geonwoo Yoo ◽

Jinhee Choi ◽

Jong-Hyeon Lee

Keyword(s):

Risk Assessment ◽

Electrical Conductivity ◽

Environmental Risk ◽

Biotic Ligand Model ◽

Data Availability ◽

Outcome Variable ◽

Model Based ◽

Biotic Ligand ◽

Input Variables ◽

Ligand Model

The copper biotic ligand model (BLM) has been used for environmental risk assessment by taking into account the bioavailability of copper in freshwater. However, the BLM-based environmental risk of copper has been assessed only in Europe and North America, with monitoring datasets containing all of the BLM input variables. For other areas, it is necessary to apply surrogate tools with reduced data requirements to estimate the BLM-based predicted no-effect concentration (PNEC) from commonly available monitoring datasets. To develop an optimized PNEC estimation model based on an available monitoring dataset, an initial model that considers all BLM variables, a second model that requires variables excluding alkalinity, and a third model using electrical conductivity as a surrogate of the major cations and alkalinity have been proposed. Furthermore, deep neural network (DNN) models have been used to predict the nonlinear relationships between the PNEC (outcome variable) and the required input variables (explanatory variables). The predictive capacity of DNN models in this study was compared with the results of other existing PNEC estimation tools using a look-up table and multiple linear and multivariate polynomial regression methods. Three DNN models, using different input variables, provided better predictions of the copper PNECs compared with the existing tools for four test datasets, i.e., Korean, United States, Swedish, and Belgian freshwaters. The adjusted r2 values in all DNN models were higher than 0.95 in the test datasets, except for the Swedish dataset (adjusted r2 > 0.87). Consequently, the most applicable model among the three DNN models could be selected according to the data availability in the collected monitoring database. Because the most simplified DNN model required only three water quality variables (pH, dissolved organic carbon, and electrical conductivity) as input variables, it is expected that the copper BLM-based risk assessment can be applied to monitoring datasets worldwide.

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