Using neural networks and remote sensing for spatio-temporal prediction of air pollution during the COVID-19 pandemic.

The study aims to monitor air pollution in Iranian metropolises using remote sensing, specifically focusing on pollutants such as O3, CH4, NO2, CO2, SO2, CO, and suspended particles (aerosols) in 2001 and 2019. Sentinel 5 satellite images are utilized to prepare maps of each pollutant. The relationship between these pollutants and land surface temperature (LST) is determined using linear regression analysis. Additionally, the study estimates air pollution levels in 2040 using Markov and Cellular Automata (CA)-Markov chains. Furthermore, three neural network models, namely multilayer perceptron (MLP), radial basis function (RBF), and long short-term memory (LSTM), are employed for predicting contamination levels. The results of the research indicate an increase in pollution levels from 2010 to 2019. It is observed that temperature has a strong correlation with contamination levels (R2 = 0.87). The neural network models, particularly RBF and LSTM, demonstrate higher accuracy in predicting pollution with an R2 value of 0.90. The findings highlight the significance of managing industrial towns to minimize pollution, as these areas exhibit both high pollution levels and temperatures. So, the study emphasizes the importance of monitoring air pollution and its correlation with temperature. Remote sensing techniques and advanced prediction models can provide valuable insights for effective pollution management and decision-making processes.

An applicability test of the conventional and neural network methods to map the overall water quality of the Caspian Sea.

This study investigates the water quality of the Caspian Sea by examining the presence of nutrients and heavy metals in the water. Water samples were collected from 22 stations and analyzed for nutrient and heavy metal levels. The study used the fuzzy method to prepare water quality maps and employed ANNs methods to predict microbial contamination for future years. The results revealed that the western and northwestern parts of the region had higher nutrient levels (about 40.2 % of the region), while the eastern and northeastern shores were highly polluted due to increased urbanization (about 70.1 % of the region). The long short-term memory (LSTM) method was found to have the highest accuracy compared to other ANNs methods and indicated a recent increase in pollution (RWater quality2=0.940, ROECD2=0.950, RTRIX2=0.840). The study recommends targeted research to identify the causes and means of controlling pollution in light of the predicted increase in pollution in the Caspian Sea.

A hybrid GIS-MCDM approach for multi-level risk assessment and corresponding effective criteria in optimal solar power plant.

This study aims to propose a hybrid method for suitability assessment with different risk levels to construct solar power plants (CSPPs) in southern Iran. The fuzzy-analytic hierarchy process (AHP) and fuzzy were applied to forecast and determine the suitable location for CSPPs. To extract suitable location maps with different risk levels for CSPPs, ordered weighted averaging (OWA) was implemented. In addition, the best subset regression method was used to determine the most effective factors in CSPPs. Based on the results of the fuzzy-AHP method, 42% of the southern regions of the area was suitable for CSPPs. Based on the results of the OWA method, the most suitable areas were located in the north and south in all of the risk areas with increasing values. The results demonstrated that the FCM and sub-clustering approaches can accurately predict land suitability classes (LSCs) for CSPPs. Moreover, the best subsets regression (BSR) results showed that distance to power transmission line (PTL) and temperature exhibited the strongest correlation. Finally, receiver operating characteristics (ROC) were used to determine the accuracy of these methods. The results showed that the area under the curve (AUC) values were highly accurate (AUCFuzzy-AHP = 85.0%, AUCOWA = 83.0%).

Land quality evaluation for sustainable development goals: a structured review using bibliometric and social network analysis.

This literature review focuses on land quality evaluation (LQE) and its effects on sustainable development through quantitative analysis and value-added information. In contrast to the traditional perspective, a structured review based on bibliometric indicators and social network analysis allows identifying hidden evidence for answering the following research questions: (i) What: What is the application of LQE? (ii) Which: Which sustainable development goals does the application contribute to? (iii) Why and how: What are the main applications and methods of each topic? (iv) Where: Where is the hotspot of the problem? What is the future research orientation of the topic? (v) How and when: How has the topic grown since 2000? Data investigation explores 4029 articles in 2000-2019 from four publishers. With the support of VOSviewer software, six clusters corresponding to six main applications of LQE are classified. Overlapping keywords in several clusters are resolved by the binary term frequency counter for the cluster preference determination. After conducting the data verification and editing process, a structured review is performed again with systematic research questions. This research offers a synthesis of traditional and novel quantitative analysis for literature review, which is comprehensive, accurate, and reliable.

Mapping the potential aggregation values of ecotourism landscapes from stakeholder survey, structural equation modeling and GIS: Case study of Moc Chau site, Vietnam.

The primary aim of this study is to propose a potential landscape value assessment from different dimensions rather than the traditional approach of a composite indicator. The method used in this study is the combination of data collection from stakeholder survey, score measurement for landscape value dimensions using Structural Equation Modeling (SEM), and spatial representation with the support of Geographic Information System (GIS). From a large-scale (n = 400) investigation in the Moc Chau district, the statistical data extracted from the survey provides input data for the score determination process. SEM analysis shows that each landscape site has 11 determinants influencing the landscape value assessment. Using the RMSE comparison (for validation) with different interpolation methods, the ordinary kriging method is chosen to model the aggregation landscape value map of Moc Chau District. About 24.97% total area of the study area has great potential for tourism development, being mainly distributed in the center of a high mountainous area. This approach can be used as a model to advocate local and regional assessment and enhance value-based management in other territories in Vietnam and beyond.