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Water quality monitoring is an important component of water resources management. In order to predict two water quality variables, namely dissolved oxygen (DO; mg/L) and chlorophyll-a (Chl-a; µg/L) in the Small Prespa Lake in Greece, two standalone deep learning (DL) models, the long short-term memory (LSTM) and convolutional neural network (CNN) models, along with their hybrid, the CNN–LSTM model, were developed. The main novelty of this study was to build a coupled CNN–LSTM model to predict water quality variables. Two traditional machine learning models, support-vector regression (SVR) and decision tree (DT), were also developed to compare with the DL models. Time series of the physicochemical water quality variables, specifically pH, oxidation–reduction potential (ORP; mV), water temperature (°C), electrical conductivity (EC; µS/cm), DO and Chl-a, were obtained using a sensor at 15-min intervals from June 1, 2012 to May 31, 2013 for model development. Lag times of up to one (t − 1) and two (t − 2) for input variables pH, ORP, water temperature, and EC were used to predict DO and Chl-a concentrations, respectively. Each model’s performance in both training and testing phases was assessed using statistical metrics including the correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE), their normalized equivalents (RRMSE, RMAE; %), percentage of bias (PBIAS), Nash–Sutcliffe coefficient (ENS), Willmott’s Index, and graphical plots (Taylor diagram, box plot and spider diagram). Results showed that LSTM outperformed the CNN model for DO prediction, but the standalone DL models yielded similar performances for Chl-a prediction. Generally, the hybrid CNN–LSTM models outperformed the standalone models (LSTM, CNN, SVR and DT models) in predicting both DO and Chl-a. By integrating the LSTM and CNN models, the hybrid model successfully captured both the low and high levels of the water quality variables, particularly for the DO concentrations.

In this paper, we analyze the relationship between economic complexity and environmental performance using annual data on 88 developed and developing countries for the period of 2002–2012. We use the Economic Complexity Index, which links a country’s productive structure with the amount of knowledge and know-how embodied in the goods it produces, and the Environmental Performance Index as a measure of environmental performance. We show that moving to higher levels of economic complexity leads to better overall environmental performance, which means that sophistication of exported products does not induce environmental degradation. Nevertheless, we find that the effect of economic complexity on air quality is negative, i.e., exposure to PM2.5, CO2, methane and nitrous oxide emissions increases, and these findings are robust across alternative econometric specifications.

Monkeypox (MPX), similar to both smallpox and cowpox, is caused by the monkeypox virus (MPXV). It occurs mostly in remote Central and West African communities, close to tropical rain forests. It is caused by the monkeypox virus in the Poxviridae family, which belongs to the genus Orthopoxvirus. We develop and analyse a deterministic mathematical model for the monkeypox virus. Both local and global asymptotic stability conditions for disease-free and endemic equilibria are determined. It is shown that the model undergo backward bifurcation, where the locally stable disease-free equilibrium co-exists with an endemic equilibrium. Furthermore, we determine conditions under which the disease-free equilibrium of the model is globally asymptotically stable. Finally, numerical simulations to demonstrate our findings and brief discussions are provided. The findings indicate that isolation of infected individuals in the human population helps to reduce disease transmission.

Landslide susceptibility assessment is highly valuable for disaster prevention and mitigation. This study utilized the aspects of data and content to comprehensively examine the research status of landslide susceptibility. First, we used CiteSpace to visually analyze papers with “landslide susceptibility” as their theme word in the Web of Science database from 1991 to 2020. Next, we summarized the characteristics of quantitative trends, journals, authors, organization types, and keywords, and created a map of authors, institutions, and keywords. Then we presented the common methods and their advantages and disadvantages of landslide inventory, evaluation index, evaluation unit, evaluation model and verification method, combs the shortcomings of each part at the present stage, and looks forward to the possible research direction in the future. Finally, the research difficulties of landslide susceptibility in spatial scale, qualitative and quantitative problems, and spatial representation of landslide information are discussed. We find that the development of 3S and new technology in computer field promotes the development of landslide susceptibility research, makes landslide inventory more efficient and accurate, makes the assessment factor system considering factor contribution more reasonable, and makes more intelligent models applied to landslide susceptibility research. The results are beneficial for researchers to understand the current landslide susceptibility condition and can provide a reference for subsequent landslide susceptibility studies.

This study aims to demonstrate the impact of renewable energy consumption (REC) on environmental degradation using the EKC hypothesis testing for the BRIC and G-7 countries. Two EKC models were created and tested, with Model 2 including REC and other independent variables such as economic freedom (EF) and economic policy uncertainty (EPU), which affect the level of renewable energy consumption and CO2 emissions. Empirical findings indicate that the EKC hypothesis is verified faster in the REC-EF-EPU-based EKC model (Model 2) than in the EF-EPU-based EKC model (Model 1) for G-7 countries since the turning point takes place earlier in Model 2 than in Model 1 with REC. This suggests that renewable energy consumption accelerates the reduction of CO2 emissions. Moreover, this earlier turning point results in lower environmental cleaning costs, less time vesting, and saving resources and money for G-7 countries. However, the study found no evidence supporting the EKC hypothesis for the BRIC countries.

The Covid-19 is a highly contagious disease which becomes a serious global health concern. The residents living in areas with high population density, such as big or metropolitan cities, have a higher probability to come into close contact with others and consequently any contagious disease is expected to spread rapidly in dense areas. However, recently, after analyzing Covid-19 cases in the USA researchers at the Johns Hopkins Bloomberg School of Public Health, London school of economics, and IZA—Institute of Labour Economics conclude that the spread of Covid-19 is not linked with population density. Here, we investigate the influence of population density on Covid-19 spread and related mortality in the context of India. After a detailed correlation and regression analysis of infection and mortality rates due to Covid-19 at the district level, we find moderate association between Covid-19 spread and population density.

Coronavirus disease (COVID-19) is an inflammation disease from a new virus. The disease causes respiratory ailment (like influenza) with manifestations, for example, cold, cough and fever, and in progressively serious cases, the problem in breathing. COVID-2019 has been perceived as a worldwide pandemic and a few examinations are being led utilizing different numerical models to anticipate the likely advancement of this pestilence. These numerical models dependent on different factors and investigations are dependent upon potential inclination. Here, we presented a model that could be useful to predict the spread of COVID-2019. We have performed linear regression, Multilayer perceptron and Vector autoregression method for desire on the COVID-19 Kaggle data to anticipate the epidemiological example of the ailment and pace of COVID-2019 cases in India. Anticipated the potential patterns of COVID-19 effects in India dependent on data gathered from Kaggle. With the common data about confirmed, death and recovered cases across India for over the time length helps in anticipating and estimating the not so distant future. For extra assessment or future perspective, case definition and data combination must be kept up persistently.

The landslide caused a huge disaster to the living environment and seriously threatened the lives and property safety of nearby residents. Assess or predict landslide-susceptible the landslide displacement through monitoring are great beneficial to guide landslide control and mitigate these hazards by taking appropriate preparatory measures. In this paper, a new water cycle algorithm optimization BP neural network (BPNN) dynamic prediction model (WCA-BPNN) was established to make up for the shortcoming of BPNN convergence speed. A typical step-wise landslide——Langshuwan Landslide happened in the Three Gorges Reservoir area of China is taken as a case, and the displacement monitoring data of 4 years was used for time series analysis and modeling. The long-term creep effect of the landslide and the short-term acceleration effect of the climate are considered in the model, and the accumulative displacement is divided into two kinds of trend displacement and periodic displacement. The key influencing factors of landslide periodic displacement were screened by gray relational grade analysis method, and then used as learning data. In addition to comparing the predicted value of the model with the measured value, it also compares the accuracy of the three models of BPNN, support vector machine, extreme learning machine under the training conditions of the same learning data set. The results show that the WAC-BPNN model has faster convergence speed and higher prediction accuracy than the traditional BPNN model, and it is also the most accurate of the four models.

Accurate runoff forecasting plays an important role in management and utilization of water resources. This paper investigates the accuracy of hybrid long short-term memory neural network and ant lion optimizer model (LSTM–ALO) in prediction of monthly runoff. As the parameters of long short-term memory neural network (LSTM) have influence on the prediction performance, the parameters of the LSTM are calibrated by using ant lion optimizer. Then the selection of suitable input variables of the LSTM–ALO is discussed for monthly runoff forecasting. Finally, we decompose root mean square error into three parts, which can help us better understanding the origin of differences between the observed and predicted runoff. To test the merits of the LSTM–ALO for monthly runoff forecasting, other models are employed to compare with the LSTM–ALO. The scatter-plots and box-plots are adopted for evaluating the performance of all models. In the case study, simulation results with the historical monthly runoff of the Astor River Basin show that the LSTM–ALO model has higher accuracy than that of other models. Therefore, the proposed LSTM–ALO provides an effective method for monthly runoff forecasting.

Abstract This paper presents a new mathematical model of the green closed-loop supply chain network (GCLSCN) during the COVID-19 pandemic. The suggested model can explain the trade-offs between environmental (minimizing CO2 emissions) and economic (minimizing total costs) aspects during the COVID-19 outbreak. Considering the guidelines for hygiene during the outbreak helps us design a new sustainable hygiene supply chain (SC). This model is sensitive to the cost structure. The cost includes two parts: the normal cost without considering the coronavirus pandemic and the cost with considering coronavirus. The economic novelty aspect of this paper is the hygiene costs. It includes disinfection and sanitizer costs, personal protective equipment (PPE) costs, COVID-19 tests, education, medicines, vaccines, and vaccination costs. This paper presents a multi-objective mixed-integer programming (MOMIP) problem for designing a GCLSCN during the pandemic. The optimization procedure uses the scalarization approach, namely the weighted sum method (WSM). The computational optimization process is conducted through Lingo software. Due to the recency of the COVID-19 pandemic, there are still many research gaps. Our contributions to this research are as follows: (i) designed a model of the green supply chain (GSC) and showed the better trade-offs between economic and environmental aspects during the COVID-19 pandemic and lockdowns, (ii) designed the hygiene supply chain, (iii) proposed the new indicators of economic aspects during the COVID-19 outbreak, and (iv) have found the positive (reducing CO2 emissions) and negative (increase in costs) impacts of COVID-19 and lockdowns. Therefore, this study designed a new hygiene model to fill this gap for the COVID-19 condition disaster. The findings of the proposed network illustrate the SC has become greener during the COVID-19 pandemic. The total cost of the network was increased during the COVID-19 pandemic, but the lockdowns had direct positive effects on emissions and air quality.