Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir,Turkey

The predictive ability of a hybrid model integrating the Firefly Algorithm (FFA), as a heuristic optimization tool with the Multilayer Perceptron (MLP-FFA) algorithm for the prediction of water level in Lake Egirdir, Turkey, is investigated. The accuracy of the hybrid MLP-FFA model is then evaluated against the standalone MLP-based model developed with the Levenberg–Marquadt optimization scheme applied for in the backpropagation-based learning process. To develop and investigate the veracity of the proposed hybrid MLP-FFA model, monthly time scale water level data for 56 years (1961–2016) are applied to train and test the hybrid model. The input combinations of the standalone and the hybrid predictive models are determined in accordance with the Average Mutual Information computed from the historical water level (training) data; generating four statistically significant lagged combinations of historical data to be adopted for the 1-month forecasting of lake water level. The proposed hybrid MLP-FFA model is evaluated with statistical score metrics: Nash–Sutcliffe efficiency, root mean square and mean absolute error, Wilmott’s Index and Taylor diagram developed in the testing phase. The analysis of the results showed that the hybrid MLP–FFA4 model (where 4 months of lagged combinations of lake water level data are utilized) performed more accurately than the standalone MLP4 model. For the fully optimized hybrid (MLP-FFA4) model evaluated in the testing phase, the Willmott’s Index was approximately 0.999 relative to 0.988 (MLP 4) and the root mean square error was approximately 0.029 m and compared to 0.102 m. Moreover, the inter-comparison of the forecasted and the observed data with various other performance metrics (including the Taylor diagram) verified the robustness of the proposed hybrid MLP-FFA4 model over the standalone MLP4 model applied in the problem of forecasting lake water level prediction in the current semi-arid region in Turkey.     

A Topology‐Inferred Graph‐Based Heuristic Algorithm for Map Simplification

In this article we present a heuristic map simplification algorithm based on a novel topology‐inferred graph model. Compared with the existing algorithms, which only focus either on geometry simplification or on topological consistency, our algorithm simplifies the map composed of series of polylines and constraint points while maintaining the topological relationships in the map, maximizing the number of removal points, and minimizing error distance efficiently. Unlike some traditional geometry simplification algorithms, such as Douglas and Peucker's, which add points incrementally, we remove points sequentially based on a priority determined by heuristic functions. In the first stage, we build a graph to model the topology of points in the map from which we determine whether a point is removable or not. As map generalization is needed in different applications with different requirements, we present two heuristic functions to determine the priority of points removal for two different purposes: to save storage space and to reduce computation time. The time complexity of our algorithm is which is efficient enough to be considered for real‐time applications. Experiments on real maps were conducted and the results indicate that our algorithm produces high quality results; one heuristic function results in higher removal points saving storage space and the other improves the time performance significantly.     

Response of sediment-dwelling annelida community in relation to geochemical parameters in the Gorgan Bay,Caspian Sea

This study aimed to investigate the population of annelida communities in relation to environmental factors and heavy metals accumulated in sediments of the Gorgan Bay. The pollution load index and potential ecological risk (PER) were calculated. The results indicated mean concentrations (ppm) of heavy metals were (mean ± SD) Pb: 11.5 ± 4.88, Cu: 18 ± 8.83, Zn: 42 ± 22.15, Ni: 29.20 ± 14.68, Co: 10.56 ± 14.68, As: 7.77 ± 2.12, Sr: 1,449 ± 902.59 and V: 26.64 ± 10.25. Considering PER, sediments of the Gorgan Bay had low ecological risk. Based on abundance data, dominant species were Streblospio gynobranchiata, Nereis diversicolor, Tubificoides fraseri and Tubificidae unknown, respectively. Results of redundancy analysis displayed that T. fraseri and N. diversicolor were associated with high values of Sr. All the species were negatively correlated with As. There were positive correlation between S. gynobranchiata and N. diversicolor with values of clay, salinity, depth and silt. The present study provided the relative importance of heavy metals and environmental variables which partly assist in structuring assemblages of annelida in a transitional area.     

A Geographic Information System‐based site selection experience for the construction of a geomagnetic observatory in Kerman Province,Iran

In recent years, the necessity of constructing new geomagnetic observatories in Iran has been discussed from various aspects. Improper site selection of such important data centres can significantly affect the quality of their recorded data. In this research, site selection studies were performed to find the most favourable location to construct a geomagnetic observatory in Kerman Province, southeast of Iran. Having defined 11 site selection criteria for geomagnetic observatories, all the data layers were prepared for the whole province. After detection of seven promising regions using analytical hierarchy process and fuzzy logic method in geographical information system, Technique for Order of Preference by Similarity to Ideal Solution was used for ranking of the suitable areas. The most favourable region was finally detected southwest of Kerman Province, located between the cities of Baft and Sirjan. Detailed land surveys can be focused in this region to decide on the optimum area for the construction of the geomagnetic observatory.     

Understanding experts’ views and risk perceptions on carbon capture and storage in three European countries

The challenge of making the transition to a sustainable energy regime is not limited to engineering; it has important social and political dimensions. Therefore, implementation of new technologies, such as carbon capture and storage (CCS), requires not only economic and technical capacities but also an understanding of social factors. These factors include experts’ views and risk perceptions. Understanding them will contribute to the risk governance of CCS by demonstrating who is concerned about what and why with respect to CCS and how risk perception and stakeholders’ concerns vary in different countries. This research is based on analysis and mapping of data collected from case studies of three countries: Germany, Norway and Finland. Our analysis shows that in countries where opposition to CCS is the strongest, like Germany, risk perceptions can be driven by such factors such as the lack of trust and doubts about the need of the project. At the same time as in countries with moderate opposition, such as Norway or Finland, risk perceptions are more connected with the risk for investment. We also conclude that the strongest polarization in risk perceptions is among NGOs in different countries, followed by scientists. The positions of private sector stakeholders and government are more homogenous. Such large variation in risk perceptions of experts could be influenced by several factors, including cultural orientation, attitudes and views of stakeholders, and the social, political and technical settings for deployment of technology in each country.     

Enhancement of nickel biosorption on fungal biomass by enzymatic and alkali pretreatments

Enzymatic and alkali pretreatments were employed to improve nickel biosorption capacity of Rhizomucor pusillus biomass. Pretreatment with 0.002–80 g l^?1 NaOH and 0.0001–0.1 Anson Unit (AU) g^?1 protease enhanced the biosorption capacity of fungal biomass. Increasing the concentration of NaOH from 0.002 to 5 g l^?1 improved nickel removal from 93.2 to 100.0 % while untreated biomass showed 64.6 % Ni(II) removal. Pretreatment with higher concentrations of NaOH, 5–80 g l^?1 resulted in nearly complete removal of nickel ions. Pretreatment of the biomass with 0.0001 AU g^?1 protease improved the nickel removal to over 91 %, while increasing the enzyme loading to 0.1 AU g^?1 improved the removal to 93 %. Untreated biomass removed 78.4, 63.0, and 96.3 % of chromium, copper, and lead ions, respectively, from a mixture solution of the ions. Respective metal removals were increased to 100, 98.9, and 100 % after pretreatment with 0.2 g l^?1 NaOH solution and to 87.8, 86.7, and 100 % after the enzymatic pretreatment with 0.1 AU g^?1 protease. Scanning electron microscopy analysis indicated that alkali and enzymatic pretreatments enhanced the porosity of the biomass. Furthermore, compositional analysis showed that both of the pretreatments removed a major part of fungal proteins (2.1–95.8 % removal). Glucosamine, N-acetyl glucosamine, and phosphates were the major ingredients of the pretreated biomass.     

Selection of wastewater treatment process based on the analytical hierarchy process and fuzzy analytical hierarchy process methods

This paper presents an application of the analytical hierarchy process and fuzzy analytical hierarchy process methods for selecting the best wastewater treatment process. The analytical hierarchy process is one of the best ways for deciding among the complex criteria structure in different levels, and the fuzzy analytical hierarchy process is a synthetic extension of the classical method when the fuzziness of the decision makers are considered. After reviewing aerobic treatment processes operated in Iran’s industrial estates and determining the main criteria used for treatment process evaluation, they are arranged in a hierarchy structure. Selection of the best wastewater treatment process is a multi-criteria decision making problem. Conventional methods are inadequate for dealing with the imprecise or vague nature of linguistic assessment. To overcome this difficulty, the fuzzy analytical hierarchy process is proposed for dealing with the vagueness of decision makers’ judgments. The alternatives consist of extended aeration, absorption bio-oxidation, integrated fixed-film activated sludge, sequencing batch reactor, aerated lagoon. Based on the general condition of industrial estate’s wastewater treatment plants, technical/administrative, economic and environmental criteria and their sub-criteria are weighted and then criteria evaluated and priorities of alternatives have been done by analytical hierarchy process and fuzzy analytical hierarchy process methods by the use of triangular fuzzy numbers. Finally, selection of the best process and ranking of these five processes are carried out by these foregoing methods, and some sensitivity analyses are conducted to show the results’ sensitiveness to the changes of the weights of the evaluation criteria.     

Prediction of seismic collapse risk of steel moment frame mid-rise structures by meta-heuristic algorithms

Different performance levels may be obtained for sideway collapse evaluation of steel moment frames depending on the evaluation procedure used to handle uncertainties. In this article, the process of representing modelling uncertainties, record to record (RTR) variations and cognitive uncertainties for moment resisting steel frames of various heights is discussed in detail. RTR uncertainty is used by incremental dynamic analysis (IDA), modelling uncertainties are considered through backbone curves and hysteresis loops of component, and cognitive uncertainty is presented in three levels of material quality. IDA is used to evaluate RTR uncertainty based on strong ground motion records selected by the k-means algorithm, which is favoured over Monte Carlo selection due to its time saving appeal. Analytical equations of the Response Surface Method are obtained through IDA results by the Cuckoo algorithm, which predicts the mean and standard deviation of the collapse fragility curve. The Takagi-Sugeno-Kang model is used to represent material quality based on the response surface coefficients. Finally, collapse fragility curves with the various sources of uncertainties mentioned are derived through a large number of material quality values and meta variables inferred by the Takagi-Sugeno-Kang fuzzy model based on response surface method coefficients. It is concluded that a better risk management strategy in countries where material quality control is weak, is to account for cognitive uncertainties in fragility curves and the mean annual frequency.