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One of the most significant issues in urban design is energy-related CO2 emissions, which are rising quickly as cities expand. The GDP of the Central European countries (from 1990 to 2016) based on several energy sources, such as coal, oil, natural gas, and renewable energy, are used as inputs in this study. To develop a reliable predictive network considering the problem complexity, multi-layer perceptron (MLP) is combined with several nature-inspired optimization algorithms, namely, black hole algorithm (BHA), future search algorithm (FSA), backtracking search algorithm (BSA), biogeography-based optimization (BBO), and shuffled complex evolution (SCE). By applying the approaches mentioned above to the synthesis of the MLP, the recommended BBO, BHA, BSA, FSA, and SCE ensembles are obtained. A series of parametric studies are performed to improve the effectiveness of the employed models. It is found that, by combining the BBO, BHA, BSA, FSA, and SCE algorithms, the MLP's accuracy is increased. The result from this parametric analysis showed that SCE and BBO perform better than the other three algorithms as the CO2 emission was computed with the highest level of accuracy using R² = 0.9999 and 0.9998, RMSE = 1.6781 and 2.0539 for SCE, and R² = 0.9999 and 0.9998, RMSE = 1.8689 and 2.3833 for BBO. [ABSTRACT FROM AUTHOR]

Energy-related CO2 emissions are one of the biggest concerns facing urban design today, increasing rapidly as cities grow. This study uses as inputs the GDP of the G8 nations (from 1990 to 2016) depending on the utilization of various energy sources, including coal, oil, natural gas, and renewable energy. Multilayer perceptrons (MLP) are combined with various nature-inspired optimization algorithms, such as Heap-Based Optimizer (HBO), Teaching-Learning-Based Optimization (TLBO), Whale Optimization Algorithm (WOA), Vortex Search algorithm (VS), and Earthworm Optimization Algorithm (EWA), to create a dependable predictive network that takes the complexity of the problem into account. Our key contributions lie in developing and comprehensively evaluating these hybrid models assessing their efficacy in capturing the intricate dynamics of carbon emissions. The study found that TLBO and VS outperform other algorithms in CO2 emission computation accuracy. TLBO has a higher training MSE (3.6778) and lower testing MSE (4.4673), suggesting larger squared errors on training data and lower testing MSE, suggesting less overfitting due to better generalization to the testing set. [ABSTRACT FROM AUTHOR]

This research aims to forecast, using various criteria, the flow of soil erosion that will occur at a particular geographical location. As for the training dataset, 80% of the dataset from the sample sites, four hybrid algorithms, namely heap-based optimizer (HBO), political optimizer (PO), teaching-learning based optimization (TLBO), and backtracking search algorithm (BSA) combined with artificial neural network (ANN) was used to create an erosion susceptibility model and establishes a unique and original approach. After it was confirmed to be successful, the algorithms were applied to create a susceptibility map for this area, demonstrating the integrity of the results. The AUC values were computed for every optimisation algorithm used in this study. The optimal estimated accuracy indices for populations of 450 were determined to be 0.9846 using the BSA-MLP training databases. The maximum AUC value for the HBO-MLP training databases with different swarm sizes was 0.9736. A swarm size of 350-300 is considered optimal for forecasting erosion susceptibility mapping in hybrid models. With the same swarm size constraints, the AUC value for training in the TLBO-MLP scenario was 0.996. After 150 swarm size conditions were used to train the PO-MLP model, the AUC values were 0.9845. According to these findings, the TLBO-MLP and PO-MLP algorithms worked best with populations of 50 and 150, respectively. [ABSTRACT FROM AUTHOR]

Flash floods pose a major challenge in various regions of the world, causing serious damage to life and property. Here we investigated the Izvorul Dorului river basin from Romania, to identify slope surfaces with a high potential for flash-flood employing a combination of fuzzy logic algorithm with the following four machine learning models: classification and regression tree, deep learning neural network, XGBoost and naïve Bayes. Ten flash-flood predictors were used as independent variables to determine the flash-flood potential index. As a dependent variable, we used areas with ttorrential phenomena divided into training (70%) and validating data set (30%). Predictive ability and the degree of correlation between factors were assessed through the correlation-based feature selection (CFS) method and through the confusion matrix, respectively. In the training phase, all ensemble models yielded good and very good accuracies of over 84%. The spatialization of flash-flood potential index (FFPI) over the study area showed that high and very high values of flash-flood potential occur in the northern half of the region and occupy the following weights within the study area: 53.11% (FFPI Fuzzy-CART), 45.09% (Fuzzy-DLNN), 45.58% (Fuzzy-NB) and 44.85% (Fuzzy-XGBoost). The validation of the results was done through the ROC curve method. Thus, according to success rate, Fuzzy-XGBoost (AUC = 0.886) is the best model, while in terms of prediction rate, the ideal one is Fuzzy-DLNN (AUC = 0.84). The novelty of this work is the application of the four ensemble models in evaluating this natural hazard. [ABSTRACT FROM AUTHOR]

Flood-susceptibility mapping is an important component of flood risk management to control the effects of natural hazards and prevention of injury. We used a remote-sensing and geographic information system (GIS) platform and a machine-learning model to develop a flood susceptibility map of Kangsabati River Basin, India where flash flood is common due to monsoon precipitation with short duration and high intensity. And in this subtropical region, climate change's impact helps to influence the distribution of rainfall and temperature variation. We tested three models-particle swarm optimization (PSO), an artificial neural network (ANN), and a deep-leaning neural network (DLNN)-and prepared a final flood susceptibility map to classify flood-prone regions in the study area. Environmental, topographical, hydrological, and geological conditions were included in the models, and the final model was selected based on the relations between potentiality of causative factors and flood risk based on multi-collinearity analysis. The model results were validated and evaluated using the area under receiver operating characteristic (ROC) curve (AUC), which is an indicator of the current state of the environment and a value >0.95 implies a greater risk of flash floods. The AUC values for ANN, DLNN, and PSO for training datasets were 0.914, 0.920, and 0.942, respectively. Among these three models, PSO showed the best performance with an AUC value of 0.942. The PSO approach is applicable for flood susceptibility mapping of the eastern part of India, a subtropical region, to allow flood mitigation and help to improve risk management in this region. [ABSTRACT FROM AUTHOR]

Among natural disasters, drought hits almost half of the world every year, regardless of the climatic zones. Identifying drought vulnerability regions is fundamental to plan and adopt mitigation measures. Here we apply a multi-criteria-based machine learning technique that integrates spatial data for preparing drought vulnerability map of different categories. We adopted remote sensing tools with three machine learning models namely support vector machine (SVM), random forest (RF) and support vector regression (SVR) and their ensembles (i.e. Bagging, Boosting and Stacking), as applied to the northwestern part of Iran as a case study. Various types of geo-environmental factors were considered including meteorological, hydrological, agricultural and socio-economic. The result of the model was evaluated through arithmetic logic values (area under the curve [AUC]) under the receiver operating curve (ROC). Through multi-collinearity test, the prominent causative factors for the occurrences of drought are defined. The AUC value from ROC of SVR-Stacking, RF-Stacking and SVM-Stacking model for training datasets are 0.942, 0.918 and 0.896, respectively. The SVR-Stacking yielded the best result (AUC = 0.94) confirming that SVR serves as a robust model for the preparation of drought susceptibility maps that can be used by governmental and other administrative agencies. [ABSTRACT FROM AUTHOR]

Pipeline pressure drops can be decreased by various methods. Utilizing small amounts of additives that named drag reducing agent (DRA) in pipelines with crude oil may reduce the friction caused by fluid. It highlights the importance of accurate approximation of drag reduction (DR). In this work, the performance of artificial neural network (ANN) in forecasting DR in crude oil pipelines was enhanced using artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms. To this end, we considered Reynolds number, concentration of DRA, type of DRA, temperature, and kind of pipe as the DR influential factors. Using 80:20 ratios for determining the training and testing data, each model performed with its optimal parameters. Three accuracy criteria of coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate their efficiency. Based on the results, applying ABC and PSO evolutionary algorithm leads to increasing R2 from 0.9487 to 0.9743 and 0.9806 in the training phase, and from 0.9584 to 0.9795 and 0.9835 in the testing phase. Moreover, a considerable decrease was observed for the RMSE (30% and 36%) and MAE (31% and 38%) in the testing stage. [ABSTRACT FROM AUTHOR]

Due to the rapid development of process manufacturing, composite materials with graphene-reinforcement have obtained commercially notices in promoted engineering applications. For this regard, vibrational characteristics of a cylindrical nanoshell reinforced by graphene nanoplatelets (GPL) and coupled with piezoelectric actuator (PIAC) is investigated. Also, the nanostructure is embedded in a viscoelastic medium. The material properties of piece-wise graphene-reinforced composite (GPLRC) are assumed to be graded in the thickness direction of a cylindrical nanoshell and estimated through a nanomechanical model. For the first time in the current study is considering the effects of piezoelectric layer, viscoelastic foundation, GPLRC, and size-effects on the frequency responses of the GPLRC cylindrical nanoshell coupled with PIAC and by assuming perfect bonding between the core (GPLRC cylindrical shell) and the piezoelectric layer. The governing equations and boundary conditions have been developed using minimum potential energy and solved with the aid of the generalized differential quadrature method. In addition, because of piezoelectric layer, Maxwell's equation is derived. The results show that viscoelastic foundation, piezoelectric layer, GPL distribution pattern, length scale parameter and GPL weight function have important role in the frequency characteristics of the GPLRC cylindrical nanoshell coupled with PIAC and surrounded by viscoelastic foundation. The results of the current study are useful suggestions for design of materials science, micro-electro-mechanical systems, and nanoelectromechanical systems such as nanoactuators and nanosensors. Communicated by Francesco Tornabene. [ABSTRACT FROM AUTHOR]

Land Use Land Cover (LULC) mapping has been used in different environmental applications including disaster management, risk analysis, heat island mapping, and the effects of urbanization on environmental changes such as floods and droughts in the recent decade. The earth's natural surface coverage including urban infrastructure, surface vegetation, bare soil can be identified with LULC maps. Besides, LULC is one of the most important tasks in natural hazards, planning activities, resource management, and global monitoring studies. The present study aimed to improve the level of land used and land cover change mapping in a semi-arid environment using Landsat 8 Operational Land Imager (OLI) image. In this research, several tree-based algorithms for LULC mapping are used and compared. These algorithms are used in a combination model implementation of WEKA 3.8 and R programming language to provide a method of a fit-for-purpose algorithms for LULC mapping. It is found that Reduced Error Pruning Tree (REP Tree) is the best approach according to the training and test datasets which are four classes including the build-up, soil, roads, and vegetation region pixels in a semi-arid environment. For the training dataset, the best values of Overall Accuracy (OA), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) is determined, 99.7313, 0.002 and 0.0354 as well as for the test dataset are 99.7313, 0.002 and 0.0354, respectively. Finally, trained models of the implemented data mining algorithms are used for the classification of a different dataset without using any training data where the Logical Analysis of Data Tree (LAD Tree) algorithm shows the best performance in terms of OA, MAE, and RMSE with values of 99.2815, 0.0057, and 0.0557 respectively. [ABSTRACT FROM AUTHOR]

This article addresses the spatial analysis of landslide susceptibility in the Ardabil province of Iran. To this end, two well-known optimization algorithms, namely genetic algorithm (GA) and particle swarm optimization (PSO) are synthesized with an adaptive neuro-fuzzy inference system (ANFIS) to create the ensembles of GA-ANFIS and PSO-ANFIS. Besides, the statistical index (SI) model is also performed to be compared with the mentioned intelligent techniques. Fourteen landslide conditioning factors including elevation, slope aspect, land use, plan curvature, profile curvature, soil type, distance to river, distance to road, distance to fault, rainfall, slope degree, stream power index (SPI), topographic wetness index (TWI), and lithology were considered within the geographic information system (GIS). Out of 253 identified landslides, 177 points (70% of them) were randomly selected and used for the training phase, and the remaining 76 points (30% of them) were used to evaluate the accuracy of the SI, GA-ANFIS, and PSO-ANFIS models. Referring to the calculated area under the receiver operating characteristic curve (AUROC) index, the GA-ANFIS (AUROC = 0.914) and SI (AUROC = 0.821) showed the best performance, respectively in the training and testing phases. Notably, ANFIS-PSO emerged as the faster prediction method compared to the GA-ANFIS. Also, from spatial analysis, it was revealed that around 95%, 87%, and 97% of the training landslides, and 96%, 84%, and 76% of the testing landslides are located in hazardous areas. [ABSTRACT FROM AUTHOR]

Due to the wide application of evolutionary science in different engineering problems, the main aim of this paper is to present two novel optimizations of multi-layer perceptron (MLP) neural network, namely dragonfly algorithm (DA) and biogeography-based optimization (BBO) for landslide susceptibility assessment at a study area, West of Iran. Utilizing 14 landslide conditioning factors, namely elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), and topographic wetness index (TWI) and rainfall as the input variables, and 208 historical landslides as target variable, the required spatial database is created. Then, the MLP is synthesized with the mentioned algorithms to develop the proposed DA-MLP and BBO-MLP ensembles. Three accuracy criteria of mean square error, mean absolute error, and area under the receiving operating characteristic curve are used to evaluate the performance of the models and also to develop a score-based ranking system. As the first outcome, the application of the DA and BBO metaheuristic algorithms enhances the accuracy of the MLP. Moreover, referring to the calculated total ranking scores of 6, 14, and 16, it was revealed that the BBO performs more efficiently than DA in optimizing the MLP. [ABSTRACT FROM AUTHOR]

Strong ground motions usually trigger lots of slope failures in the affected area. In this work, we analyse the occurrence likelihood of earthquake-triggered landslide by employing the ensembles of adaptive neuro-fuzzy inference systems (ANFIS) with four well-known metaheuristics techniques, namely particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), and differential evolution (DE) algorithms. Twelve landslide conditioning factors namely, elevation, slope degree, lithology, peak ground acceleration (PGA), stream power index (SPI), topographic wetness index (TWI), distance to road, distance to river, distance to fault, normalized difference vegetation index (NDVI), slope aspect, and plan curvature are considered within the geographic information system (GIS) to produce the required spatial database. In this paper, frequency ratio (FR) model is used to evaluate the spatial interaction between the landslides and conditioning factors. Meantime, among a total of 458 marked earthquake-induced landslides, 366 (80%) are specified to the learning process, and the remaining 92 (20%) landslides are used to evaluate the accuracy of applied models. The landslide susceptibility maps are generated in the GIS environment. Three accuracy criteria of mean square error (MSE), root mean square error (RMSE), and area under the receiving operating characteristic curve (AUROC) are used to develop a ranking system for comparing the integrity of the designed models. The total ranking scores (TRSs) of 15, 8, 10, and 18, respectively, obtained for PSO-ANFIS, GA-ANFIS, ACO-ANFIS, and DE-ANFIS revealed the superiority of the DE algorithm compared to other metaheuristics techniques. Also, the DE-ANFIS emerged as the fastest ensemble, due to the highest convergence speed obtained for this model. [ABSTRACT FROM AUTHOR]

The focal point of this study is to assess the efficacy of a state-of-the-art optimization technique namely, particle swarm optimization (PSO) for enhancing the performance of the artificial neural network (ANN) in modeling the seismic landslides at Ludian districts, China. Twelve geological and hydrological landslide-conditioning factors namely, elevation, lithology, slope degree, slope aspect, stream power index, peak ground acceleration, topographic wetness index, distance to river, distance to road, distance to fault, normalized difference vegetation index and plan curvature were considered within a geographic information system (GIS). After achieving the optimal structure of the multilayer perceptron neural network, the PSO algorithm was applied to improve its efficiency. The landslide susceptibility maps were generated in a GIS environment and area under the curve (AUC) criterion was used to assess the integrity of employed predictive models. The results showed that after applying the PSO algorithm, AUC experiences a significant increase from 0.765 to 0.825 in the validation phase. Moreover, respective AUCs of 0.812 and 0.828 obtained for the training phase of ANN and PSO-ANN reveal the efficiency of the proposed algorithm in improving the ANN accuracy. [ABSTRACT FROM AUTHOR]

As a prevalent disaster, landslides cause severe loss of property and human life worldwide. The specific objective of this study is to evaluate the capability of artificial neural network (ANN) synthesized with artificial bee colony (ABC) and particle swarm optimization (PSO) evolutionary algorithms, in order to draw the landslide susceptibility map (LSM) at Golestan province, Iran. The required spatial database was created from 12 landslide conditioning factors. The area under curve (AUC) criterion was used to assess the integrity of employed predictive approaches. In this regard, the calculated AUCs of 90.10%, 85.70%, 80.30% and 76.60%, respectively, for SI, PSO-ANN, ABC-ANN and ANN showed that all models have enough accuracy for simulating the LSM, although SI presents the best performance. The landslide vulnerability map obtained by PSO-ANN model is more accurate than other intelligent techniques. In addition, training the ANN with ABC and PSO optimization algorithms conduced to enhancing the reliability of this model. Note that, a total of 76.72%, 23.96%, 30.55% and 5.37% of the study area were labeled as perilous (High and Very high susceptibility classes), respectively by SI, PSO-ANN, ABC-ANN and ANN results. [ABSTRACT FROM AUTHOR]

Electroflotation is an unconventional separation process owing its name to the bubbles generation method (i.e., electrolysis of the aqueous medium) caused by the hydrogen and hydroxide development. Collecting the colloidal particles in surface water has been a long-term issue all over the world, because of their adverse influence. In the present research, the electroflocculation technique have been use to collect the humus particles throughout the polluted wastewater. The uses of different chemical additives namely, Al2(SO4)3, Al(OH)3, CaCl2, CaO, and Na2SiO3were also successfully examined and enhanced the electroflocculation technique. Although high negatively surface electrical charge of humus causes difficulties in the conventional flocculation and coagulation technique, it enhances the flocculation process when the electroflocculation techniques have been used. The results of both Jar test and electroflocculation technique are further compared. It can be concluded that the polluted water were fully decontaminated (i.e., all of the suspended colloids through the water were removed) by using the electroflocculation technique. The removal rate after electroflocculation test were 91.8%, 98%, 93.5%, 85.3%, 95.4%, and 94% for neutral, Al2(SO4)3, Al(OH)3, CaCl2, CaO, and Na2SiO3, respectively. While the removal rate after Jar test were 84.8%, 83.79%, 71.44%, 84.83%, 77.09%, and 77.09% for neutral, Al2(SO4)3, Al(OH)3, CaCl2, CaO, and Na2SiO3, respectively. [ABSTRACT FROM PUBLISHER]

The effect of CaO, Na2SiO3, Al(OH)3, Al2(SO4)3, and CaCl2on the surface charge and the diffuse double layer (DDL) thickness of a natural tropical peat were investigated in this study. Consequently, the flocculation and coagulation behavior of peat colloids were studied. As result, the thickness of DDL were changed significantly due to the addition of each electrolytes. The compression of the DDL thickness causes a weaker electrostatic repulsion and therefore a higher settlement rate. The surface charge of pure peat was almost zero at pH 3.2. The pH at the isoelectric point was increased by increasing the electrolyte concentration. The resulted flocculation and coagulation behavior of suspended peat in cationic reagents was in contrary with the surface electrical charge characteristics where the Na2SiO3and the Al2(SO4)3electrolytes showed the lowest and highest settlement rate, respectively. [ABSTRACT FROM PUBLISHER]

The surface electrical charge was measured for laboratory grade organic soil colloidal particles by varying pH between 2 and 11 and at 0.001, 0.01, 0.1, and 1.0 mol/L of cationic saline solution (CaCl2, Na2Sio3, Al2(SO4)3, MgCl2, and Na2CO3) concentrations. The zeta (ζ) potential values showed low sensitivity to pH in salt solutions. The ζ potential of pure organic soils varied from +7.5 mV at pH 2.8 to −28.5 mV at pH 11.3. The ζ potential calculation of humus particles in distilled water showed an increase in ζ potential value beyond pH > 3.3, which could be due to charge development at the humic substances by direct transfer of H+from organic soil to water. The surface electrical charge was almost zero at pH 3.25. Colloidal charge reversal was observed at several stages according to the electrolyte concentrations and chemical reagents. It was also found that using high molarities of multivalent saline solution (e.g., CaCl2, Na2Sio3, Na2CO3, and MgCl2) as electrolytes led to very high ζ potential values and consequently high electro-osmotic conductivity (Ke). On the basis of the results of this research, it is recommended that the ζ potential of the soils be determined before any application of electrokinetic (EK) treatment. [ABSTRACT FROM PUBLISHER]

The influence of the dispersion of pH and concentration of chemicals on zeta potential of kaolinite were investigated. Adding the cationic species led to an increase of the zeta potential, contrary to measuring done in water. The results proved that even a very low concentration of the cationic species (0.001 mol/L) causes a remarkable change of the zeta potential. The zeta potential of the kaolinite soils varied from 204.6 to −41.9 mV, according to the chemical reagents and electrolyte concentration. Moreover, the negative charge in kaolinite soils is highly pH dependent and surface charge of pure kaolinite is dropped to zero, (pHpzc) at pH 3.2–3.5. The greater electrolyte concentration resulted in the thicker diffuse double layer and higher pH at the iso-electric point. While, for some reagents there was no isoelectric point that to be observed, increase the pH at isoelectric point and Na2CO3 as well as CH3COONa led to decrease in pH at iso electric point of suspension kaolinite. Results revealed that presence of low molecular weight CaCl2, , H3PO4, Na2Sio2, CH3COONa, and Na2CO3 led to a increase in diffuse double layer thickness in order of CaCl2, , Na2CO3 and H3PO4, and Na2SiO4, and electrolyte concentration has important effect on such incrasing. Based on results of ζ and pH observed from using different cationic species, those pHpzc which are more close to the soil pH (ζ is dropped to zero) are more succeptible to be chosen as best reagent to stabilizing soil. [ABSTRACT FROM PUBLISHER]