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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]

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]

• Indirect measurement of driven piles capacity is presented in this study. • Developing an FA-ANFIS model for prediction of driven piles capacity. • Results signify effectiveness of FA-ANFIS to predict driven piles capacity. Parametric studies of driven piles response during dynamic and static load experiments, especially in the field, are generally too costly to measure. This study constructs and verifies a computational intelligence technique for predicting the bearing capacity of driven piles. The proposed technique is a hybridization of adaptive neuro-fuzzy inference system (ANFIS) and firefly algorithm (FA). It is worth mentioning that this is the first work that explores the ability of FA-ANFIS in the field of piles capacity prediction. In modeling, different FA-ANFIS and ANFIS models were constructed based on different combination of input parameters, and finally, the performances of all models were compared using statistical functions. The results demonstrated that the proposed FA-ANFIS model with coefficient of determination (R2) of 0.997 was more effective than the ANFIS in predicting the pile capacity, and thereby the effectiveness of FA to optimize ANFIS was proved. [ABSTRACT FROM AUTHOR]

Forests are important dynamic systems which are widely affected by fire worldwide. Due to the complexity and non-linearity of the forest fire problem, employing hybrid evolutionary algorithms is a logical task to achieve a reliable approximation of this environmental threat. Three fuzzy-metaheuristic ensembles, based on adaptive neuro-fuzzy inference systems (ANFIS) incorporated with genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) evolutionary algorithms are used to produce the forest fire susceptibility map (FFSM) of a fire-prone region in Iran. A sensitivity analysis is also executed to evaluate the effectiveness of the proposed ensembles in terms of time and complexity. The results revealed that all models produce FFSMs with acceptable accuracy. However, the superiority of the GA-ANFIS was shown in both recognizing the pattern (AUROC train = 0.912 and Error = 0.1277) and predicting unseen fire events (AUROC test = 0.850 and Error = 0.1638). The optimized structures of the proposed GA-ANFIS and PSO-ANFIS ensembles could be good alternatives to traditional forest fire predictive models, and their FFSMs can be promisingly used for future planning and decision making in the proposed area. • Three Fuzzy-Metaheuristic Ensembles proposed for Spatial Forest Fire Susceptibility Assessment. • The superiority of the GA-ANFIS was proved. • Pattern recognition, as well as unseen fire event predictions, are assessed. [ABSTRACT FROM AUTHOR]