A novel evolutionary combination of artificial intelligence algorithm and machine learning for landslide susceptibility mapping in the west of Iran.

Detecting and mapping landslides are crucial for effective risk management and planning. With the great progress achieved in applying optimized and hybrid methods, it is necessary to use them to increase the accuracy of landslide susceptibility maps. Therefore, this research aims to compare the accuracy of the novel evolutionary methods of landslide susceptibility mapping.

A new combined approach of neural-metaheuristic algorithms for predicting and appraisal of landslide susceptibility mapping.

In this research, to predict landslide susceptibility mapping (LSM), we have studied and optimized an artificial neural network (ANN) by utilizing the backtracking search algorithm (BSA) as well as the Cuckoo optimization algorithm (COA). Multiple research studies have shown that ANN-based techniques can be used to figure out the LSM. Still, ANN computing models have big problems, like slow system learning and getting stuck in their local minimums.

Modeling the susceptibility of an uneven-aged broad-leaved forest to snowstorm damage using spatially explicit machine learning.

Snowstorms are disturbance agents that have received relatively little research attention rather than significant disturbances that they pose to forest ecosystems. In this study, we modeled the interactions between snowstorms and different characteristics of a forest stand in northern Iran and spatially visualized the susceptibility of the stand to damage caused by snowstorms using the random forest (RF) and logistic regression (LR) methods. After a severe snowstorm in November 2021 that caused stem breakage and uprooting of individual trees, the location of 185 damaged trees was identified via field surveys and used for generating an inventory map of snowstorm damage.

Predicting the sorption efficiency of heavy metal based on the biochar characteristics, metal sources, and environmental conditions using various novel hybrid machine learning models.

Heavy metals in water and wastewater are taken into account as one of the most hazardous environmental issues that significantly impact human health. The use of biochar systems with different materials helped significantly remove heavy metals in the water, especially wastewater treatment systems. Nevertheless, heavy metal's sorption efficiency on the biochar systems is highly dependent on the biochar characteristics, metal sources, and environmental conditions.

Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques.

Four state-of-the-art metaheuristic algorithms including the genetic algorithm (GA), particle swarm optimization (PSO), differential evolutionary (DE), and ant colony optimization (ACO) are applied to an adaptive neuro-fuzzy inference system (ANFIS) for spatial prediction of landslide susceptibility in Qazvin Province (Iran). To this end, the landslide inventory map, composed of 199 identified landslides, is divided into training and testing landslides with a 70:30 ratio. To create the spatial database, thirteen landslide conditioning factors are considered within the geographic information system (GIS).

The impact of physical exercise on the fatigue symptoms in patients with multiple sclerosis: a systematic review and meta-analysis.

Background: Despite many benefits of the physical activity on physical and mental health of patients with Multiple Sclerosis (MS), the activity level in these patients is still very limited, and they continue to suffer from impairment in functioning ability. The main aim of this study is thus to closely examine exercise's effect on fatigue of patients with MS worldwide, with particular interest on Iran based on a comprehensive systematic review and meta-analysis.

Methods: The studies used in this systematic review were selected from the articles published from 1996 to 2019, in national and international databases including SID, Magiran, Iranmedex, Irandoc, Google Scholar, Cochrane, Embase, ScienceDirect, Scopus, PubMed and Web of Science (ISI).

Fuzzy-metaheuristic ensembles for spatial assessment of forest fire susceptibility.

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.

Ultrasound-accelerated synthesis of uniform SrMnO nanoparticles as water-oxidizing catalysts for water splitting systems.

One of the major issue in the 21st century is the humans request to green energy. The best form of green, sustainable and safe energy is hydrogen source due to its ecological and economical aspects. Herein, In order to obtain a highly water-oxidizing catalysts for water splitting systems, the sonochemical procedure applied for fabrication of practical SrMnO nanoparticles.

Spatial Landslide Susceptibility Assessment Based on Novel Neural-Metaheuristic Geographic Information System Based Ensembles.

Regular optimization techniques have been widely used in landslide-related problems. This paper outlines two novel optimizations of artificial neural network (ANN) using grey wolf optimization (GWO) and biogeography-based optimization (BBO) metaheuristic algorithms in the Ardabil province, Iran. To this end, these algorithms are synthesized with a multi-layer perceptron (MLP) neural network for optimizing its computational parameters.

Slope Stability Monitoring Using Novel Remote Sensing Based Fuzzy Logic.

By the assist of remotely sensed data, this study examines the viability of slope stability monitoring using two novel conventional models. The proposed models are considered to be the combination of neuro-fuzzy (NF) system along with invasive weed optimization (IWO) and elephant herding optimization (EHO) evolutionary techniques. Considering the conditioning factors of land use, lithology, soil type, rainfall, distance to the road, distance to the river, slope degree, elevation, slope aspect, profile curvature, plan curvature, stream power index (SPI), and topographic wetness index (TWI), it is aimed to achieve a reliable approximation of landslide occurrence likelihood for unseen environmental conditions.

Prediction of Pullout Behavior of Belled Piles through Various Machine Learning Modelling Techniques.

The main goal of this study is to estimate the pullout forces by developing various modelling technique like feedforward neural network (FFNN), radial basis functions neural networks (RBNN), general regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS). A hybrid learning algorithm, including a back-propagation and least square estimation, is utilized to train ANFIS in MATLAB (software). Accordingly, 432 samples have been applied, through which 300 samples have been considered as training dataset with 132 ones for testing dataset.

Sonochemical synthesis of PrMoO nanostructures as an effective photocatalyst for waste-water treatment.

Synthesis of pure PrMoO nanoparticles was the aim of the present work, which was prepared by sonochemical method which is a controllable rout on size, purity, and morphology of products. The experiments were carried out under a probe as sonication source, and its power was adjusted in 30 W (9 kHz), 50 W (15 kHz), and 80 W (24 kHz) for different samples. The optimum product with the smallest size and highest purity was synthesized by changing time, power of sonication, solvent and capping agent.

A Novel Swarm Intelligence-Harris Hawks Optimization for Spatial Assessment of Landslide Susceptibility.

In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors-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), topographic wetness index (TWI), and rainfall-is prepared to develop the ANN and HHO-ANN predictive tools.

Ultrasound-accelerated synthesis of uniform DyVO nanoparticles as high activity visible-light-driven photocatalyst.

In order to obtain a highly efficient photocatalyst for water treatment, the sonochemical procedure applied to fabrication of excellent DyVO nanoparticles. A comparative study between two different synthesis routes (precipitation and sonochemical) was investigated in this work. Also, the influence of anionic, cationic and nonionic surfactant was studied on the formation of uniform particles.

S,N co-doped graphene quantum dots-induced ascorbic acid fluorescent sensor: Design, characterization and performance.

In this work, new detection route for ascorbic acid was designed. First, highly luminescent sulfur and nitrogen doped graphene quantum dots (S,N-GQDs) were prepared via simple hydrothermal method using citric acid and thiourea as the C, N and S sources respectively. The prepared S,N-GQDs are characterized by XRD, HRTEM, FTIR, EDS and PL.

A review of combinations of electrokinetic applications.

Anthropogenic activities contaminate many lands and underground waters with dangerous materials. Although polluted soils occupy small parts of the land, the risk they pose to plants, animals, humans, and groundwater is too high. Remediation technologies have been used for many years in order to mitigate pollution or remove pollutants from soils.

Investigating the effect of lignosulfonate on erosion rate of the embankments constructed with clayey sand.

Internal erosion is known as the most important cause of dam failure after overtopping. It is important to improve the erosion resistance of the erodible soil by selecting an effective technique along with the reasonable costs. To prevent internal erosion of embankment dams the use of chemical stabilizers that reduce the soil erodibility potential is highly recommended.