Assessment of liquefaction-induced lateral spread using soft computing approaches

Chen, Zhixiong; Chen, Yuhui; Zhang, Yanmei; Xiao, Peng; Samui, Pijush

doi:10.1016/j.gr.2022.08.006

Cited by 9 publications

(1 citation statement)

References 54 publications

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“…For the evaluation of the effectiveness of the computational models there have been proposed a multitude of statistical mainly indices, and among them main position beacause of wide acceptance [100][101][102][103][104][105][106][107][108][109][110][111][112][113][114], have indices that are presented in Table 3. Dominant position as the Pearson among them has the Pearson correlation coefficient.…”

Section: Performance Indices and Principles For The Assessement Of So...mentioning

confidence: 99%

Application of Bio and Nature-Inspired Algorithms in Agricultural Engineering

Maraveas

Arvanitis

Bartzanas

et al. 2022

Arch Computat Methods Eng

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The article reviewed the four major Bioinspired intelligent algorithms for agricultural applications, namely ecological, swarm-intelligence-based, ecology-based, and multi-objective algorithms. The key emphasis was placed on the variants of the swarm intelligence algorithms, namely the artificial bee colony (ABC), genetic algorithm, flower pollination algorithm (FPA), particle swarm, the ant colony, firefly algorithm, artificial fish swarm, and Krill herd algorithm because they had been widely employed in the agricultural sector. There was a broad consensus among scholars that certain BIAs' variants were more effective than others. For example, the Ant Colony Optimization Algorithm and genetic algorithm were best suited for farm machinery path optimization and pest detection, among other applications. On the contrary, the particle swarm algorithm was useful in determining the plant evapotranspiration rates, which predicted the water requirements and optimization of the irrigation process. Despite the promising applications, the adoption of hyper-heuristic algorithms in agriculture remained low. No universal algorithm could perform multiple functions in farms; different algorithms were designed to perform specific functions. Secondary concerns relate to data integrity and cyber security, considering the history of cyber-attacks on smart farms. Despite the concerns, the benefits associated with the BIAs outweighed the risks. On average, farmers can save 647–1866 L on fuel which is equivalent to US$734-851, with the use of GPS-guided systems. The accuracy of the BIAs mitigated the risk of errors in applying pesticides, fertilizers, irrigation, and crop monitoring for better yields.

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Section: Performance Indices and Principles For The Assessement Of So...mentioning

confidence: 99%

Application of Bio and Nature-Inspired Algorithms in Agricultural Engineering

Maraveas

Arvanitis

Bartzanas

et al. 2022

Arch Computat Methods Eng

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Modelling biochemical oxygen demand using improved neuro-fuzzy approach by marine predators algorithm

Adnan,

Dai,

Kisi

et al. 2023

Environ Sci Pollut Res

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Biochemical oxygen demand (BOD) is one of the most important parameters used for water quality assessment. Alternative methods are essential for accurately prediction of this parameter because the traditional method in predicting the BOD is time-consuming and it is inaccurate due to inconstancies in microbial multiplicity. In this study, the applicability of four hybrid neuro-fuzzy (ANFIS) methods, ANFIS with genetic algorithm (GA), ANFIS with particle swarm optimization (PSO), ANFIS with sine cosine algorithm (SCA), and ANFIS with marine predators algorithm (MPA), was investigated in predicting BOD using distinct input combinations such as potential of hydrogen (pH), dissolved oxygen (DO), electrical conductivity (EC), water temperature (WT), suspended solids (SS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (T-P) acquired from two river stations, Gongreung and Gyeongan, South Korea. The applicability of multi-variate adaptive regression spline (MARS) in determination of the best input combination was examined. The ANFIS-MPA was found to be the best model with the lowest root mean square error and mean absolute error and the highest determination coefficient. It improved the root mean square error of ANFIS-PSO, ANFIS-GA, and ANFIS-SCA models by 13.8%, 12.1%, and 6.3% for Gongreung Station and by 33%, 25%, and 6.3% for Gyeongan Station in the test stage, respectively.

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