SGOA: annealing-behaved grasshopper optimizer for global tasks

Yu, Caiyang; Chen, Mengxiang; Cheng, Kai; Zhao, Xuehua; Ma, Chao; Kuang, Fangjun; Chen, Huiling

doi:10.1007/s00366-020-01234-1

Cited by 102 publications

(46 citation statements)

References 130 publications

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“…With increase in remote sensing and geography information system techniques (Zuo et al 2015(Zuo et al , 2017Jiang et al 2018;Zhou et al 2020;Zhao et al, 2020;Yu et al, 2020), qualitative and quantitative methods can be used to evaluate LS (Ayalew and Yamagishi 2005). Qualitative methods require two data types (Xu et al 2019;Wang et al 2020;Zhang et al 2020;Yu et al 2021;Hu et al 2021): a landslides inventory and heuristic datasets (Aleotti and Chowdhury 1999). The landslide inventory map (LIM) is the primary survey of landslides.…”

Section: Introductionmentioning

confidence: 99%

Spatial prediction of shallow landslide: application of novel rotational forest-based reduced error pruning tree

Arabameri

Santosh²,

Saha

et al. 2021

Geomatics, Natural Hazards and Risk

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Landslides are a form of soil erosion threatening the sustainability of some areas of the world. There is, therefore, a need to investigate landslide rates and behaviour. In this research, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a meta classifier based on reduced error pruning tree (REPTree) as a base classifier called RF-REPTree, for landslide susceptibility mapping (LSM) in the Kalaleh watershed, Golestan Province, Iran. Some benchmark models, including the opensource Java decision tree (J48), naive Bayes tree (NBTree), and REPTree were used to compare the designed model. A total of 249 landslide locations were identified and mapped. The group was split into training (70%) and testing (30%) data for modelling and reliability analysis. Based on a literature review and multi-collinearity tests, 16 landslide conditioning factors (LCFs) were selected. Of the LCFs, the topographical position index (TPI) had the highest correlation with landslide occurrence. The LSM produced by RF-REPTree revealed that nearly 29% of the study areas have high to very high landslide susceptibility (LS). Statistical analysis of the model results included the receiver operating characteristic curve (ROC), the efficiency test, the true skill statistic (TSS), and the kappa index. ROC demonstrated that the AUC values of RF-REPTree, REPTree, J48, and NBTree models were 0.832, 0.700, 0.695, and 0.759 for succession rate curves and 0.794, 0.740, 0.788, and 0.728 for prediction rate curves, respectively. Therefore, ARTICLE HISTORY

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Section: Introductionmentioning

confidence: 99%

Spatial prediction of shallow landslide: application of novel rotational forest-based reduced error pruning tree

Arabameri

Santosh²,

Saha

et al. 2021

Geomatics, Natural Hazards and Risk

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“…Collecting and preprocessing of data is a very crucial step in any statistical or analytical analysis [47][48][49][50][51][52].…”

Section: Methodsmentioning

confidence: 99%

Analysis and Measurement of Parameters of Quality of Life in Informal Settlements Surrounding of Tehran Metropolis

Ghaderpour

Taleshmekaiil

Rouki

et al. 2021

Mathematical Problems in Engineering

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Quality of life is a concept used to describe the development of the welfare of society, and improving the quality of life is one of the most important goals of governments around the world. Informal settlements, as one of the most vulnerable urban areas, are always in unfavorable conditions in terms of various social, economic, and physical indicators. Therefore, the main purpose of this study is to investigate the status of various quality of life indicators in informal settlements around the metropolis of Tehran in Iran. This research is descriptive, analytical, and correlational based on primary data in which the initial data are collected through the documentary and survey (field) method using a questionnaire. The collected data were processed by SPSS and SmartPLS software. Based on the results of the t-test, quality of life indices in informal settlements around the metropolis of Tehran, on a Likert scale with an average of 23.2, is at a low level. Also, the results of the PSL path modelling and multivariate regression test show that increasing the quality of social, economic, physical, service, and access indicators, in terms of residents, has the greatest impact on improving the quality of life of this urban fabric and security index. It has the least impact. There is also a significant relationship between indicators, and the mental perspective of citizens changes under social, service, economic, and physical conditions.

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“…Historical and real-time data of Islamabad was used to design an energy management system strategy for said goals. e proposed model is solved using power algorithm in MATLAB, which is popular software in optimization [34][35][36][37][38][39][40][41][42][43][44] and machine learning [45][46][47][48][49][50] literature as well. Waqar et al [51] presented a microgrid model for Pakistani environment.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Operation of the Campus Microgrid considering the Resource Uncertainty and Demand Response Schemes

Muqeet

Munir

Ahmad

et al. 2021

Mathematical Problems in Engineering

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Present power systems face problems such as rising energy charges and greenhouse gas (GHG) releases. These problems may be assuaged by participating distributed generators (DGs) and demand response (DR) policies in the distribution system (DS). The main focus of this paper is to propose an energy management system (EMS) approach for campus microgrid (µG). For this purpose, a Pakistani university has been investigated and an optimal solution has been proposed. Conventionally, it contains electricity from the national grid only as a supply to fulfil the energy demand. Under the proposed setup, it contains campus owned nondispatchable DGs such as solar photovoltaic (PV) panels and microturbines (MTs) as dispatchable sources. To overcome the random nature of solar irradiance, station battery has been integrated as energy storage. The subsequent nonlinear mathematical problem has been scheduled by mixed-integer nonlinear programming (MINLP) in MATLAB for saving energy cost and battery aging cost. The framework has been validated under deterministic and stochastic environments. Among random parameters, solar irradiance and load have been taken into consideration. Case studies have been carried out considering the demand response strategies to analyze the proposed model. The obtained results show that optimal management and scheduling of storage in the presence of DGs mutually benefit by minimizing consumption cost (customer) and grid load (utility) which show the efficacy of the proposed model. The results obtained are compared to the existing literature and a significant cost reduction is found.

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SGOA: annealing-behaved grasshopper optimizer for global tasks

Cited by 102 publications

References 130 publications

Spatial prediction of shallow landslide: application of novel rotational forest-based reduced error pruning tree

Spatial prediction of shallow landslide: application of novel rotational forest-based reduced error pruning tree

Analysis and Measurement of Parameters of Quality of Life in Informal Settlements Surrounding of Tehran Metropolis

Optimal Operation of the Campus Microgrid considering the Resource Uncertainty and Demand Response Schemes

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