Robust outlier detection in geo-spatial data based on LOLIMOT and KNN search

Tabatabaei, Mohammadreza; Kimiaefar, Roohollah; Hajian, Alireza; Akbari, Alireza

doi:10.1007/s12145-021-00610-9

Cited by 7 publications

(2 citation statements)

References 20 publications

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“…However, the major impediment lies in the constraints to axis-orthogonal splits which degrade the efficacy of these algorithms at higher dimensions. Nonetheless, recent applications of the LOLIMOT algorithm can be found in [81][82][83][84][85].…”

Section: Heuristic Tree-based Partitioningmentioning

confidence: 99%

Enhancement of the HILOMOT Algorithm with Modified EM and Modified PSO Algorithms for Nonlinear Systems Identification

Mahfuz

Muyeen

2022

Electronics

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Developing a mathematical model has become an inevitable need in studies of all disciplines. With advancements in technology, there is an emerging need to develop complex mathematical models. System identification is a popular way of constructing mathematical models of highly complex processes when an analytical model is not feasible. One of the many model architectures of system identification is to utilize a Local Model Network (LMN). Hierarchical Local Model Tree (HILOMOT) is an iterative LMN training algorithm that uses the axis-oblique split method to divide the input space hierarchically. The split positions of the local models directly influence the accuracy of the entire model. However, finding the best split positions of the local models presents a nonlinear optimization problem. This paper presents an optimized HILOMOT algorithm with enhanced Expectation–Maximization (EM) and Particle Swarm Optimization (PSO) algorithms which includes the normalization parameter and utilizes the reduced-parameter vector. Finally, the performance of the improved HILOMOT algorithm is compared with the existing algorithm by modeling the NOx emission model of a gas turbine and multiple nonlinear test functions of different orders and structures.

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Section: Heuristic Tree-based Partitioningmentioning

confidence: 99%

Enhancement of the HILOMOT Algorithm with Modified EM and Modified PSO Algorithms for Nonlinear Systems Identification

Mahfuz

Muyeen

2022

Electronics

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“…One novel approach combines the local linear model tree (LOLIMOT) network with a k-nearest neighborhood (kNN) search. This method selects data pairs through decile analysis based on distances calculated during kNN data grouping [41]. The positive aspect of this approach lies in its robust performance, demonstrated in both synthetic 3D datasets and real-world micro-gravimetric data and earthquake catalogues.…”

Section: Introductionmentioning

confidence: 99%

Digital Visualization of Environmental Risk Indicators in the Territory of the Urban Industrial Zone

Safarov,

Shomanova,

Nossenko

et al. 2024

Sustainability

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This study focused on predicting the spatial distribution of environmental risk indicators using mathematical modeling methods including machine learning. The northern industrial zone of Pavlodar City in Kazakhstan was used as a model territory for the case. Nine models based on the methods kNN, gradient boosting, artificial neural networks, Kriging, and multilevel b-spline interpolation were employed to analyze pollution data and assess their effectiveness in predicting pollution levels. Each model tackled the problem as a regression task, aiming to estimate the pollution load index (PLI) values for specific locations. It was revealed that the maximum PLI values were mainly located to the southwest of the TPPs over some distance from their territories according to the average wind rose for Pavlodar City. Another area of high PLI was located in the northern part of the studied region, near the Hg-accumulating ponds. The high PLI level is generally attributed to the high concentration of Hg. Each studied method of interpolation can be used for spatial distribution analysis; however, a comparison with the scientific literature revealed that Kriging and MLBS interpolation can be used without extra calculations to produce non-linear, empirically consistent, and smooth maps.

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Performance enhancement of deep neural network using fusional data assimilation and divide-and-conquer approach; case study: earthquake magnitude calculation

Esmaeili,

Kimiaefar,

Hajian

et al. 2024

Neural Comput & Applic

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Robust outlier detection in geo-spatial data based on LOLIMOT and KNN search

Cited by 7 publications

References 20 publications

Enhancement of the HILOMOT Algorithm with Modified EM and Modified PSO Algorithms for Nonlinear Systems Identification

Enhancement of the HILOMOT Algorithm with Modified EM and Modified PSO Algorithms for Nonlinear Systems Identification

Digital Visualization of Environmental Risk Indicators in the Territory of the Urban Industrial Zone

Performance enhancement of deep neural network using fusional data assimilation and divide-and-conquer approach; case study: earthquake magnitude calculation

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