Projection pursuit evaluation model of a regional surface water environment based on an Ameliorative Moth-Flame Optimization algorithm

Liu, Dong; Zhang, Guodong; Li, Heng; Fu, Qiang; Li, Mo; Faiz, Muhammad Abrar; Ali, Shoaib; Li, Tianxiao; Khan, Muhammad Imran

doi:10.1016/j.ecolind.2019.105674

Cited by 32 publications

(14 citation statements)

References 31 publications

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“…Commonly utilized methods include the fuzzy artificial neural network, health risk assessment methods. These methods simplify the problem-solving to a certain extent, but some shortcomings still exist: the parameters of the fuzzy artificial neural network method are difficult to set; the convergence speed is slow; the stability is poor; it is easy to succumb to local minimization; and the resolution is low (Liu et al 2019b). The health risk assessment method considers only the harm of pollutants to the human body as the measurement standard (Liu et al 2018).…”

Section: Introductionmentioning

confidence: 99%

“…The projection pursuit evaluation model is completely data-driven and avoids the interference of subjective factors. This model projects high-dimensional data into low-dimensional space and analyzes the characteristics of high-dimensional data according to the projected eigenvalues, thereby reflecting the structure and characteristics of high-dimensional data in low-dimensional space to solve the highdimensional problem (Liu et al 2019b).…”

Section: Introductionmentioning

confidence: 99%

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Assessment of karst water quality and analysis of pollution sources with a projection pursuit algorithm in Jinan spring area, China

Xing

et al. 2022

Water Practice and Technology

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Karst water is one of the main drinking water sources in North China. The single factor method and projection pursuit algorithm (PPA) are employed to assess the karst water quality of the Baotu spring area in Jinan. The water quality distribution pattern, its causes, and the main groundwater pollution sources are analyzed. The water quality evaluation results of the PPA model are more reliable than those of the single factor method because the PPA model comprehensively considers the weight and correlation of various factors. The water quality of the study area is generally excellent, but the NO3− index content is high. In recent years, the water quality grades have been mainly class II ∼ class IV. The driving factors of water quality evolution are not only human activities, including artificial recharge, but also natural factors, such as carbonate mineral dissolution. These factors control both the distribution and evolution trend of water quality. Urban nonpoint sources have a significant impact on groundwater quality. Based on the current water quality situation, it is urgent to strengthen protection of the ecological environment in the southern recharge area of the spring region and the water quality control in the western region.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of karst water quality and analysis of pollution sources with a projection pursuit algorithm in Jinan spring area, China

Xing

et al. 2022

Water Practice and Technology

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“…Industrial and agricultural areas are well developed in the region, and immigration is high. Under the pressure of intensive urbanization, along with water requirements and competition between users (agriculture, industry, households), the problem of water quality has become multidimensional and critical, especially during scarcity [27]. Therefore, in order to solve the conflicting interests among users, to explore the sources of river pollutants in traditional agricultural areas and enhance agricultural production efficiency, this study combined the fuzzy matter-element model with the VIKOR model.…”

Section: Introductionmentioning

confidence: 99%

Water Quality Characteristics and Source Analysis of Pollutants in the Maotiao River Basin (SW China)

Jiao

et al. 2022

Water

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Rivers are an important mediator between human activities and the natural environment. They provide multiple functions, including irrigation, transportation, food supply, recreation, and water supply. Therefore, evaluations of water quality and pollution sources are of great significance for ecological restoration and management of rivers. In this study, the improved “vušekriterijumska optimizacija i kompromisno rješenje” (VIKOR in Serbian; in English: Multicriteria Optimization and Compromise Solution), and a geodetector were used to analyze the water quality characteristics and pollution sources of the Maotiao River Basin (Gizhou province, SW China). The results showed that the water quality of the Maotiao River Basin deteriorated significantly during the summer drought period, as was evident in the reservoirs and lakes. It improved in the wet season (i.e., during the summer period) due to runoff dilution. Water quality decreased along the river’s course, from upstream to downstream sections. The results of the geographic detector analysis showed that agricultural areas were the primary factor affecting the spatial distribution of water quality in the river basin. In July, August, and November 2020, the influence of agricultural land was 0.72, 0.60, or 0.80, respectively, and the interactions among urban, industrial, agricultural, and forested areas explained 99.2%, 83.2%, or 99.9% of the spatial differentiation of water quality, respectively. Due to the influence of spatial scale, settlements have a small influence on the spatial distribution of water quality. Their impact factors were 0.38, −0.24, and −0.05, respectively. Notably, the negative relationship of water quality and forested areas reflects that topography, types of landscapes, and soil thickness have considerable influences on the Maotiao River Basin’s water quality. Based on the findings, we infer that good farmland water conservancy projects and comprehensive management of different types of landscapes, such as forests, agriculture, and urban area and water bodies, are of great significance for improving water quality.

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“…Some evolutionary algorithms and intelligent swarm methods were introduced to handle this problem and have achieved specific results, but they rarely consider the multiple uncertainties of evaluation indicators. The main techniques include the differential evolution method [10], particle swarm optimization method [11], shuffled frog leaping algorithm [12], moth-flame optimization method [13], grey wolf optimization method [14], real coding based accelerating genetic algorithm (RAGA) [15] and a whale optimization strategy based on the Gaussian cloud model [16]. Nevertheless, the fruit fly optimization algorithm (FOA) [17] raised recently provides a new way to find the best PDV for the PP evaluation method, since it can conduct global information exchange and local deep search.…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Projection Pursuit Method Based on a Connection Cloud Model and Set Pair Analysis for Evaluation of Slope Stability

Wang

Shen

et al. 2021

Symmetry

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Determining the projection direction vector (PDV) is essential to the projection pursuit evaluation method for high-dimensional problems under multiple uncertainties. Although the PP method using a cloud model can facilitate interpretation of the fuzziness and randomness of the PDV, it ignores the asymmetry of the PDV and the fact that indicators are actually distributed over finite intervals; it quickly falls into premature defects. Therefore, a novel PP evaluation method based on the connection cloud model (CCM) is discussed to remedy these drawbacks. In this approach, adaptive numerical characteristics of the CCM are adopted to represent the randomness and fuzziness of the candidate PDV and evaluation indicators. Meanwhile, to avoid complex computing and to accelerate the convergence speed of the optimization procedure, an improved fruit fly optimization algorithm (FOA) is set up to find the rational PDV. Alternatively, candidate PDVs are mutated based on the mechanism “pick the best of the best” using set pair analysis (SPA) and chaos theory. Furthermore, the applicability and reliability are discussed based on an illustrative example of slope stability evaluation and comparisons with the neural network method and the PP evaluation method based on the other FOAs and the genetic algorithm. Results indicate that the proposed method with simpler code and quicker convergence speed has good global ergodicity and local searching capabilities, and can better explore the structure of high-dimensional data with multiple uncertainties and asymmetry of the PDV relative to other methods.

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Projection pursuit evaluation model of a regional surface water environment based on an Ameliorative Moth-Flame Optimization algorithm

Cited by 32 publications

References 31 publications

Assessment of karst water quality and analysis of pollution sources with a projection pursuit algorithm in Jinan spring area, China

Assessment of karst water quality and analysis of pollution sources with a projection pursuit algorithm in Jinan spring area, China

Water Quality Characteristics and Source Analysis of Pollutants in the Maotiao River Basin (SW China)

A Heuristic Projection Pursuit Method Based on a Connection Cloud Model and Set Pair Analysis for Evaluation of Slope Stability

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