An alternative aggregate preference ranking algorithm to assess environmental effects on macrobenthic abundance in coastal water

TL;DR

This paper introduces a novel aggregate preference ranking algorithm combining ecological assessment of macrobenthic abundance with a mathematical DEA-based approach to evaluate environmental effects on coastal water ecosystems.

Contribution

It presents a new aggregation algorithm that integrates ecological data with a modified DEA model for environmental assessment of coastal waters.

Findings

Identified key water quality parameters affecting macrobenthic abundance.

Applied the algorithm to coastal samples revealing spatial variations.

Demonstrated the effectiveness of the ranking method in environmental evaluation.

Abstract

Coastal marine waters are ranked among the most important aquatic ecosystems on earth in terms of ecological and economic significance. Since the Industrial Revolution, human activities have drastically changed coastal marine ecosystems. The development of rules and regulations to protect these ecosystems against human activities requires availability of environmental assessment standard. This necessitates the identification of the key parameters that reflect condition of the coastal water ecosystem. Macrobenthic assemblages are recognized to rapidly respond to changes in the quality of water or habitat. Therefore, it would be useful to study the population of macrobenthos and assess the influential factors on the growth of this species. This study is categorized as multidisciplinary approach which contains two perspectives, ecological and mathematical. In the ecological section, the effect of the water quality parameters (e.g. pH, temperature, dissolved oxygen and salinity) and the sediment characteristics on the macrobenthic abundance is studied. A total of 432 samples were collected and analyzed from four touristic costal locations (at various distances form the coast) of Penang National Park to investigate the spatial change of macrobenthic assemblage. In a mathematical perspective, this paper pursues a new algorithm based on the performance evaluation methods. For this purpose, first, Data Envelopment Analysis (DEA) is employed to evaluate a group of Decision Making Units (DMUs). Consequently, inputs of the mentioned DMUs are considered as alternatives (or candidates), and using a modified DEA model that is categorized as aggregating preference ranking method, the influence of inputs in efficiency of DMUs is investigated.