Water, soil and air pollutants' interaction on mangrove ecosystem and corresponding artificial intelligence techniques used in decision support systems : A review

• Main Authors: Wong, Wen Yee, Al-Ani, Ayman Khallel Ibrahim, Hasikin, Khairunnisa, Khairuddin, Anis Salwa Mohd, Razak, Sarah Abdul, Hizaddin, Hanee Farzana, Mokhtar, Mohd Istajib, Azizan, Muhammad Mokhzaini
• Format: Article
• Published: Institute of Electrical and Electronics Engineers 2021
• Subjects: QA Mathematics; QA75 Electronic computers. Computer science; QK Botany; SD Forestry; TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.um.edu.my/34126/

The feasibility of artificial intelligence (AI) as a predictive model for thorough efficacy analysis on environmental pollution applied on mangrove forests are discussed. Mangrove forests are among the most productive and biological diverse ecosystems on the planet. However, due to environmental pollution and climate change, mangrove forests are in serious decline. Despite crucial issues pertaining mangrove forests, the law enforcement on the ecosystem is still dubious due to the lack of evidence and data that could provide accurate analysis and prediction. The main highlight of this review elaborates on pollutant markers in soil, water, and air, by correlating these three aspects to the sustainability of mangrove ecosystem. The research gap identified from this review suggests the application of an integrated environmental prediction system for practical environmental insights. A predictive model for environmental decision-making could be developed by integrating meteorological, climatological, hydrological, atmospheric, and heavy metal concentration to understand the interaction between each factor for an efficient solution of pollutant reduction scheme involving mangrove ecosystems.