Predicting Coastal Dissolved Inorganic Nitrogen Levels by Applying Data-Driven Modelling: The Case Study of Cyprus (Eastern Mediterranean Sea)

¹ Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, 3036 Limassol, Cyprus
² University of Nicosia Research Foundation, 46 Makedonitissas Avenue, CY-2417 P.O. Box 24005, CY-1700 Nicosia, Cyprus.
³ Department of Fisheries and Marine Research, Ministry of Agriculture, Rural Development and the Environment, 2033 Nicosia, Cyprus
⁴ Cyprus Subsea Consulting and Services (CSCS) Ltd, 34A Paragogikotitas Street, 2326 Lakatamia, Nicosia, Cyprus
⁵ Cyprus Marine & Maritime Institute, CMMI House, Vasileos Pavlou Square, P.O.Box 40930, 6023 Larnaca, Cyprus

^* Corresponding author: e.hadjisolomou@cut.ac.cy

Abstract

A surfeit of Dissolved Inorganic Nitrogen (DIN), which is defined as the total amount of nitrite, nitrate, and ammonium levels in water, may cause negative effects to the marine environment. For example, elevated levels of DIN may promote surplus production of algae and possible depletion of oxygen in the water column. The DIN in the marine water column is monitored as part of the Water Framework Directive (WFD), the Nitrates Directive and the EU Marine Strategy Framework Directive (MSFD). Data-driven models have been proved to be an excellent management tool for environmental issues related to coastal water quality protection and management. Based on data-drive models, and specifically the Artificial Neural Networks (ANNs), the DIN levels from coastal stations in Cyprus were predicted. To do so, three different ANNs models were created, each of them calculating nitrite, nitrate, and ammonium levels respectively with high accuracy (r>0.95). The results derived from these models can be used to identify hot-spot areas with increased DIN levels and to evaluate management scenarios and measures to be implemented in order to maintain the good Environmental Status and quality of the coastal waters.