Predicting Coastal Dissolved Oxygen Values with the Use of Artificial Neural Networks: A Case Study for Cyprus

Abstract

Coastal hypoxia is a serious environmental problem that needs to be addressed at a global level. The phenomenon of hypoxia is characterized by low Dissolved Oxygen (DO) levels in the water column that causes detrimental effects on aquatic organisms. Anthropogenic activities such as intensive agriculture practices and point-source nutrient loading are considered the main causes of coastal hypoxia. This study utilizes data-driven modelling based on Artificial Neural Networks (ANNs), and specifically Feed-Forward ANNs, to predict surface DO levels. Several surface water quality parameters such as water temperature, nitrogen species (ammonium, nitrite and nitrate), phosphorus, pH, salinity, electrical conductivity, and chlorophyll-a served as the ANN’s input parameters. These parameters were measured at several sampling sites in the coastal waters of Cyprus and some of the sites were located near areas with anthropogenic activities, during the period 2000-2021. An ANN with a 9-5-1 topology was developed and ANN managed to predict with good accuracy the DO levels, with the Coefficient of determination (r 2) as high as r 2=0.991 for the test set. Additionally, sensitivity analysis was performed to measure the impact of each input parameter on the DO level, and it was estimated that the water temperature is the most influential factor. The “Weights” sensitivity analysis algorithm was used for this purpose. The ANN-based method proposed can be used as a management tool for predicting the DO levels and prevention of hypoxia. Therefore, this work has a positive impact on marine sciences and marine information technology.

Data-Driven Models for Evaluating Coastal Eutrophication: A Case Study for Cyprus

“…The Levantine Sea is considered as one of the most oligotrophic seas worldwide [28] and, therefore, Cypriot marine waters have very low primary algal production due to the limited nutrient availability [29]. In addition, the Levantine Sea has high temperatures fluctuating annually from 16 • C (winter season) up to 26 • C (summer season) [22]. Moreover, the evaporation and salinity are high (yearly average salinity of Eastern Mediterranean exceeds 37.5 psu, while average salinity of coastal waters of Cyprus is 39.1 psu).…”

“…Another category of ANNs are multilayer feed-forward neural networks, which are supervised-learning-based ANNs. This type of ANN is capable of predicting Chl-a levels based on several water quality parameters associated with algal production [22]. These environmental parameters, which are used as the ANN's input, may differ among modeling studies of coastal eutrophication.…”

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

“…Additionally, ecological data are usually noisy, non-linear, complex and affected by internal relationships between the parameters [12]. Artificial Neural Networks (ANNs) -which are data-driven models-are suitable for modelling the non-linear and complex aquatic systems, producing results highly accurate (e.g., [13]).…”

“…Several water quality modelling studies utilizing ANNs are created during the last decades (e.g., [5,[12][13][14][15]). For example, in the study of Salami-Shahid and Ehteshami [16] two ANNs were developed to predict the DO and salinity parameters using variables from a datarecording station in the San Joaquin River (USA).…”

“…In this modelling study three different ANNs were created, each of them calculating nitrite, nitrate, and ammonium levels for the coastal waters of Cyprus. Additionally, the fact that the created ANNs models are regional models, which take into consideration the unique environmental characteristics of Cyprus coastal water quality parameters [13] enables us to utilize these ANNs to investigate specialized management scenarios regarding the DIN parameter for the coastal water of Cyprus. Sensitivity analysis was calculated for each of these ANNs to investigate the impact/role of each input parameter.…”