Mr. Georgios Spanou Profile

Georgios Spanou

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Proceedings Article | 12 August 2014 Paper

Forecasting precipitation and temperatures at the island of Cyprus to enhance wetland management

Georgios Spanou, Konstantinos Ioannou, Valasia Iakovoglou, George Zaimes

Proceedings Volume 9229, 92291N (2014) https://doi.org/10.1117/12.2066296

KEYWORDS: Neural networks, Neurons, Error analysis, Water, Ecosystems, Signal processing, Climatology, Artificial neural networks, Prototyping, Forestry

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Droughts on the island of Cyprus are more frequently occurring during the last decades. This has and will have major impacts on natural resources, particularly on semi-aquatic and aquatic ecosystems. Wetlands are very important aquatic ecosystems with many functions and values, especially in semi-arid regions. The study area is the Wetland of the Larnanca Salt Lake that belongs to the Natura 2000 Network and the Ramsar Convention. It hosts thousands of migratory birds every year. Forecasting accurately the future climatic conditions of an area can greatly enhance the ability to provide the best possible managerial practices regarding a natural resource (e.g. wetland). These climate forecasts can provide significant information on future conditions of the Wetland of Larnaca Salt Lake, particularly when forecasting when and how long the drying conditions could last. In this study, an Artificial Neural Networks (ANN) was used as a tool for short term prediction of the precipitation in the study area. The methodology used two time series (temperature and precipitation) in order to train the ANN. Temperatures were used as the input variable to the ANN while precipitation was used as the output variables. The forecast was based on data from the period between 1993 and 2013. In order to estimate the accuracy of the produced results the correlation coefficient, the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) was correlated. Overall, this tool can help the responsible authorities of the wetland to manage it more efficiently.

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