Data for physico-chemical limnological concentrations determination in aquaponic pond
Understanding the physico-chemical interactions on outdoor artificial aquaponics pond is the fundamental for effective nutrient dynamics in cultivating fishes and vegetable crops. The current trend of water quality monitoring is through time consuming laboratory experiments or deployment of expensive chemical sensors. This dataset contains water temperature, pH and electrical conductivity sensor-acquired data from temperature perturbed aquaponic pond water samples collected from 5 zones in a 250 cubic meter pond in Morong, Rizal, Philippines, and the corresponding dissolved organic compound namely nitrate, phosphate, and potassium absorbances and concentrations through spectrophotometry in the UV-Vis-NIR spectrum. Codes for principal component analysis in selecting the characteristic activated water bands resembling nutrient biomarkers detection, and the codes for regression based decision tree, recurrent neural network and multigene symbolic regression genetic programming for nutrient prediction were provided.