Numeric Prediction of Dissolved Oxygen Status Through Two-Stage Training for Classification-Driven Regression

Abstract

Dissolved oxygen of aquaculture is an important measure of the quality of culture environment and how aquatic products have been grown. In the machine learning context, the above measure can be achieved by defining a regression problem, which aims at numerical prediction of the dissolved oxygen status. In general, the vast majority of popular machine learning algorithms were designed for undertaking classification tasks. In order to effectively adopt the popular machine learning algorithms for the above-mentioned numerical prediction, in this paper, we propose a two-stage training approach that involves transforming a regression problem into a classification problem and then transforming it back to regression problem. In particular, unsupervised discretization of continuous attributes is adopted at the first stage to transform the target (numeric) attribute into a discrete (nominal) one with several intervals, such that popular machine learning algorithms can be used to predict the interval to which an instance belongs in the setting of a classification task. Furthermore, based on the classification result at the first stage, some of the instances within the predicted interval are selected for training at the second stage towards numerical prediction of the target attribute value of each instance. An experimental study is conducted to investigate in general the effectiveness of the popular learning algorithms in the numerical prediction task and also analyze how the increase of the number of training instances (selected at the second training stage) can impact on the final prediction performance. The results show that the adoption of decision tree learning and neural networks lead to better and more stable performance than Naive Bayes, K Nearest Neighbours and Support Vector Machine.

Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach

“…Several models based on different prediction methods have been developed for DO concentration forecasting in aquaculture ecosystems [11][12][13][14][15][16][17]. Xiao et al [11] applied back propagation (BP) NN method with the combination of purelin, logsig, and tansig activation functions to propose a prediction model for DO concentration in aquaculture.…”

“…Wijayanti KN [12] proposed a forecasting model based on Smooth Support Vector Machine (SSVM) for short-term forecasting of the aquaculture water quality. Guo et al [13] proposed a numeric forecasting model for DO status through a two-stage training for Classification-Driven Regression (CDR). Xue et al [14] applied neural network and decision tree to conduct forecasting and warning system regarding DO concentration in carp aquaculture.…”

An efficient strategy for predicting river dissolved oxygen concentration: application of deep recurrent neural network model

“…Many studies have previously focused on estimating/predicting DO concentration (Huan et al 2018). Various methods have been adopted (Poole 1976), either numerical or physical (Guo et al 2019). Physical models producing deterministic equations are somewhat limited because they are typically time-consuming and costly and because the complexity of biotic and abiotic processes cannot fully be taken into account during the experiments and in the resulting mathematical equations.…”