Machine-learning paradigms for selecting ecologically significant input variables

Abstract

Harmful algal blooms, which are considered a serious environmental problem nowadays, occur in coastal waters in many parts of the world. They cause acute ecological damage and ensuing economic losses, due to fish kills and shellfish poisoning as well as public health threats posed by toxic blooms. Recently, data-driven models including machine learning (ML) techniques have been employed to mimic dynamics of algal blooms. One of the most important steps in the application of a ML technique is the selection of significant model input variables. In the present paper, we use two extensively used ML techniques, artificial neural networks (ANN) and genetic programming (GP) for selecting the significant input variables. The efficacy of these techniques is first demonstrated on a test problem with known dependence and then they are applied to a real-world case study of water quality data from Tolo Harbour, Hong Kong. These ML techniques overcome some of the limitations of the currently used techniques for input variable selection, a review of which is also presented. The interpretation of the weights of the trained ANN and the GP evolved equations demonstrate their ability to identify the ecologically significant variables precisely. The significant variables suggested by the ML techniques also indicate chlorophyll-a itself to be the most significant input in predicting the algal blooms, suggesting an auto-regressive nature or persistence in the algal bloom dynamics, which may be related to the long flushing time in the semi-enclosed coastal waters. The study also confirms the previous understanding that the algal blooms in coastal waters of Hong Kong often occur with a life cycle of the order of 1-2 weeks.

Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

“…The process is repeated over the whole time series, and then the average errors of all the months data are calculated. The obtained appropriate architectures of the ANN model A1 and A2 for Xinfengjiang reservoir are (12,15,1) and (2, 5, 1), respectively. Moreover, using GA for parameter selection, the SVM model with parameters (C, ε, σ) = (9.425, 0.823, 0.081) is the forecasting model for Xinfengjiang reservoir.…”

“…Long-term hydrological prediction is of significance for water resource activities, such as reservoir operation [1][2][3][4][5], water resource planning [6][7][8][9], risk management [10][11][12][13], and urbanization [14,15]. Hence, hydrologic time-series forecasting, especially monthly inflow, has triggered great interest in hydrology and water resources fields [16,17].…”

Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China

“…BPNN, GRNN, and SVM model construction were conducted using the MATLAB 2014a software (MathWorks Inc., Natick, USA) installed in Windows XP. It is essential to select appropriate input variables for datadriven model development (Muttil and Chau 2007). In previous studies, many different variables have been employed as model inputs for DO estimation (Antanasijević et al 2013a;Najah et al 2014;Singh et al 2009).…”

Data mining and multivariate statistical analysis for ecological system in coastal waters

“…These include doi: 10.2166/hydro. 2007.003 principal component analysis (Petersen et al 2001;Chen & Mynett 2003), cluster analysis (Brosse et al 2001), machine learning techniques (Muttil & Chau 2007), etc.…”