Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Abstract

Freshwater reservoirs are considered as the source of atmospheric greenhouse gas (GHG), but more than 96% of global reservoirs have never been monitored. Compared to the difficulty and high cost of field measurements, statistical models are a better choice to simulate the carbon emissions from reservoirs. In this study, two types of Artificial Neural Networks (ANNs), Back Propagation Neural Network (BPNN) and Generalized Regression Neural Network (GRNN), were used to predict carbon dioxide (CO 2 ) flux emissions from reservoirs based on the published data. Input variables, which were latitude, age, the potential net primary productivity, and mean depth, were selected by Spearman correlation analysis, and then the rationality of these inputs was proved by sensitivity analysis. Besides this, a Multiple Non-Linear Regression (MNLR) and a Multiple Linear Regression (MLR) were used for comparison with ANNs. The performance of models was assessed by statistical metrics both in training and testing phases. The results indicated that ANNs gave more accurate results than regression models and GRNN provided the best performance. With the help of this GRNN, the total CO 2 emitted by global reservoirs was estimated and possible CO 2 flux emissions from a planned reservoir was assessed, which illustrated the potential application of GRNN.

Modelling Methane and Nitrous Oxide Emissions from Rice Paddy Wetlands in India Using Artificial Neural Networks (ANNs)

“…Just as neurons in the human brain process the available information, based on past training, to arrive at intelligent solutions, artificial (and virtual) neurons are 'trained' with computers on the basis of existing data so that they can then 'intelligently' process new data and generate solutions one seeks. The ANN technique, which has been made increasingly versatile and precise over the years, has been gaining popularity due to its ability to pick up the linear or non-linear relationships among data, identify complex patterns in datasets, and make adequately accurate estimations even in situations where the number of input variables are limited [10][11][12]. Further, ANNs can be used for the mapping of input to output data without knowing a priori relationship between that data.…”

“…The competence of the ANNs is further enhanced by their non-parametric nature. Due to this, the learning mechanism of the ANNs is independent of the structure of the data; hence the need for establishing prior distribution of data is obviated [12,13].…”

“…In a similar fashion Perez-Zarate et al [21] have used 80% of the past data for ANN training, 10% for validation, and 10% of training. Chen et al [12] have also used 70% of past data (generated by others) for model development and the remaining 30% for model validation without using any data of their own.…”

Recent Advances in Adaptive Catchment Management and Reservoir Operation

“…Recent research has confirmed that reservoirs emit a significant amount of greenhouse gas emissions, but one of the challenges is how to accurately quantify greenhouse gas emissions from individual reservoirs. Chen et al [23] used two artificial neutral networks to estimate the total carbon dioxide emissions from the world's reservoirs and concluded that the models can be used to predict CO 2 emissions from new reservoirs.…”

A Model to Forecast Methane Emissions from Topical and Subtropical Reservoirs on the Basis of Artificial Neural Networks

“…In recent reports, Deemer et al, [6]; the World Bank [1], and Chen et al [7] have emphasized the paucity of information that exists on the emission of carbon dioxide from hydroelectric and other surface water reservoirs. This has been so despite the fact that the first report on this subject had come over a quarter-century ago-in 1993 [8]-and the issue has been hotly debated ever since, remaining alive in scientific circles, as well as layperson's concerns.…”