Improved Understanding of Dissolved Organic Matter Processing in Freshwater Using Complementary Experimental and Machine Learning Approaches

2020 ◽  
Vol 54 (21) ◽  
pp. 13556-13565 ◽  
Author(s):  
Peter Herzsprung ◽  
Valerie Wentzky ◽  
Norbert Kamjunke ◽  
Wolf von Tümpling ◽  
Christin Wilske ◽  
...  
Keyword(s):  
Machine Learning ◽  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Learning Approaches ◽  
Organic Matter Processing

scholarly journals Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using vis-NIR Spectra

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 263 ◽  
Author(s):  
Meihua Yang ◽  
Dongyun Xu ◽  
Songchao Chen ◽  
Hongyi Li ◽  
Zhou Shi
Keyword(s):  
Machine Learning ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Least Squares ◽  
Paddy Soil ◽  
Prediction Accuracy ◽  
Accurate Determination ◽  
Support Vector ◽  
Learning Approaches ◽  
Lower Yangtze

Soil organic matter (SOM) and pH are essential soil fertility indictors of paddy soil in the middle-lower Yangtze Plain. Rapid, non-destructive and accurate determination of SOM and pH is vital to preventing soil degradation caused by inappropriate land management practices. Visible-near infrared (vis-NIR) spectroscopy with multivariate calibration can be used to effectively estimate soil properties. In this study, 523 soil samples were collected from paddy fields in the Yangtze Plain, China. Four machine learning approaches—partial least squares regression (PLSR), least squares-support vector machines (LS-SVM), extreme learning machines (ELM) and the Cubist regression model (Cubist)—were used to compare the prediction accuracy based on vis-NIR full bands and bands reduced using the genetic algorithm (GA). The coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to inter-quartile distance (RPIQ) were used to assess the prediction accuracy. The ELM with GA reduced bands was the best model for SOM (SOM: R2 = 0.81, RMSE = 5.17, RPIQ = 2.87) and pH (R2 = 0.76, RMSE = 0.43, RPIQ = 2.15). The performance of the LS-SVM for pH prediction did not differ significantly between the model with GA (R2 = 0.75, RMSE = 0.44, RPIQ = 2.08) and without GA (R2 = 0.74, RMSE = 0.45, RPIQ = 2.07). Although a slight increase was observed when ELM were used for prediction of SOM and pH using reduced bands (SOM: R2 = 0.81, RMSE = 5.17, RPIQ = 2.87; pH: R2 = 0.76, RMSE = 0.43, RPIQ = 2.15) compared with full bands (R2 = 0.81, RMSE = 5.18, RPIQ = 2.83; pH: R2 = 0.76, RMSE = 0.45, RPIQ = 2.07), the number of wavelengths was greatly reduced (SOM: 201 to 44; pH: 201 to 32). Thus, the ELM coupled with reduced bands by GA is recommended for prediction of properties of paddy soil (SOM and pH) in the middle-lower Yangtze Plain.

scholarly journals Machine Learning Algorithms for Chromophoric Dissolved Organic Matter (CDOM) Estimation Based on Landsat 8 Images

2021 ◽  
Vol 13 (18) ◽  
pp. 3560
Author(s):  
Xiao Sun ◽  
Yunlin Zhang ◽  
Yibo Zhang ◽  
Kun Shi ◽  
Yongqiang Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Absorption Coefficient ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Chromophoric Dissolved Organic Matter ◽  
Estimation Accuracy ◽  
Inland Waters ◽  
Landsat 8

Chromophoric dissolved organic matter (CDOM) is crucial in the biogeochemical cycle and carbon cycle of aquatic environments. However, in inland waters, remotely sensed estimates of CDOM remain challenging due to the low optical signal of CDOM and complex optical conditions. Therefore, developing efficient, practical and robust models to estimate CDOM absorption coefficient in inland waters is essential for successful water environment monitoring and management. We examined and improved different machine learning algorithms using extensive CDOM measurements and Landsat 8 images covering different trophic states to develop the robust CDOM estimation model. The algorithms were evaluated via 111 Landsat 8 images and 1708 field measurements covering CDOM light absorption coefficient a(254) from 2.64 to 34.04 m−1. Overall, the four machine learning algorithms achieved more than 70% accuracy for CDOM absorption coefficient estimation. Based on model training, validation and the application on Landsat 8 OLI images, we found that the Gaussian process regression (GPR) had higher stability and estimation accuracy (R2 = 0.74, mean relative error (MRE) = 22.2%) than the other models. The estimation accuracy and MRE were R2 = 0.75 and MRE = 22.5% for backpropagation (BP) neural network, R2 = 0.71 and MRE = 24.4% for random forest regression (RFR) and R2 = 0.71 and MRE = 24.4% for support vector regression (SVR). In contrast, the best three empirical models had estimation accuracies of R2 less than 0.56. The model accuracies applied to Landsat images of Lake Qiandaohu (oligo-mesotrophic state) were better than those of Lake Taihu (eutrophic state) because of the more complex optical conditions in eutrophic lakes. Therefore, machine learning algorithms have great potential for CDOM monitoring in inland waters based on large datasets. Our study demonstrates that machine learning algorithms are available to map CDOM spatial-temporal patterns in inland waters.

scholarly journals Machine Learning Regression Approaches for Colored Dissolved Organic Matter (CDOM) Retrieval with S2-MSI and S3-OLCI Simulated Data

2018 ◽  
Vol 10 (5) ◽  
pp. 786 ◽  
Author(s):  
Ana Ruescas ◽  
Martin Hieronymi ◽  
Gonzalo Mateo-Garcia ◽  
Sampsa Koponen ◽  
Kari Kallio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Simulated Data ◽  
Colored Dissolved Organic Matter

scholarly journals Dissolved Organic Matter Processing in Pristine Antarctic Streams

2021 ◽  
Author(s):  
Morimaru Kida ◽  
Nobuhide Fujitake ◽  
Taichi Kojima ◽  
Yukiko Tanabe ◽  
Kentaro Hayashi ◽  
...  
Keyword(s):  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Organic Matter Processing
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