Assessment for surface water quality in Lake Taihu Tiaoxi River Basin China based on support vector machine

Li, Wei; Yang, Muyi; Liang, Zhiwei; Zhu, Yuchuan; Mao, Wei Juan; Shi, Jiyan; Chen, Yingxu

doi:10.1007/s00477-013-0720-3

Cited by 41 publications

(11 citation statements)

References 25 publications

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“…Detailed information about the application of the cross-validation is described in Park et al (2015b). This procedure has the advantage of utilizing all available data for both model building and calibration, and can prevent overfitting (Bergstr€ om et al, 2013;Li et al, 2013). The hyperparameters were tuned for each model to obtain the optimal result with package "caret" in R (Kuhn et al, 2016;Kuhn, 2008).…”

Section: Model Developmentmentioning

confidence: 99%

Comparison of models for predicting the changes in phytoplankton community composition in the receiving water system of an inter-basin water transfer project

Zeng

Liu

Zhao

et al. 2017

Environmental Pollution

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Inter-basin water transfer projects might cause complex hydro-chemical and biological variation in the receiving aquatic ecosystems. Whether machine learning models can be used to predict changes in phytoplankton community composition caused by water transfer projects have rarely been studied. In the present study, we used machine learning models to predict the total algal cell densities and changes in phytoplankton community composition in Miyun reservoir caused by the middle route of the South-to-North Water Transfer Project (SNWTP). The model performances of four machine learning models, including regression trees (RT), random forest (RF), support vector machine (SVM), and artificial neural network (ANN) were evaluated and the best model was selected for further prediction. The results showed that the predictive accuracies (Pearson's correlation coefficient) of the models were RF (0.974), ANN (0.951), SVM (0.860), and RT (0.817) in the training step and RF (0.806), ANN (0.734), SVM (0.730), and RT (0.692) in the testing step. Therefore, the RF model was the best method for estimating total algal cell densities. Furthermore, the predicted accuracies of the RF model for dominant phytoplankton phyla (Cyanophyta, Chlorophyta, and Bacillariophyta) in Miyun reservoir ranged from 0.824 to 0.869 in the testing step. The predicted proportions with water transfer of the different phytoplankton phyla ranged from -8.88% to 9.93%, and the predicted dominant phyla with water transfer in each season remained unchanged compared to the phytoplankton succession without water transfer. The results of the present study provide a useful tool for predicting the changes in phytoplankton community caused by water transfer. The method is transferrable to other locations via establishment of models with relevant data to a particular area. Our findings help better understanding the possible changes in aquatic ecosystems influenced by inter-basin water transfer.

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Section: Model Developmentmentioning

confidence: 99%

Comparison of models for predicting the changes in phytoplankton community composition in the receiving water system of an inter-basin water transfer project

Zeng

Liu

Zhao

et al. 2017

Environmental Pollution

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“…SVR explores a kernel-based ANN to address the drawbacks of conventional ANNs [35]. As a result, SVR has been shown to be very resilient and efficient for the nonlinear modeling of noisy mixed data [36][37][38]. The main principle underlying SVR is the use of mathematical functions (kernels) to move the original data sets from the input space to a high-dimensional feature space, simplifying the regression in the feature space [39].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Hybrid Random Forest Model for Tea Yield Prediction Using Satellite-Derived Variables

et al. 2022

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Crop yield forecasting is critical for enhancing food security and ensuring an appropriate food supply. It is critical to complete this activity with high precision at the regional and national levels to facilitate speedy decision-making. Tea is a big cash crop that contributes significantly to economic development, with a market of USD 200 billion in 2020 that is expected to reach over USD 318 billion by 2025. As a developing country, Bangladesh can be a greater part of this industry and increase its exports through its tea yield and production with favorable climatic features and land quality. Regrettably, the tea yield in Bangladesh has not increased significantly since 2008 like many other countries, despite having suitable climatic and land conditions, which is why quantifying the yield is imperative. This study developed a novel spatiotemporal hybrid DRS–RF model with a dragonfly optimization (DR) algorithm and support vector regression (S) as a feature selection approach. This study used satellite-derived hydro-meteorological variables between 1981 and 2020 from twenty stations across Bangladesh to address the spatiotemporal dependency of the predictor variables for the tea yield (Y). The results illustrated that the proposed DRS–RF hybrid model improved tea yield forecasting over other standalone machine learning approaches, with the least relative error value (11%). This study indicates that integrating the random forest model with the dragonfly algorithm and SVR-based feature selection improves prediction performance. This hybrid approach can help combat food risk and management for other countries.

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“…The application of artificial intelligence approaches, such as artificial neural networks (ANNs), adaptive neuro‐fuzzy inference system (ANFIS), gene expression programming (GEP) and support vector machines (SVM), have received much attention in the last decades (Aytek and Alp, ; Turan and Yurdusev, ; Kisi et al , ; Sanikhani et al , ; Li et al , ; Mehr et al , ). The comprehensive review of such applications is beyond the scope of this paper, and only some related studies will be given here.…”

Section: Introductionmentioning

confidence: 99%

Modelling long‐term monthly temperatures by several data‐driven methods using geographical inputs

Kisi

Hadi

2015

Intl Journal of Climatology

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This paper investigates the ability of five different data‐driven methods, artificial neural networks (ANNs), adaptive neuro‐fuzzy inference system (ANFIS) with grid partition (GP), ANFIS with subtractive clustering (SC), support vector regression (SVR) and gene expression programming (GEP), in predicting long‐term monthly temperatures by using data from 50 stations in Iran. The periodicity component (month of the year), station latitude, longitude and altitude values were used as inputs to the applied models to predict the long‐term monthly temperatures. The overall accuracy of the SVR model was found to be better than that of the other models. The GEP model gave the worst estimates. The maximum determination coefficient (R2) values were found to be 0.996, 0.999, 0.997 and 0.959 for the ANN, ANFIS‐GP, ANFIS‐SC and GEP models in Karaj and Qazvin stations, respectively. The highest R2 value (0.999) of SVR model was found for the Tabas station. The minimum R2 values were respectively found as 0.988, 0.946 and 0.985 for the ANFIS‐GP, ANFIS‐SC and SVR models in Bandar Abbas station while the ANN and GEP models gave the minimum R2 values of 0.982 and 0.886 in the Abadan and Kerman stations, respectively. The results indicated that the long‐term monthly temperatures of any site can be successfully estimated by data‐driven methods applied in this study using geographical inputs. The interpolated maps of temperatures were also obtained by using the optimal SVR model and evaluated in the study. The temperature maps showed that the highest temperatures were occurred in the southeastern and central parts of the Iran.

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Assessment for surface water quality in Lake Taihu Tiaoxi River Basin China based on support vector machine

Cited by 41 publications

References 25 publications

Comparison of models for predicting the changes in phytoplankton community composition in the receiving water system of an inter-basin water transfer project

Comparison of models for predicting the changes in phytoplankton community composition in the receiving water system of an inter-basin water transfer project

Spatiotemporal Hybrid Random Forest Model for Tea Yield Prediction Using Satellite-Derived Variables

Modelling long‐term monthly temperatures by several data‐driven methods using geographical inputs

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