scholarly journals Prediction of E. coli Concentrations in Agricultural Pond Waters: Application and Comparison of Machine Learning Algorithms

2022 ◽  
Vol 4 ◽  
Author(s):  
Matthew D. Stocker ◽  
Yakov A. Pachepsky ◽  
Robert L. Hill
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Quality Parameters ◽  
Machine Learning Algorithms ◽  
Water Quality Parameters ◽  
Gradient Boosting ◽  
Support Vector ◽  
E Coli ◽  
Stochastic Gradient Boosting ◽  
Significant Difference

The microbial quality of irrigation water is an important issue as the use of contaminated waters has been linked to several foodborne outbreaks. To expedite microbial water quality determinations, many researchers estimate concentrations of the microbial contamination indicator Escherichia coli (E. coli) from the concentrations of physiochemical water quality parameters. However, these relationships are often non-linear and exhibit changes above or below certain threshold values. Machine learning (ML) algorithms have been shown to make accurate predictions in datasets with complex relationships. The purpose of this work was to evaluate several ML models for the prediction of E. coli in agricultural pond waters. Two ponds in Maryland were monitored from 2016 to 2018 during the irrigation season. E. coli concentrations along with 12 other water quality parameters were measured in water samples. The resulting datasets were used to predict E. coli using stochastic gradient boosting (SGB) machines, random forest (RF), support vector machines (SVM), and k-nearest neighbor (kNN) algorithms. The RF model provided the lowest RMSE value for predicted E. coli concentrations in both ponds in individual years and over consecutive years in almost all cases. For individual years, the RMSE of the predicted E. coli concentrations (log10 CFU 100 ml−1) ranged from 0.244 to 0.346 and 0.304 to 0.418 for Pond 1 and 2, respectively. For the 3-year datasets, these values were 0.334 and 0.381 for Pond 1 and 2, respectively. In most cases there was no significant difference (P > 0.05) between the RMSE of RF and other ML models when these RMSE were treated as statistics derived from 10-fold cross-validation performed with five repeats. Important E. coli predictors were turbidity, dissolved organic matter content, specific conductance, chlorophyll concentration, and temperature. Model predictive performance did not significantly differ when 5 predictors were used vs. 8 or 12, indicating that more tedious and costly measurements provide no substantial improvement in the predictive accuracy of the evaluated algorithms.

Remote sensing inversion of water quality in coastal sea area based on machine learning: a case study of Shenzhen bay, China

2021 ◽  
Author(s):  
Xiaotong Zhu ◽  
Jinhui Jeanne Huang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Predictive Accuracy ◽  
Water Environment ◽  
Quality Parameters ◽  
Machine Learning Algorithms ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Seawater Quality

<p>Remote sensing monitoring has the characteristics of wide monitoring range, celerity, low cost for long-term dynamic monitoring of water environment. With the flourish of artificial intelligence, machine learning has enabled remote sensing inversion of seawater quality to achieve higher prediction accuracy. However, due to the physicochemical property of the water quality parameters, the performance of algorithms differs a lot. In order to improve the predictive accuracy of seawater quality parameters, we proposed a technical framework to identify the optimal machine learning algorithms using Sentinel-2 satellite and in-situ seawater sample data. In the study, we select three algorithms, i.e. support vector regression (SVR), XGBoost and deep learning (DL), and four seawater quality parameters, i.e. dissolved oxygen (DO), total dissolved solids (TDS), turbidity(TUR) and chlorophyll-a (Chla). The results show that SVR is a more precise algorithm to inverse DO (R<sup>2</sup> = 0.81). XGBoost has the best accuracy for Chla and Tur inversion (R<sup>2</sup> = 0.75 and 0.78 respectively) while DL performs better in TDS (R<sup>2</sup> =0.789). Overall, this research provides a theoretical support for high precision remote sensing inversion of offshore seawater quality parameters based on machine learning.</p>

scholarly journals Comparison Between Machine Learning Models for Yield Forecast in Cocoa Crops in Santander, Colombia

2020 ◽  
Vol 29 (54) ◽  
pp. e10853
Author(s):  
Henry Lamos-Díaz ◽  
David Esteban Puentes-Garzón ◽  
Diego Alejandro Zarate-Caicedo
Keyword(s):  
Machine Learning ◽  
Influencing Factors ◽  
Crop Yield ◽  
Sun Exposure ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Models ◽  
Essential Information ◽  
Significant Difference

The identification of influencing factors in crop yield (kg·ha-1) provides essential information for decision-making processes related to the prediction and improvement of productivity, which gives farmers the opportunity to increase their income. The current study investigates the application of multiple machine learning algorithms for cocoa yield prediction and influencing factors identification. The Support Vector Machines (SVM) and Ensemble Learning Models (Random Forests, Gradient Boosting) are compared with Least Absolute Shrinkage and Selection Operator (LASSO) regression models. The considered predictors were climate conditions, cocoa variety, fertilization level and sun exposition in an experimental crop located in Rionegro, Santander. Results showed that Gradient Boosting is the best prediction alternative with Coefficient of determination (R2) = 68%, Mean Absolute Error (MAE) = 13.32, and Root Mean Square Error (RMSE) = 20.41. The crop yield variability is explained mainly by the radiation one month before harvest, the accumulated rainfall on the harvest month, and the temperature one month before harvest. Likewise, the crop yields are evaluated based on the kind of sun exposure, and it was found that radiation one month before harvest is the most influential factor in shade-grown plants. On the other hand, rainfall and soil moisture are determining variables in sun-grown plants, which is associated with the water requirements. These results suggest a differentiated management for crops depending on the kind of sun exposure to avoid compromising productivity, since there is no significant difference in the yield of both agricultural managements.

Retrieval of Water Quality Parameters in Lake Ontario Based on Hyperspectral Remote Sensing Data and Intelligent Algorithms

2020 ◽  
Author(s):  
Yu Li ◽  
Youyue Sun ◽  
Jinhui Jeanne Huang ◽  
Edward McBean
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Remote Sensing Data ◽  
Quality Parameters ◽  
Hyperspectral Remote Sensing ◽  
Optically Active ◽  
Water Quality Parameters ◽  
Support Vector ◽  
Sensing Data

<p>With the increasingly prominent ecological and environmental problems in lakes, the monitoring water quality in lakes by satellite remote sensing is becoming more and more high demanding. Traditional water quality sampling is normally conducted manually and are time-consuming and labor-costly. It could not provide a full picture of the waterbodies over time due to limited sampling points and low sampling frequency. A novel attempt is proposed to use hyperspectral remote sensing in conjunction with machine learning technologies to retrieve water quality parameters and provide mapping for these parameters in a lake. The retrieval of both optically active parameters: Chlorophyll-a (CHLA) and dissolved oxygen concentration (DO), as well as non-optically active parameters: total phosphorous (TP), total nitrogen (TN), turbidity (TB), pH were studied in this research. A comparison of three machine learning algorithms including Random Forests (RF), Support Vector Regression (SVR) and Artificial Neural Networks were conducted. These water parameters collected by the Environment and Climate Change Canada agency for 20 years were used as the ground truth for model training and validation. Two set of remote sensing data from MODIS and Sentinel-2 were utilized and evaluated. This research proposed a new approach to retrieve both optically active parameters and non-optically active parameters for water body and provide new strategy for water quality monitoring.</p>

scholarly journals A Generalized Method for Modeling the Adsorption of Heavy Metals with Machine Learning Algorithms

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3490
Author(s):  
Noor Hafsa ◽  
Sayeed Rushd ◽  
Mohammed Al-Yaari ◽  
Muhammad Rahman
Keyword(s):  
Machine Learning ◽  
Heavy Metals ◽  
Mean Squared Error ◽  
Learning Algorithms ◽  
Regression Tree ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Support Vector ◽  
Stochastic Gradient Boosting

Applications of machine learning algorithms (MLAs) to modeling the adsorption efficiencies of different heavy metals have been limited by the adsorbate–adsorbent pair and the selection of specific MLAs. In the current study, adsorption efficiencies of fourteen heavy metal–adsorbent (HM-AD) pairs were modeled with a variety of ML models such as support vector regression with polynomial and radial basis function kernels, random forest (RF), stochastic gradient boosting, and bayesian additive regression tree (BART). The wet experiment-based actual measurements were supplemented with synthetic data samples. The first batch of dry experiments was performed to model the removal efficiency of an HM with a specific AD. The ML modeling was then implemented on the whole dataset to develop a generalized model. A ten-fold cross-validation method was used for the model selection, while the comparative performance of the MLAs was evaluated with statistical metrics comprising Spearman’s rank correlation coefficient, coefficient of determination (R2), mean absolute error, and root-mean-squared-error. The regression tree methods, BART, and RF demonstrated the most robust and optimum performance with 0.96 ⫹ R2 ⫹ 0.99. The current study provides a generalized methodology to implement ML in modeling the efficiency of not only a specific adsorption process but also a group of comparable processes involving multiple HM-AD pairs.

scholarly journals Retrieval of Water Quality from UAV-Borne Hyperspectral Imagery: A Comparative Study of Machine Learning Algorithms

2021 ◽  
Vol 13 (19) ◽  
pp. 3928
Author(s):  
Qikai Lu ◽  
Wei Si ◽  
Lifei Wei ◽  
Zhongqiang Li ◽  
Zhihong Xia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Learning Algorithms ◽  
Quality Parameters ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Water Bodies ◽  
Water Quality Parameters ◽  
World Population ◽  
Freshwater Resources

The rapidly increasing world population and human activities accelerate the crisis of the limited freshwater resources. Water quality must be monitored for the sustainability of freshwater resources. Unmanned aerial vehicle (UAV)-borne hyperspectral data can capture fine features of water bodies, which have been widely used for monitoring water quality. In this study, nine machine learning algorithms are systematically evaluated for the inversion of water quality parameters including chlorophyll-a (Chl-a) and suspended solids (SS) with UAV-borne hyperspectral data. In comparing the experimental results of the machine learning model on the water quality parameters, we can observe that the prediction performance of the Catboost regression (CBR) model is the best. However, the prediction performances of the Multi-layer Perceptron regression (MLPR) and Elastic net (EN) models are very unsatisfactory, indicating that the MLPR and EN models are not suitable for the inversion of water quality parameters. In addition, the water quality distribution map is generated, which can be used to identify polluted areas of water bodies.

scholarly journals Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4068
Author(s):  
Xu Huang ◽  
Mirna Wasouf ◽  
Jessada Sresakoolchai ◽  
Sakdirat Kaewunruen
Keyword(s):  
Machine Learning ◽  
Search Algorithm ◽  
Weather Conditions ◽  
Prediction Performance ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Support Vector ◽  
Self Healing ◽  
Artificial Neural Network Ann

Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world’s first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R2) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R2GSA-GBR = 0.958) and stronger robustness (RMSEGSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved.

scholarly journals Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements

2021 ◽  
Vol 10 (1) ◽  
pp. 42
Author(s):  
Kieu Anh Nguyen ◽  
Walter Chen ◽  
Bor-Shiun Lin ◽  
Uma Seeboonruang
Keyword(s):  
Machine Learning ◽  
Soil Erosion ◽  
Ensemble Methods ◽  
Machine Learning Algorithms ◽  
Multivariate Adaptive Regression Splines ◽  
Gradient Boosting ◽  
Support Vector ◽  
Ensemble Machine Learning ◽  
Boosting Method ◽  
Bagging Method

Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging, (b) boosting, and (c) stacking. The bagging method in this study refers to bagged multivariate adaptive regression splines (bagged MARS) and random forest (RF), and the boosting method includes Cubist and gradient boosting machine (GBM). Finally, the stacking method is an ensemble method that uses a meta-model to combine the predictions of base models. This study used RF and GBM as the meta-models, decision tree, linear regression, artificial neural network, and support vector machine as the base models. The dataset used in this study was sampled using stratified random sampling to achieve a 70/30 split for the training and test data, and the process was repeated three times. The performance of six ensemble methods in three categories was analyzed based on the average of three attempts. It was found that GBM performed the best among the ensemble models with the lowest root-mean-square error (RMSE = 1.72 mm/year), the highest Nash-Sutcliffe efficiency (NSE = 0.54), and the highest index of agreement (d = 0.81). This result was confirmed by the spatial comparison of the absolute differences (errors) between model predictions and observations using GBM and RF in the study area. In summary, the results show that as a group, the bagging method and the boosting method performed equally well, and the stacking method was third for the erosion pin dataset considered in this study.

Prediction and Analysis of Gold Prices using Ensemble Machine Learning Algorithms

2021 ◽  
Vol 9 (VI) ◽  
pp. 4367-4374
Author(s):  
Gudipally Chandrashakar
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Gold Price ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Gradient Boosting ◽  
Support Vector ◽  
Average Value ◽  
Ensemble Machine Learning

In this article, we used historical time series data up to the current day gold price. In this study of predicting gold price, we consider few correlating factors like silver price, copper price, standard, and poor’s 500 value, dollar-rupee exchange rate, Dow Jones Industrial Average Value. Considering the prices of every correlating factor and gold price data where dates ranging from 2008 January to 2021 February. Few algorithms of machine learning are used to analyze the time-series data are Random Forest Regression, Support Vector Regressor, Linear Regressor, ExtraTrees Regressor and Gradient boosting Regression. While seeing the results the Extra Tree Regressor algorithm gives the predicted value of gold prices more accurately.

scholarly journals MODIS-FIRMS and ground-truthing based wildfire likelihood mapping of Sikkim Himalaya using machine learning algorithms.

2021 ◽  
Author(s):  
Polash Banerjee
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Tree Cover ◽  
Anthropogenic Factors ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Methods ◽  
Sikkim Himalaya ◽  
Environmental Features ◽  
Machine Learning Methods

Abstract Wildfires in limited extent and intensity can be a boon for the forest ecosystem. However, recent episodes of wildfires of 2019 in Australia and Brazil are sad reminders of their heavy ecological and economical costs. Understanding the role of environmental factors in the likelihood of wildfires in a spatial context would be instrumental in mitigating it. In this study, 14 environmental features encompassing meteorological, topographical, ecological, in situ and anthropogenic factors have been considered for preparing the wildfire likelihood map of Sikkim Himalaya. A comparative study on the efficiency of machine learning methods like Generalized Linear Model (GLM), Support Vector Machine (SVM), Random Forest (RF) and Gradient Boosting Model (GBM) has been performed to identify the best performing algorithm in wildfire prediction. The study indicates that all the machine learning methods are good at predicting wildfires. However, RF has outperformed, followed by GBM in the prediction. Also, environmental features like average temperature, average wind speed, proximity to roadways and tree cover percentage are the most important determinants of wildfires in Sikkim Himalaya. This study can be considered as a decision support tool for preparedness, efficient resource allocation and sensitization of people towards mitigation of wildfires in Sikkim.

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