The Machine learning Technique used in a Study of Water Quality & Water Level estimation Using Remote Sensing & GIS

Silage is the main feed in milk and ruminant meat production in Northern Europe. Novel drone-based remote sensing technology could be utilized in many phases of silage production, but advanced methods of utilizing these data are still developing. Grass swards are harvested three times in season, and fertilizer is applied similarly three times—once for each harvest when aiming at maximum yields. Timely information of the yield is thus necessary several times in a season for making decisions on harvesting time and rate of fertilizer application. Our objective was to develop and assess a novel machine learning technique for the estimation of canopy height and biomass of grass swards utilizing multispectral photogrammetric camera data. Variation in the studied crop stand was generated using six different nitrogen fertilizer levels and four harvesting dates. The sward was a timothy-meadow fescue mixture dominated by timothy. We extracted various features from the remote sensing data by combining an ultra-high resolution photogrammetric canopy height model (CHM) with a pixel size of 1.0 cm and red, green, blue (RGB) and near-infrared range intensity values and different vegetation indices (VI) extracted from orthophoto mosaics. We compared the performance of multiple linear regression (MLR) and a Random Forest estimator (RF) with different combinations of the CHM, RGB and VI features. The best estimation results with both methods were obtained by combining CHM and VI features and all three feature classes (CHM, RGB and VI features). Both estimators provided equally accurate results. The Pearson correlation coefficients (PCC) and Root Mean Square Errors (RMSEs) of the estimations were at best 0.98 and 0.34 t/ha (12.70%), respectively, for the dry matter yield (DMY) and 0.98 and 1.22 t/ha (11.05%), respectively, for the fresh yield (FY) estimations. Our assessment of the sensitivity of the method with respect to different development stages and different amounts of biomass showed that the use of the machine learning technique that integrated multiple features improved the results in comparison to the simple linear regressions. These results were extremely promising, showing that the proposed multispectral photogrammetric approach can provide accurate biomass estimates of grass swards, and could be developed as a low-cost tool for practical farming applications.

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Multi-task learning framework for predicting water quality using non-linear machine learning technique

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-212117 ◽

2021 ◽

pp. 1-13

Author(s):

D. Senthilkumar ◽

D. George Washington ◽

A.K. Reshmy ◽

M. Noornisha

Keyword(s):

Machine Learning ◽

Water Quality ◽

Prediction Model ◽

Quality Prediction ◽

Machine Learning Technique ◽

Water Quality Prediction ◽

Task Learning ◽

Learning Technique ◽

Non Linear ◽

Linear Machine

Predicting the quality of water is a very important issue in an ecosystem and it can be used to control the increase of water contamination. Also, water quality prediction is a prominent complex non-linear multi-target learning problem and extracting a relevant subset of features from a large number of features with multiple targets is a challenging task. Existing water quality prediction model not focused on multi-target learning process simultaneously and not identifying the non-linear relationship between the features and target variables. Therefore, this study proposes a multi-task learning method dealing with multi-target regression using non-linear machine learning technique. Finally, experiments are conducted to build a prediction model based on the proposed methods to evaluate accuracy on water quality dataset. The experimental results indicate that our method increases the overall accuracy of the experimental dataset compared with the existing methods with the reduced number of significant features.

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A machine learning approach for estimation of shallow water depths from optical satellite images and sonar measurements

Journal of Hydroinformatics ◽

10.2166/hydro.2013.234 ◽

2013 ◽

Vol 15 (4) ◽

pp. 1408-1424 ◽

Cited By ~ 10

Author(s):

Z. Vojinovic ◽

Y. A. Abebe ◽

R. Ranasinghe ◽

A. Vacher ◽

P. Martens ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Shallow Water ◽

Satellite Images ◽

Image Data ◽

Support Vector ◽

Machine Learning Technique ◽

High Resolution Satellite Images ◽

Svm Model ◽

Learning Technique

There has been a rapid growth in the field of remote sensing and its various applications in the area of water management. Nowadays, there are several remote sensing techniques that can be used as a source to derive bathymetry data along coastal areas. The key techniques are: sonar (sound navigating and ranging), LiDAR (light detection and ranging) and high-resolution satellite images. The present paper describes a method which was developed and used to create a shallow water bathymetry data along the Dutch side of Sint Maarten Island by combining sonar measurements and satellite images in a nonlinear machine learning technique. The purpose of this work is to develop a bathymetry dataset that can be used to set up physically-based models for coastal flood modelling work. The nonlinear machine learning technique used in the work is a support vector machine (SVM) model. The sonar data were used as an output whereas image data were used as an input into the SVM model. The results were analysed for three depth ranges and the findings are promising. It remains to further verify the capacity of the new method on a dataset with higher resolution satellite imagery.

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Prediction of the Concentration of Dissolved Oxygen in Running Water by Employing A Random Forest Machine Learning Technique

10.20944/preprints202004.0342.v1 ◽

2020 ◽

Author(s):

Mohammad Hafez Ahmed

Keyword(s):

Machine Learning ◽

Water Quality ◽

Random Forest ◽

Dissolved Oxygen ◽

Water Temperature ◽

Quality Data ◽

Linear Regression Method ◽

Machine Learning Technique ◽

Learning Technique ◽

Input Variables

Dissolved oxygen (DO) is a key indicator in the study of the ecological health of rivers. Modeling DO is a major challenge due to complex interactions among various process components of it. Considering the vital importance of it in water bodies, the accurate prediction of DO is a critical issue in ecosystem management. Given the intricacy of the current process-based water quality models, a data-driven model could be an effective alternative tool. In this study, a random forest machine learning technique is employed to predict the DO level by identifying its major drivers. Time-series of half-hourly water quality data, spanning from 2007 to 2019, for the South Branch Potomac River near Springfield, WV, are obtained from the United States Geological Survey database. Key drivers are identified, and models are formulated for different scenarios of input variables. The model is calibrated for each input scenario using 80% of the data. Water temperature and pH are found to be the most influential predictors of DO. However, satisfactory model performance is achieved by considering water temperature, pH, and specific conductance as input variables. The model validation is made by predicting DO concentrations for the remaining 20% of the data. The comparison with the traditional multiple linear regression method shows that the random forest model performs significantly better. The study insights are, therefore, expected to be useful to estimate stream/river DO levels at various sites with a minimum number of predictors and help build a sturdy framework for ecosystem health management across an environmental gradient.

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Performance evaluation of remote sensing data with machine learning technique to determine soil color

Polish Journal of Soil Science ◽

10.17951/pjss.2020.53.1.97 ◽

2020 ◽

Vol 53 (1) ◽

pp. 97

Author(s):

Laleh Parviz

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Correlation Coefficient ◽

Stepwise Regression ◽

Vegetation Index ◽

Remote Sensing Data ◽

Soil Color ◽

Machine Learning Technique ◽

Sensing Data ◽

Learning Technique

<p>The aim of the present research is the determination of soil color by spectral bands and indices obtained from MODIS images. For this purpose, soil samples were collected from East Azerbaijan Province (Iran) and their color and texture were investigated through Munsell color system and hydrometer method, respectively. Stepwise regression, principle component analysis and sensitivity function methods were employed to find the dominant indices and bands using artificial neural network (ANN) as one of the machine learning techniques. The improved indices as the model input had better performance, for example, the calculation of correlation coefficient between indices and hue showed 51.48% increase of correlation coefficient with comparison of the normalized difference vegetation index (NDVI) to modified soil adjustment vegetation index (MSAVI) and 54.54% correlation enhancement of soil adjustment vegetation index (SAVI) compared to MSAVI. Stepwise regression method along with error criteria decline may enhance the performance of soil color model. In comparison with multivariate regression, ANN model exhibited better performance (with a 12.61% mean absolute error [MAE] decline). Temporal variation of modified perpendicular drought index (MPDI) as well as band 31 could justify the Munsell soil color components variations specifically chroma and hue. MPDI and thermal bands could be employed as a precise indicator in soil color analysis. Thus, remote sensing data combined with machine learning technique can clarify the procedure potential for soil color determination.</p>

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What Should Investors Care About? Mutual Fund Ratings by Analysts vs. Machine Learning Technique

SSRN Electronic Journal ◽

10.2139/ssrn.3702749 ◽

2020 ◽

Author(s):

Si Cheng ◽

Ruichang Lu ◽

Xiaojun Zhang

Keyword(s):

Machine Learning ◽

Mutual Fund ◽

Machine Learning Technique ◽

Learning Technique

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The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽

10.3390/atmos11010111 ◽

2020 ◽

Vol 11 (1) ◽

pp. 111 ◽

Cited By ~ 2

Author(s):

Chul-Min Ko ◽

Yeong Yun Jeong ◽

Young-Mi Lee ◽

Byung-Sik Kim

Keyword(s):

Machine Learning ◽

Heavy Rainfall ◽

Extreme Rainfall ◽

Machine Learning Techniques ◽

Precipitation Forecast ◽

Machine Learning Technique ◽

Rainfall Forecast ◽

Quantitative Precipitation Forecast ◽

Correction Technique ◽

Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

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