Development of Dissolved Oxygen Forecast Model Using Hybrid Machine Learning Algorithm with Hydro-Meteorological Variables

2021 ◽  
Author(s):  
Abul Abrar Masrur Ahmed ◽  
M A I Chowdhury ◽  
Oli Ahmed ◽  
Ambica Sutradhar
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Learning Algorithm ◽  
Data Extraction ◽  
Forecast Model ◽  
Multivariate Adaptive Regression Splines ◽  
Meteorological Variables ◽  
Discrete Wavelet ◽  
Input Selection

Abstract The ability to predict dissolved oxygen, which is a critical water quality (WQ) parameter, is critical for aquatic managers responsible for maintaining ecosystem health and the management of reservoirs affected by WQ. This paper reports forecasting dissolved oxygen (DO) concentration using multivariate adaptive regression splines (MARS) of running river water using a set of water quality and hydro-meteorological variables. This study’s key objectives were to assess input selection methods and five multi-resolution analyses as a data extraction approach. Moreover, the hybrid model is prepared by maximum overlap discrete wavelet transformation (MODWT) with the MARS model (i.e., MODWT-MARS). The proposed model is further compared with numerous machine learning methods. The result shows that the hybrid algorithms (i.e., MODWT-MARS) outperformed the other models (r = 0.981, WI = 0.990, RMAE = 2.47% and MAE = 0.089). This hybrid method may serve as the foundation for forecasting water quality variables with fewer predictor variables.

Developing a Machine Learning Algorithm to Identify Hot Moments Using Mass Balance Approach at Watershed Scale

2021 ◽  
Author(s):  
Thakshajini Thaasan ◽  
Phung Quang ◽  
Noel Aloysius
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Mass Balance ◽  
Crop Yield ◽  
Land Management ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Watershed Scale ◽  
Relative Change ◽  
Mass Balance Approach

<p>Preserving and promoting the sustainable use of natural resources while stabilizing healthy ecosystems under rapid environmental changes is a tremendous challenge for the international community. Science-based strategies are imperative to maintain and improve Earth’s ecosystem. Our research is designed to improve predictive ability of managed ecosystems’ responses to changing weather patterns and human management. Specifically, our research seeks to develop conservation plans to improve water quality in streams and lakes, while maintaining the economic sustainability of food production systems. Reducing pollution loading into aquatic systems help improve the water quality and enhance ecosystem sustainability. Non-point pollution sources are predominant factors in increasing pollution into the water bodies. Identifying the pollution sources is important to mitigate the impact. For this reason, the main objective of our study is to identify the “hot spots” and “hot moments” of excessive nitrogen and phosphorus leaching from managed landscapes in the midwestern United States.</p><p>We developed a simple lumped model with three parameters to simulate key water fluxes - surface and subsurface runoff, and evapotranspiration (ET) in the Maumee River Basin. We designed a machine learning algorithm to identify “hot moments” using nitrogen mass balance approach at watershed-scale. The simple model helps to link the relationship between applied fertilizer and retained nutrients in the soil that the heterogeneous landscape and land management influence. Nitrogen retained in the soil will be used as an output variable and connected with predictor variable ET. Relationships between crop yield and water use in crop growth (ET) could be interpreted in a simple empirical formulation where relative change in crop yield is related to the corresponding relative change in ET, which can be expressed as,</p><p>1−𝑌<sub>𝑎</sub>/ 𝑌<sub>𝑥</sub>=𝐾<sub>𝑦</sub> (1− 𝐸𝑇<sub>𝑥</sub>/𝐸𝑇<sub>𝑎</sub>)</p><p>where Yx and Ya are the maximum and actual yields, ETx and ETa are the maximum and actual evapotranspiration, and Ky is a yield response factor representing the effect of relative change in ET on crop yield. The developed algorithm will be trained, tested, and validated using the coupled water flux and crop yield models. We will then demonstrate how these relationships can be extended to complex watershed model simulations that account for key land management decisions, land use pattern, crop type, soil, and topographic variability. Ultimately, we hope our findings will enhance the knowledge related to the environmental policy and decision making.</p>

Characterizing water quality and quantity profiles with poor quality data in a machine learning algorithm

2020 ◽  
Vol 182 ◽  
pp. 127-134
Author(s):  
Zhonghyun Kim ◽  
Heewon Jeong ◽  
Sora Shin ◽  
Jinho Jung ◽  
Joon Ha Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Learning Algorithm ◽  
Poor Quality ◽  
Quality Data ◽  
Machine Learning Algorithm ◽  
Poor Quality Data ◽  
Water Quality And Quantity

Extraction of actionable information from crowdsourced disaster data

2016 ◽  
Vol 14 (6) ◽  
pp. 377 ◽  
Author(s):  
Rungsun Kiatpanont, MS ◽  
Uthai Tanlamai, PhD ◽  
Prabhas Chongstitvatana, PhD
Keyword(s):  
Machine Learning ◽  
Disaster Management ◽  
Learning Algorithm ◽  
Data Extraction ◽  
Extraction Process ◽  
Support Vector ◽  
Test Case ◽  
Crowdsourced Data ◽  
Disaster Data ◽  
Percent Accuracy

Natural disasters cause enormous damage to countries all over the world. To deal with these common problems, different activities are required for disaster management at each phase of the crisis. There are three groups of activities as follows: (1) make sense of the situation and determine how best to deal with it, (2) deploy the necessary resources, and (3) harmonize as many parties as possible, using the most effective communication channels. Current technological improvements and developments now enable people to act as real-time information sources. As a result, inundation with crowdsourced data poses a real challenge for a disaster manager. The problem is how to extract the valuable information from a gigantic data pool in the shortest possible time so that the information is still useful and actionable. This research proposed an actionable-data-extraction process to deal with the challenge. Twitter was selected as a test case because messages posted on Twitter are publicly available. Hashtag, an easy and very efficient technique, was also used to differentiate information.A quantitative approach to extract useful information from the tweets was supported and verified by interviews with disaster managers from many leading organizations in Thailand to understand their missions. The information classifications extracted from the collected tweets were first performed manually, and then the tweets were used to train a machine learning algorithm to classify future tweets. One particularly useful, significant, and primary section was the request for help category. The support vector machine algorithm was used to validate the results from the extraction process of 13,696 sample tweets, with over 74 percent accuracy. The results confirmed that the machine learning technique could significantly and practically assist with disaster management by dealing with crowdsourced data.

scholarly journals Machine learning for manually-measured water quality prediction in fish farming

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256380
Author(s):  
Andres Felipe Zambrano ◽  
Luis Felipe Giraldo ◽  
Julian Quimbayo ◽  
Brayan Medina ◽  
Eduardo Castillo
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Random Forests ◽  
Prediction Models ◽  
Fish Farming ◽  
Time Data ◽  
Water Quality Prediction ◽  
Water Pond ◽  
Measured Water

Monitoring variables such as dissolved oxygen, pH, and pond temperature is a key aspect of high-quality fish farming. Machine learning (ML) techniques have been proposed to model the dynamics of such variables to improve the fish farmer’s decision-making. Most of the research on ML in aquaculture has focused on scenarios where devices for real-time data acquisition, storage, and remote monitoring are available, making it easy to develop accurate ML techniques. However, fish farmers do not necessarily have access to such devices. Many of them prefer to use equipment to manually measure these variables limiting the amount of available data to process. In this work, we study the use of random forests, multivariate linear regression, and artificial neural networks in scenarios with limited amount of measurements to analyze data from water-quality variables that are commonly measured in fish farming. We propose a methodology to build models in two scenarios: i) estimation of unobserved variables based on the observed ones, and ii) forecasting when a low amount of data is available for training. We show that random forests can be used to forecast dissolved oxygen, pond temperature, pH, ammonia, and ammonium when the water pond variables are measured only twice per day. Moreover, we showed that these prediction models can be implemented on a mobile-based information system and run in an average smartphone that fish farmers can afford.

scholarly journals Spatial Forecasting of Dissolved Oxygen Concentration in the Eastern Black Sea Basin, Turkey

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1041
Author(s):  
Sinan Nacar ◽  
Adem Bayram ◽  
Osman Tugrul Baki ◽  
Murat Kankal ◽  
Egemen Aras
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Dissolved Oxygen ◽  
Black Sea ◽  
Surface Water Quality ◽  
Absolute Error ◽  
Multivariate Adaptive Regression Splines ◽  
Linear Functions ◽  
Exponential Power ◽  
Teaching Learning

The aim of this study was to model, as well as monitor and assess the surface water quality in the Eastern Black Sea (EBS) Basin stream, Turkey. The water-quality indicators monitored monthly for the seven streams were water temperature (WT), pH, total dissolved solids (TDS), and electrical conductivity (EC), as well as luminescent dissolved oxygen (LDO) concentration and saturation. Based on an 18-month data monitoring, the surface water quality variation was spatially and temporally evaluated with reference to the Turkish Surface Water Quality Regulation. First, the teaching–learning based optimization (TLBO) algorithm and conventional regression analysis (CRA) were applied to three different regression forms, i.e., exponential, power, and linear functions, to predict LDO concentrations. Then, the multivariate adaptive regression splines (MARS) method was employed and three performance measures, namely, mean absolute error (MAE), root means square error (RMSE), and Nash Sutcliffe coefficient of efficiency (NSCE) were used to evaluate the performances of the MARS, TLBO, and CRA methods. The monitoring results revealed that all streams showed the same trend in that lower WT values in the winter months resulted in higher LDO concentrations, while higher WT values in summer led to lower LDO concentrations. Similarly, autumn, which presented the higher TDS concentrations brought about higher EC values, while spring, which presented the lower TDS concentrations gave rise to lower EC values. It was concluded that the water quality of the streams in the EBS basin was high-quality water in terms of the parameters monitored in situ, of which the LDO concentration varied from 9.13 to 10.12 mg/L in summer and from 12.31 to 13.26 mg/L in winter. When the prediction accuracies of the three models were compared, it was seen that the MARS method provided more successful results than the other methods. The results of the TLBO and the CRA methods were very close to each other. The RMSE, MAE, and NSCE values were 0.2599 mg/L, 0.2125 mg/L, and 0.9645, respectively, for the best MARS model, while these values were 0.4167 mg/L, 0.3068 mg/L, and 0.9086, respectively, for the best TLBO and CRA models. In general, the LDO concentration could be successfully predicted using the MARS method with various input combinations of WT, EC, and pH variables.

Wavelet Based Machine Learning Technique to Classify the Different Shoulder Movement of Upper Limb Amputee

2017 ◽  
Vol 31 ◽  
pp. 32-43 ◽  
Author(s):  
Amanpreet Kaur ◽  
Amod Kumar ◽  
Ravinder Agarwal
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Random Forest ◽  
Upper Limb ◽  
Learning Algorithm ◽  
Discrete Wavelet ◽  
Semg Signal ◽  
Shoulder Movement ◽  
Fundamental Information

The wavelet transform is an accurate, efficient and efficacious method to improve the quality of the myoelectric signal. Classification of the signal from upper limb using Surface Electromyogram (SEMG) signal has been the matter of extensive research. Number of methods and algorithms have been described by researchers to classify biomedical signals. The main aim of this paper to extract the different coefficient values from the given SEMG data by using Discrete Wavelet Transform (DWT). Afterward, random forest machine learning algorithm was used to identify the different shoulder movement of an upper limb amputee. The combination of wavelet coefficients and random forest exhibited the best performance with 99.2% accuracy for the classification of different shoulder motions. It was found that the different motion can be identified accurately and provide the fundamental information to develop an efficient prosthetic device.

Sign in / Sign up

Export Citation Format

Share Document