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

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>

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Noisy Environments ◽  
Satisfactory Performance ◽  
Feature Values ◽  
Learning Machine ◽  
Stockwell Transform ◽  
Electrical Utilities

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.

Sign in / Sign up

Export Citation Format

Share Document