Forecasting Amazon Rain-Forest Deforestation Using a Hybrid Machine Learning Model

The present work aims to carry out an analysis of the Amazon rain-forest deforestation, which can be analyzed from actual data and predicted by means of artificial intelligence algorithms. A hybrid machine learning model was implemented, using a dataset consisting of 760 Brazilian Amazon municipalities, with static data, namely geographical, forest, and watershed, among others, together with a time series data of annual deforestation area for the last 20 years (1999–2019). The designed learning model combines dense neural networks for the static variables and a recurrent Long Short Term Memory neural network for the temporal data. Many iterations were performed on augmented data, testing different configurations of the regression model, for adjusting the model hyper-parameters, and generating a battery of tests to obtain the optimal model, achieving a R-squared score of 87.82%. The final regression model predicts the increase in annual deforestation area (square kilometers), for a decade, from 2020 to 2030, predicting that deforestation will reach 1 million square kilometers by 2030, accounting for around 15% compared with the present 1%, of the between 5.5 and 6.7 millions of square kilometers of the rain-forest. The obtained results will help to understand the impact of man’s footprint on the Amazon rain-forest.

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Computationally Efficient and Effective Machine Learning Model Using Time Series Data in Different Prediction Problems

Advances in Data Mining and Database Management - Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science ◽

10.4018/978-1-7998-6659-6.ch012 ◽

2021 ◽

pp. 218-233

Author(s):

Dwiti Krishna Bebarta ◽

Birendra Biswal

Keyword(s):

Machine Learning ◽

Predictive Models ◽

Time Series Data ◽

Predictive Analytics ◽

Learning Model ◽

Series Data ◽

Computationally Efficient ◽

Machine Learning Model ◽

High Degree ◽

Prediction Problems

Automated feature engineering is to build predictive models that are capable of transforming raw data into features, that is, creation of new features from existing ones on various datasets to create meaningful features and examining their effect on planned model performances on various parameters like accuracy, efficiency, and prevent data leakage. So the challenges for experts are to plan computationally efficient and effective machine, learning-based predictive models. This chapter will provide an imminent to the important intelligent techniques that could be utilized to enhance predictive analytics by using an advanced form of the predictive model. A computationally efficient and effective machine learning model using functional link artificial neural network (FLANN) is discussed to design for predicting the business needs with a high degree of accuracy for the traders or investors. The performance of the models using FLANN is encouraging when scientifically analyzed the experimental results of the model using different statistical analyses.

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A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19

Applied Sciences ◽

10.3390/app11083561 ◽

2021 ◽

Vol 11 (8) ◽

pp. 3561

Author(s):

Diego Duarte ◽

Chris Walshaw ◽

Nadarajah Ramesh

Keyword(s):

Time Series ◽

Time Series Data ◽

Moving Average ◽

General Regression Neural Network ◽

Series Data ◽

Machine Learning Model ◽

Hospital Patients ◽

Major Incidents ◽

General Regression ◽

The Impact

Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department; Number of Attendances; Unallocated Patients with a DTA (Decision to Admit); Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.

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