Supervised Learning Algorithms for Famine Prediction

Okori, Washington; Obua, Joseph

doi:10.1080/08839514.2011.611930

Cited by 10 publications

(11 citation statements)

References 7 publications

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“…Data mining and Machine Learning (ML) techniques have been used in several studies as e cient tools for predicting and identifying the risk factors associated with food insecurity (29)(30)(31)(32) . K-Nearest Neighbor (KNN), Random Forest (RF), Logistic regression and Support Vector Machine (SVM) are among the machine learning models that have been assessed for the prediction of food insecurity (29,30,33) . Gao et al examined the machine learning models used to identify vulnerable household characteristics and to predict food-insecure households.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques for the Identification of Risk Factors Associated with Food Insecurity among Adults in Arab countries during the COVID-19 Pandemic

Qasrawi

Hoteit

Tayyem

et al. 2023

Preprint

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Background: A direct consequence of global warming, and strongly correlated with poor physical and mental health, food insecurity is a rising global concern associated with low dietary intake. The Coronavirus pandemic has further aggravated food insecurity among vulnerable communities, and thus has sparked the global conversation of equal food access, food distribution, and improvement of food support programs. This research was designed to identify the key features associated with food insecurity during the COVID-19 pandemic using Machine learning techniques. Seven machine learning algorithms were used in the model, which used a dataset of 32 features. The model was designed to predict food insecurity across ten Arab countries in the Gulf and Mediterranean regions. A total of 13,443 participants were extracted from the international Corona Cooking Survey conducted by 38 different countries during the COVID -19 pandemic. Results: The findings indicate that Jordanian, Palestinian, Lebanese, and Saudi Arabian respondents reported the highest rates of food insecurity in the region (15.4%,13.7%,13.7% and 11.3% respectively). On the other hand, Oman and Bahrain reported the lowest rates (5.4% and 5.5% respectively). Our model obtained accuracy levels of 70%-82% in all algorithms. Gradient Boosting and Random Forest techniques had the highest performance levels in predicting food insecurity (82% and 80% respectively). Place of residence, age, financial instability, difficulties in accessing food, and depression were found to be the most relevant features associated with food insecurity. Conclusions: Overall, ML algorithms seem to be an effective method in early detection and prediction of food insecurity. Future research would benefit from utilizing the proposed model in developing more complex and accurate models aiming to enhance granularity, with the ability to share data, to incorporate wide range of variables, and to make use of automation for effective prevention and intervention programs at the regional and individual levels.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques for the Identification of Risk Factors Associated with Food Insecurity among Adults in Arab countries during the COVID-19 Pandemic

Qasrawi

Hoteit

Tayyem

et al. 2023

Preprint

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“…The advancement of satellite and aerial imagery together with recent developments in the field of machine learning has broadened the scope of research. It has also allowed for fast and reliable ways of classifying large territories (Mahesh 2008;Okori and Obua 2011).…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Fast Learning Methods for Classifying Forest Cover Types

Sjöqvist

Längkvist

Javed

2020

Applied Artificial Intelligence

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Proper mapping and classification of Forest cover types are integral in understanding the processes governing the interaction mechanism of the surface with the atmosphere. In the presence of massive satellite and aerial measurements, a proper manual categorization has become a tedious job. In this study, we implement three different modest machine learning classifiers along with three statistical feature selectors to classify different cover types from cartographic variables. Our results showed that, among the chosen classifiers, the standard Random Forest Classifier together with Principal Components performs exceptionally well, not only in overall assessment but across all seven categories. Our results are found to be significantly better than existing studies involving more complex Deep Learning models.

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“…Machine Learning methods are increasingly used to extract relevant information from complex and heterogeneous FS-related data, and several studies have attempted to detect food insecurity and crisis using machine learning techniques [14,1,10] with encouraging but improving results. A group of machine learning methods called deep learning is increasingly being used and is very effective in analysing complex and heterogeneous data [7].…”

Section: Introductionmentioning

confidence: 99%

Linking Heterogeneous Data for Food Security Prediction

Deléglise

Bégué

Interdonato

et al. 2020

ECML PKDD 2020 Workshops

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Identifying food insecurity situations timely and accurately is a complex challenge. To prevent food crisis and design appropriate interventions, several food security warning and monitoring systems are very active in food-insecure countries. However, the limited types of data selected and the limitations of data processing methods used make it difficult to apprehend food security in all its complexity. In this work, we propose models that aim to predict two key indicators of food security: the food consumption score and the household dietary diversity score. These indicators are time consuming and costly to obtain. We propose using heterogeneous data as explanatory variables that are more convenient to collect. These indicators are calculated using data from the permanent agricultural survey conducted by the Burkinabe government and available since 2009. The proposed models use deep and machine learning methods to obtain an approximation of food security indicators from heterogeneous explanatory data. The explanatory data are rasters (population densities, rainfall estimates, land use, etc.), GPS points (of hospitals, schools, violent events), quantitative economic variables (maize prices, World Bank variables), meteorological and demographic variables. A basic research issue is to perform pre-processing adapted to each type of data and then to find the right methods and spatio-temporal scale to combine them. This work may also be useful in an operational approach, as the methods discussed could be used by food security warning and monitoring systems to complement their methods to obtain estimates of key indicators a few weeks in advance and to react more quickly in case of famine.

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Supervised Learning Algorithms for Famine Prediction

Cited by 10 publications

References 7 publications

Machine Learning Techniques for the Identification of Risk Factors Associated with Food Insecurity among Adults in Arab countries during the COVID-19 Pandemic

Machine Learning Techniques for the Identification of Risk Factors Associated with Food Insecurity among Adults in Arab countries during the COVID-19 Pandemic

An Analysis of Fast Learning Methods for Classifying Forest Cover Types

Linking Heterogeneous Data for Food Security Prediction

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