Multi-Dimensional Data Preparation: A Process to Support Vulnerability Analysis and Climate Change Adaptation

López, Iván Darío; Casas, Apolinar Figueroa; Corrales, Juan Carlos

doi:10.1109/access.2020.2992255

Cited by 9 publications

(8 citation statements)

References 19 publications

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“…It is composed of three phases: data sources evaluation, data sources pre-processing, and variables prioritization. The complete data preparation process is detailed in [44].…”

Section: B Data Assessment (Level 0)mentioning

confidence: 99%

“…We considered several data sources as the fundamental input of this study. In this case, we extracted 16 datasets from different web portals of official public organizations (details about these data sources can be consulted in [44]). We also used the results of previous climate vulnerability assessments developed at the UCRB as inputs.…”

Section: B Data Sourcesmentioning

confidence: 99%

“…Finally, we identify the most relevant attributes of each data source. The complete and detailed data sources evaluation (level 0) is referenced in [44].…”

Section: Data Assessment (Level 0)mentioning

confidence: 99%

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Multi-Label Data Fusion to Support Agricultural Vulnerability Assessments

2021

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Identifying crop species and varieties adaptable to climate change impacts is one of the main aspects of climate vulnerability assessments. This estimation involves processing, integrating, and analyzing many information sources to provide accurate and timely responses. However, designing this evaluation, examine the information gathered, and reaching agreements among all stakeholders and experts, often requires considerable effort in time, money, and people. In this study, we propose a data fusion strategy to support climate vulnerability assessments by identifying the adaptability of crops in a territory in the short term. This strategy follows the Joint Directors of Laboratories' data fusion model guidelines. It was evaluated and validated through a case study in Colombia's upper Cauca river basin. For this purpose, we identified Climate, Soil, Water Quality, Productive Alliances, and Production as the most relevant data sources to be integrated, and using metrics such as Mean IR, SCUMBLE, TCS, among others, we evaluated the combined datasets according to their theoretical complexity. The adaptability of crops in a territory was addressed as a multi-label learning problem, assessing the performance of different multi-label classification and multi-view multi-label classification models with both test and actual data. Comparing the predicted crops with the actual ones, we obtained a 98% similarity without considering crop ranking using the Binary Relevance approach and the Random Forest and XGBoost algorithms. Using a more exhaustive test involving order, we obtained a maximum similarity of 67% applying Binary Relevance and Random Forest.

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“…It is composed of three phases: data sources evaluation, data sources pre-processing, and variables prioritization. The complete data preparation process is detailed in [44].…”

Section: B Data Assessment (Level 0)mentioning

confidence: 99%

Section: B Data Sourcesmentioning

confidence: 99%

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Multi-Label Data Fusion to Support Agricultural Vulnerability Assessments

2021

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“…However, as in any datadriven process, it is possible to find problems in data quality such as duplicate information, missing values, differences in spatial-temporal scales, noise, among others. To address these and other issues, in López et al (2020), we established a data preparation process to support climate vulnerability assessments. Table 1 provides an overview of data sources classified by dimension.…”

Section: Overview Of the Data Analytics Approachmentioning

confidence: 99%

“…To address these and other issues, in López et al. (2020), we established a data preparation process to support climate vulnerability assessments. Table 1 provides an overview of data sources classified by dimension.…”

Section: Overview Of the Data Analytics Approachmentioning

confidence: 99%

A proposal for a multi‐domain data fusion strategy in a climate‐smart agriculture context

López

Grass

Casas

et al. 2020

Int Trans Operational Res

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Agriculture provides food, raw materials, and employment opportunities for a significant percentage of the world's population. Climate, economic, political, social, and other conditions affect decision making in agricultural processes. In many cases, these conditions imply the loss of suitability of many areas for some traditional crops. In contrast, these areas can produce new crops by taking advantage of changing conditions. In this sense, having reliable tools and information for decision making is essential in adapting to new agricultural productivity scenarios. The above implies having sufficient and relevant data sources to reduce the uncertainty in the decision‐making processes. However, data by nature tend to be diverse in structure, storage formats, and access protocols. Data fusion tasks have been immersed in a multitude of applications and have been approached from different points of view when implementing a suitable solution. We propose a multi‐domain data fusion strategy to support data analysis tasks in agricultural contexts. We also describe all the data sources collected, which are the main input to the proposed strategy. The combined data sources were also evaluated through a preliminary exploratory analysis in a multi‐label learning approach. Finally, the data fusion strategy is explained through an example in agricultural crop production.

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