A review of drought monitoring with big data: Issues, methods, challenges and research directions

Balti, Hanen; Abbes, Ali Ben; Mellouli, Nédra; Farah, Imed Riadh; Sang, Yan‐Fang; Lamolle, Myriam

doi:10.1016/j.ecoinf.2020.101136

Cited by 63 publications

(27 citation statements)

References 88 publications

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“…As a forecasting method, we will choose extrapolation since the internal transportation of sugar is characterized by the time and volume of the transported cargo. Many models allow forecasting with varying degrees of accuracy: correlation-regression analysis [4][5][6][7], neural network models [8,9], research-based on multiple regression [10][11][12], models based on classification-regression trees [13,14], maximum likelihood sampling models [15][16][17] and many others [18][19][20][21]. For forecasting economic time series, models of the ARIMA class are used [22].…”

Section: Justification Of the Choice Of Methods For Solving The Problemmentioning

confidence: 99%

Analysis of the Forecast Price as a Factor of Sustainable Development of Agriculture

et al. 2021

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Analysis of the rise in prices for consumer goods is a state’s priority task. The state assumes the obligation to regulate pricing in all spheres of consumption. First of all, the prices for essential commodities to which agricultural products belong are analyzed. The article shows the changes in prices for consumer goods of agricultural products (sugar) during a pandemic. The analysis of forecasting prices for sugar and its impact on the development of its production is carried out. The construction of the forecast model was based on extrapolation. The structure of a forecast model for price changes was based on the analysis of the time series of the Autoregressive Integrated Moving Average (ARIMA) class. This model consists of an autoregressive model and a moving average model. A forecast of the volume of domestic sugar transportation by rail has been completed. The algorithms implemented this model for searching for initial approximations and optimal parameters for the predictive model. The Hirotsugu Akaike Information Criterion (AIC) was used to select the best model. The algorithms were implemented in the Python programming language. The quality check of the description was performed with a predictive model of actual data. An economic interpretation of the rise in sugar prices and proof of the forecast’s truth obtained from a financial point of view were carried out.

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Section: Justification Of the Choice Of Methods For Solving The Problemmentioning

confidence: 99%

Analysis of the Forecast Price as a Factor of Sustainable Development of Agriculture

et al. 2021

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“…This paper focuses on optical and thermal remote sensing, whereas many other data sources have also been utilized in agricultural drought monitoring [288]. These include microwave remote sensing, LiDAR, gravity remote sensing (e.g., GRACE), and other nonremote-sensing data sources.…”

Section: Organic Combination With Other Data Sourcesmentioning

confidence: 99%

Optical and Thermal Remote Sensing for Monitoring Agricultural Drought

Qin

Zhang

et al. 2021

Remote Sensing

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By effectively observing the land surface and obtaining farmland conditions, satellite remote sensing has played an essential role in agricultural drought monitoring over past decades. Among all remote sensing techniques, optical and thermal remote sensing have the most extended history of being utilized in drought monitoring. The primary goal of this paper is to illustrate how optical and thermal remote sensing have been and will be applied in the monitoring, assessment, and prediction of agricultural drought. We group the methods into four categories: optical, thermal, optical and thermal, and multi-source. For each category, a concise explanation is given to show the inherent mechanisms. We pay special attention to solar-induced chlorophyll fluorescence, which has great potential in early drought detection. Finally, we look at the future directions of agricultural drought monitoring, including (1) early detection; (2) spatio-temporal resolution; (3) organic combination of multi-source data; and (4) smart prediction and assessment based on deep learning and cloud computing.

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“…al., (2019); Salleh and Janczewski, (2019);Baig et. al., (2019); Knowles, (2020) ;Balti et. al., (2020);Bag et.…”

Section: Figure 1: Essential Elements Of Big Datamentioning

confidence: 99%

Challenges faced by the adoption of big data in the Dominican Republic construction industry: an empirical study

Reyes-Veras¹,

Renukappa²,

Suresh³

2021

ITcon

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The adoption of Big Data (BD) in the construction industry has been identified as a possible solution to the demand of the current needs of projects, but the integration of this technology has proven to be a challenge specially in industries such as construction that are not technological driven. The understanding of the key elements for the BD adoption has become the focus of many industries that seek to exploit the benefits offered by this technology. Therefore, this paper aims to explore the challenges faced by the adoption of BD in the Dominican Republic (DR) construction industry. To identify these challenges qualitative research was undertaken due to the paucity of scientific data. Twenty-one individuals representing 19 companies who have great impact in the DR construction sector were interviewed. From the analysis six main challenges were identified. They are: lack BD awareness, high cost of investment, resistance to change, lack of government support and regulation, lack of technological expertise, and security concerns of BD. The challenges identified in this study, will serve to help companies better plan their technology adoption process, mainly considering aspects such as the need to tackle the lack of awareness by disseminating and promoting the concept of BD which will not only generate a better understanding of technology by making sure that present and future professionals understand the technology and its benefits. This study provides insight in the challenges to overcome for a successful adoption of BD technology, which would help companies to prepare for a future adoption.

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A review of drought monitoring with big data: Issues, methods, challenges and research directions

Cited by 63 publications

References 88 publications

Analysis of the Forecast Price as a Factor of Sustainable Development of Agriculture

Analysis of the Forecast Price as a Factor of Sustainable Development of Agriculture

Optical and Thermal Remote Sensing for Monitoring Agricultural Drought

Challenges faced by the adoption of big data in the Dominican Republic construction industry: an empirical study

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