Application of Artificial Intelligence in Geo-Engineering

Samui, Pijush

doi:10.1007/978-3-030-32029-4_3

Cited by 10 publications

(4 citation statements)

References 104 publications

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“…Of late, the media highlighted the significant spike in farmers' deaths in Punjab. is unfortunate trend of farmer's suicide is attributed to the restructuring of the agricultural system; crop failure, especially cotton in recent times; mounting debt; and joblessness [26][27][28]. e media report calls for deep introspection of agriculture-related laws besides looking for other causes that push farmers to the death trap.…”

Section: Issuesmentioning

confidence: 99%

Implementing Machine Learning for Smart Farming to Forecast Farmers’ Interest in Hiring Equipment

Rakhra

Sanober

Naveed

et al. 2022

Journal of Food Quality

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Farmers’ physical labor and debt are reduced as a result of agricultural automation, which emphasizes efficient and effective use of various machines in farming operations with the purpose of reducing physical labor and debt. It is a revolutionary idea in agriculture to create custom hiring centers, which are intended to make it easier for like-minded farmers to embrace technology/machinery for enhanced resource management practices. The study in question examines the significance of tool renting and sharing in the workplace. Rental and sharing equipment are two approaches that might be used to enable farmers to borrow equipment at a cheaper cost than they would otherwise have to pay for it. The following is a manual pilot study of 562 farmers in India to address the numerous challenges farmers face when looking for tools and equipment, as well as to determine their strong interest in the process of renting and sharing equipment. The study was conducted to address the numerous challenges farmers face when looking for tools and equipment and to determine their strong interest in the process of renting and sharing equipment. Farmers are divided into three groups according to the results of this poll: small, moderate, and large. Training and testing splits were used on the same data set in order to get a better understanding of the target variables. The data set for the survey was standardized in order to remove ambiguity. In this research, three different machine learning models were utilized: nearest neighbors, logistic regression, and decision trees. K-nearest neighbors was the most often used model, followed by logistic regression and decision trees. In order to get the best possible result, a comparison of the aforementioned algorithm models was carried out, which revealed that the decision tree is the better model among the others in this regard. Because the decision tree model is completely reliant on a large number of input factors, such as the kind of crop, the time/month of harvest, and the type of equipment necessary for the crops, it has the potential to have a social and economic impact on farmers and their livelihoods.

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Section: Issuesmentioning

confidence: 99%

Implementing Machine Learning for Smart Farming to Forecast Farmers’ Interest in Hiring Equipment

Rakhra

Sanober

Naveed

et al. 2022

Journal of Food Quality

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show abstract

“…Indirectly, climate change might also be mitigated through AI powered innovation, infrastructure, and industry (SDG9). Another potential application of AI is in the development of controversial-but feasible-geoengineering solutions that allow people to sidestep the need for substantial and radical changes in economic and political systems [69]. Vinuesa, et al [1] find a positive AI influence on 70% of the subgoals and a negative influence on 20% of the goals.…”

Section: Sdg13: Take Urgent Action To Combat Climate Change and Its Imentioning

confidence: 99%

AI in Context and the Sustainable Development Goals: Factoring in the Unsustainability of the Sociotechnical System

Sætra

2021

Sustainability

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Artificial intelligence (AI) is associated with both positive and negative impacts on both people and planet, and much attention is currently devoted to analyzing and evaluating these impacts. In 2015, the UN set 17 Sustainable Development Goals (SDGs), consisting of environmental, social, and economic goals. This article shows how the SDGs provide a novel and useful framework for analyzing and categorizing the benefits and harms of AI. AI is here considered in context as part of a sociotechnical system consisting of larger structures and economic and political systems, rather than as a simple tool that can be analyzed in isolation. This article distinguishes between direct and indirect effects of AI and divides the SDGs into five groups based on the kinds of impact AI has on them. While AI has great positive potential, it is also intimately linked to nonuniversal access to increasingly large data sets and the computing infrastructure required to make use of them. As a handful of nations and companies control the development and application of AI, this raises important questions regarding the potential negative implications of AI on the SDGs. The conceptual framework here presented helps structure the analysis of which of the SDGs AI might be useful in attaining and which goals are threatened by the increased use of AI.

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“…The HIgh REsolution Slope Stability Simulator (HIRESSS) was used to model the outcomes, and then it was compared to a conventional, non-IoT based technique. www.ijacsa.thesai.org Models for analysis of submerged pools [30], use of random field Gaussian methods [31], blocky structure systems [32], near-surface rock strength slope analysis [33], and reinforcement learning models [34][35][36][37] are also used for high efficiency slope analysis. It can be observed that most of these models do not define sensor placement analysis, due to which their efficiency is limited.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving Slope Stability in Open Cast Mines via Machine Learning based IoT Framework

Das¹,

Pani²,

Samal³

et al. 2022

IJACSA

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Slope stability has been a matter of concern for most geologists, mainly due to the fact that unstable slopes cause a greater number of accidents, which in turn reduces efficiency of mining operations. In order to reduce the probability of these slope instabilities, methods like tension crack mapping, inclinometer measurements, time domain reflectometry, borehole extensometers, piezometer, radar systems and image processing systems are deployed. These systems work efficiently for single site slope failures, but as the number of mining sites increase, dependency of one site slope failure on nearby sites also increases. Current systems are not able to capture this data, due to which the probability of accidents at open cast mines increases. In order to reduce this probability, a high efficiency internet of things (IoT) based continuous slope monitoring and control system is designed. This system assists in improving the efficiency of real-time slope monitoring via usage of a sensor array consisting of radar, reflectometer, inclinometer, piezometer and borehole extensometer. All these measurements are given to a high efficiency machine learning classifier which uses data mining, and based on its output suitable actions are taken to reduce accidents during mining. This information is dissipated to nearby mining sites in order to inform them about any inconsistencies which might occur due to the slope changes on the current site. Results were simulated using HIgh REsolution Slope Stability Simulator (HIRESSS), and an efficiency improvement of 6% is achieved for slope analysis in open cast mines, while probability of accident reduction is increased by 35% when compared to traditional non-IoT based approach.

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Application of Artificial Intelligence in Geo-Engineering

Cited by 10 publications

References 104 publications

Implementing Machine Learning for Smart Farming to Forecast Farmers’ Interest in Hiring Equipment

Implementing Machine Learning for Smart Farming to Forecast Farmers’ Interest in Hiring Equipment

AI in Context and the Sustainable Development Goals: Factoring in the Unsustainability of the Sociotechnical System

Improving Slope Stability in Open Cast Mines via Machine Learning based IoT Framework

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