Quantum Recurrent Neural Networks for Soil Profiles Prediction in Turkiye

被引:0
作者
Drias, Yassine [1 ]
Siouane, Alaa Eddine [2 ]
Cakar, Tuna [1 ]
机构
[1] MEF Univ, Istanbul, Turkiye
[2] Univ Algiers, Algiers, Algeria
来源
QUANTUM COMPUTING: APPLICATIONS AND CHALLENGES, QSAC 2023 | 2024年 / 2卷
关键词
Quantum Computing; Recurrent Neural Networks; Quantum Machine Learning; Digital Soil Mapping;
D O I
10.1007/978-3-031-59318-5_10
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In this article, we introduce a new approach for soil profile prediction using Quantum Recurrent Neural Networks (QRNNs). By harnessing the power of quantum computing, QRNNs present a promising solution to overcome the limitations of conventional soil mapping techniques. We begin by proposing a classical Recurrent Neural Networks (RNNs) architecture for soil profiles prediction, followed by the design of its quantum counterpart with QRNNs. Focusing on the application of our model in Turkiye, we leverage geospatial data from diverse sources, including climate, vegetation, and land relief data, to showcase the efficacy of QRNNs in soil classification and resource monitoring. Our results reveal a remarkable accuracy score and computational efficiency. Moreover, we delve into the broader implications of quantum computing for digital mapping and explore potential avenues for future research. Emphasizing the integration of quantum computing techniques with digital soil mapping, we foresee a promising advancement in sustainable soil management practices, aiding decision-making processes in agriculture, environmental planning, and natural resource management.
引用
收藏
页码:120 / 133
页数:14
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