The potential evaluation of groundwater by integrating rank sum ratio (RSR) and machine learning algorithms in the Qaidam Basin

Wang, Zitao; Wang, Jianping; Yu, Dongmei; Chen, Kai

doi:10.1007/s11356-023-26961-y

Cited by 12 publications

(1 citation statement)

References 64 publications

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“…Fuzzy comprehensive evaluation models are more suitable for quantifying the extent to which the absorption spectral properties of gases are influenced. Various evaluation models are commonly used, including hierarchical process [30], Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method [31], data envelopment analysis [32], rank sum ratio method [33], fuzzy comprehensive evaluation [34], and grey relation analysis [35]. Using terms like "large" or "small" to describe the degree of disturbance to an absorption characteristic is insufficient.…”

Section: Introductionmentioning

confidence: 99%

An Analysis Method of Gas Absorption Spectrum Characteristics Based on Fuzzy Comprehensive Evaluation

Wu,

Li,

Rao

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Abstract-The growing demand for atmospheric environment monitoring requires more precise analysis of gas absorption spectral characteristics and simulation of radiation transfer within the absorption window. However, there is still a lack of clarity regarding the methods and technical details for extracting, analyzing absorption spectral characteristics, and simulating radiative transfer. This paper proposes a fuzzy comprehensive evaluation method to analyze the impact on the absorption spectral characteristics of a gas and constructs a technical framework called Spectrum Characteristics Analysis Technical Framework (SCATF) for atmospheric remote sensing detection. Firstly, this paper investigates the extraction method of the absorption window based on the HITRAN database. Then, it establishes the Improved Fuzzy Comprehensive Evaluation model (IFCE) for evaluating the absorption channel. Subsequently, an improved fuzzy comprehensive evaluation model, IFCE-α, based on the monitoring factor α, is proposed for evaluating the absorption window. Next, the radiative transfer simulation and inversion error analysis process for the absorption window was established based on the SCIATRAN model. Lastly, the feasibility of the SCATF framework is verified by using atmospheric CO2 as an illustrative example. Based on the experimental results, the SCATF framework demonstrates the ability to accurately extract the absorption characteristics of a specific gas, evaluate the extent of interference in both the absorption window and channel, and offer advanced insights into the performance of the absorption window in remote sensing detection. This framework introduces a novel approach for designing detection bands for atmospheric remote sensing.

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