An evolutionary fuzzy rule-based system applied to the prediction of soil organic carbon from soil spectral libraries

Tsakiridis, Nikolaos L.; Theocharis, John B.; Panagos, Panos; Zalidis, George C.

doi:10.1016/j.asoc.2019.105504

Cited by 35 publications

(6 citation statements)

References 49 publications

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“…As mentioned, our system can be classified into the XAI domain. Within this domain, FRBSs have demonstrated their potential to attain both interpretability and accuracy in a variety of application areas [8,23,24].…”

Section: Background and Related Workmentioning

confidence: 99%

Versatile Internet of Things for Agriculture: An eXplainable AI Approach

Tsakiridis

Diamantopoulos

Symeonidis

et al. 2020

IFIP Advances in Information and Communication Technology

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The increase of the adoption of IoT devices and the contemporary problem of food production have given rise to numerous applications of IoT in agriculture. These applications typically comprise a set of sensors that are installed in open fields and measure metrics, such as temperature or humidity, which are used for irrigation control systems. Though useful, most contemporary systems have high installation and maintenance costs, and they do not offer automated control or, if they do, they are usually not interpretable, and thus cannot be trusted for such critical applications. In this work, we design Vital, a system that incorporates a set of low-cost sensors, a robust data store, and most importantly an explainable AI decision support system. Our system outputs a fuzzy rule-base, which is interpretable and allows fully automating the irrigation of the fields. Upon evaluating Vital in two pilot cases, we conclude that it can be effective for monitoring open-field installations.

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Section: Background and Related Workmentioning

confidence: 99%

Versatile Internet of Things for Agriculture: An eXplainable AI Approach

Tsakiridis

Diamantopoulos

Symeonidis

et al. 2020

IFIP Advances in Information and Communication Technology

Self Cite

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“…Studies based on the type-1 fuzzy sets are like the type-1 fuzzy logic systems despite having many advantages in modeling real data [ 20 ]. In fact, the accuracy is often lower than that of fuzzy logic systems based on type-2 fuzzy sets and interval type-2 fuzzy sets [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Interval Type-2 Fuzzy Logic System and Ant Colony Optimization for Hydropower Dams Displacement Forecasting

Mai

Bui

Doan

2023

Int. J. Fuzzy Syst.

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Forecasting the dam displacement is an important problem in dam monitoring. By using multi-temporal monitoring data to make predictions and forecast dam structure health, it is possible to detect abnormalities over time of the dam so that remedial measures can be taken promptly to minimize risks. The paper proposes a method of applying the interval type-2 fuzzy logic system (IT2FLS) and ant colony optimization (ACO) technique to forecast hydroelectric dams’ displacement. The method consists of two stages: The first is to design an IT2FLS according to the input and output data with base rules and newly constructed membership functions. The second is to find the optimal parameters for an interval type-2 fuzzy logic system based on the ACO technique and give the final fuzzy forecasting system. Experimental data are measured from hydroelectricity monitoring data of Ialy, Gia Lai Province, Vietnam, in the period from 2004 to 2016. The proposed method is compared with some other methods to compare prediction efficiency and accuracy. The experimental results show that the proposed model has the best performance compared to other standard methods in both accuracy and stability of prediction. This shows that a predictive model based on the IT2FLS is a promising method and can be used for forecasting time-series data. Supplementary Information The online version contains supplementary material available at 10.1007/s40815-022-01452-3.

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“…To solve this issue, some new methods, such as local data‐driven, memory‐based learning (MBL) (Ramirez‐Lopez et al, 2013; Tziolas et al, 2019), and resampling approaches (Lobsey et al, 2017), have recently been proposed by some soil scientists. MBL builds a prediction model for each sample of the target site using spectrally similar samples from a reference spectral library and has presented promising results when applied to extremely complex spectral libraries, such as the libraries of the German Agricultural Soil Inventory (Jaconi et al, 2019) and of the European Union (Tsakiridis et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Strategies for efficient estimation of soil organic content at the local scale based on a national spectral database

Yang

Chen

et al. 2022

Land Degrad Dev

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Soil function degradation threatens the sustainable management of soil resources and soil organic matter (SOM) is a vital and important factor. Powerful measuring tools will become very important, especially in areas where data are poor or absent. The archive: China Soil Visible and Near Infrared (vis–NIR) Spectroscopy Library (CSSL) could help providea solution for less costly and fast measuring of SOM. The aim of this article was to compare SOM prediction performance according to three strategies: i) general global partial least squares regression (PLSR) using CSSL with and without spiking samples; ii) memory‐based learning (MBL) using CSSL with and without spiking samples; and iii) general PLSR using only spiking samples to predict soil organic matter in the target area. When using spiked subsets, we also investigated the prediction performance of the extra‐weighted (several copies) subsets. A series of spiking subsets were randomly selected from the total spiking samples, which were selected by conditioned Latin hypercube sampling (cLHS) from the target sites. We calculated only the mean squared Euclidean distance (msd) between the estimates density function (pds) of the principal components (PCs) of vis–NIR spectroscopy from the validation dataset and spiking subsets and statistically inferred the optimal sampling set size to be 30. Our study showed that global PLSR using CSSL spiked with the statistically optimal local samples can achieve higher predicted performance [with a mean root mean square error (RMSE) of 5.75]. MBL spiked with five extra‐weighted optimal spiking samples achieved the best accuracy with an RMSE of 3.98, an R2 of 0.70, a bias of 0.04, and an LCCC of 0.81. The msd is a simple and effective method to determine an adequate spiking set size using only vis–NIR data. These accurate predictions demonstrated the usefulness of statistically representative spiking and MBL for advanced large soil spectral libraries for SOM determination, which is currently lacking at large soil spectral libraries in use.

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An evolutionary fuzzy rule-based system applied to the prediction of soil organic carbon from soil spectral libraries

Cited by 35 publications

References 49 publications

Versatile Internet of Things for Agriculture: An eXplainable AI Approach

Versatile Internet of Things for Agriculture: An eXplainable AI Approach

Application of Interval Type-2 Fuzzy Logic System and Ant Colony Optimization for Hydropower Dams Displacement Forecasting

Strategies for efficient estimation of soil organic content at the local scale based on a national spectral database

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