Groundwater quality for irrigation in an arid region—application of fuzzy logic techniques

Dhaoui, Oussama; Agoubi, Belgaçem; Antunes, I.M.H.R.; Tlig, Lotfi; Kharroubi, Adel

doi:10.1007/s11356-022-24334-5

Cited by 20 publications

(4 citation statements)

References 117 publications

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“…FL is a reasoning method using approximate categories, not accuracy categories [8]. FL will only provide Boolean logic answers between true (1) or false (0), other answers such as almost false or almost true are not answers that comply with Boolean logic [9], [10].…”

Section: A Fuzzy Logic (Fl)mentioning

confidence: 99%

A Review: Artificial Intelligence Related to Agricultural Equipment Integrated with the Internet of Things

Wildan

2023

JATM

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Abstract—The development of modern technology has brought progress to the agricultural sector. Previously, farming was carried out using traditional methods, resulting in lower crop production. Now the world is faced with various problems, there are challenges such as climate fluctuations and increasing human population. This problem causes food needs to increase drastically, so adopting Industry 4.0 technology in the agricultural sector is necessary. Artificial Intelligence (AI) and Internet of Things (IoT) are part of industrial technology advances 4.0 that can be applied to modern agriculture. This paper reviews several AI technologies used in the agricultural sector, such as Fuzzy Logic (FL), Artificial Neural Network (ANN), Machine Learning (ML), Deep Learning (DL), Genetic Algorithm (GA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Support System (DSS). The application form of integration between AI and IoT is divided into several categories: soil monitoring, agricultural irrigation, fertilizer spraying, pest and plant disease control, harvesting, forecasting, and yield monitoring. This review paper was created to provide a comprehensive overview of modern agriculture integrating AI and IoT. This form of application makes it possible to predict the future of agriculture so that it can manage resources more efficiently and run autonomously. This review aims to analyze and explore the latest developments in integrating AI and IoT in agricultural equipment in the period 2019 to 2023. Thus, it is hoped that this article can provide in-depth insight into future agricultural technology advances. Keywords—Artificial Intelligence (AI), Internet of Things (IoT), Agriculture, Integration of AI and IoT, Smart farming.

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Section: A Fuzzy Logic (Fl)mentioning

confidence: 99%

A Review: Artificial Intelligence Related to Agricultural Equipment Integrated with the Internet of Things

Wildan

2023

JATM

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“…Hence, it could be inferred that fuzzy logic techniques have been very recently adopted in studying the irrigation suitability of groundwater, as the oldest work in the database was reported by Dixon [36] for groundwater vulnerability assessment, followed by studies with an emphasis on irrigation systems and GIS. Recently, more studies on fuzzy logic have aimed to unravel the ISGW and focused on drinking water purposes by involving more related parameters through the analytic hierarchical process (AHP) [32,37,38].…”

Section: Introductionmentioning

confidence: 99%

Groundwater Management for Agricultural Purposes Using Fuzzy Logic Technique in an Arid Region

Al-Rashidi,

Sabarathinam,

Samayamanthula

et al. 2023

Water

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The study aimed to determine groundwater’s suitability for irrigation and cattle rearing in Kuwait. In this regard, groundwater samples were collected from Umm Al Aish (UA) and adjoining Rawdhatain (RA) water wellfields to develop groundwater suitability maps for irrigation purposes using the fuzzy logic technique in ArcGIS. RA was dominated by Na-Cl, Na-Ca, and Ca-SO4 water types, whereas UA was dominated by the Ca-Mg water type. Due to the influence of the temperature and pCO2, the carbonates were inferred to be more susceptible to precipitation in the soil than the sulfates. The ternary plots for both regions revealed that the samples’ suitability ranged from good to unsuitable. Spatial maps of nine significant parameters governing the irrigation suitability of water were mapped and integrated using the fuzzy membership values for both regions. The final suitability map derived by overlaying all the considered parameters indicated that 8% of the RA region was categorized as excellent, while UA showed only 5%. Samples situated in the study areas showed an excellent to very satisfactory range for livestock consumption. Developing a monitoring system along with innovative water resource management systems is essential in maintaining the fertility of the soil and existing groundwater reserves.

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“…(Nurul et al, 2020) applied a fuzzy inference system to rainfall-runoff modeling. (Dhaoui et al, 2023) Modeled groundwater quality using a fuzzy inference system, and (Nayak et al, 2004) Used fuzzy-based modeling hydrological time series, while (Santos and Silva, 2014) used different artificial neural network algorithms and wavelet transforms for daily streamflow forecasting. (Pesti et al, 1996) employed such systems for drought evaluation, while (Abebe et al, 2000) utilised them for rainfall pattern forecasting and reconstructing missing precipitation events.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Flow Coefficient of the River Basin Using Adaptive Fuzzy Inference System and Fuzzy SMRGT method

Gunal,

Mehdi

2023

J. Ecol. Eng.

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In hydrology and water resources engineering, predicting the flow coefficient is a crucial task that helps estimate the precipitation resulting in a surface flow. Accurate flow coefficient prediction is essential for efficient water management, flood control strategy development, and water resource planning. This investigation calculated the flow coefficient using models based on Simple Membership functions and fuzzy Rules Generation Technique (SMRGT) and an Adaptive Neuro-Fuzzy Inference System (ANFIS) model. The fuzzy logic methods are used to model the intricate connections between the inputs and the output. Statistical parameters such as the coefficient of determination (R 2 ), the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE) were used to evaluate the performance of models. The statistical tests outcome for the SMRGT model was (RMSE:0.

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Groundwater quality for irrigation in an arid region—application of fuzzy logic techniques

Cited by 20 publications

References 117 publications

A Review: Artificial Intelligence Related to Agricultural Equipment Integrated with the Internet of Things

A Review: Artificial Intelligence Related to Agricultural Equipment Integrated with the Internet of Things

Groundwater Management for Agricultural Purposes Using Fuzzy Logic Technique in an Arid Region

Forecasting the Flow Coefficient of the River Basin Using Adaptive Fuzzy Inference System and Fuzzy SMRGT method

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