Groundwater quality forecasting using machine learning algorithms for irrigation purposes

Bilali, Ali El; Taleb, Abdeslam; Brouziyne, Youssef

doi:10.1016/j.agwat.2020.106625

Cited by 206 publications

(72 citation statements)

References 70 publications

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“…Taking into account the rapid depletion rate of a lot of aquifers with negligible recharge, more effective water management is needed for the purpose of better conserving water in terms of accomplishing a sustainable crop production [ 65 ]. Effective water management can also lead to the improvement of water quality as well as reduction of pollution and health risks [ 66 ]. Recent research on precision agriculture offers the potential of variable rate irrigation so as to attain water savings.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Agriculture: A Comprehensive Updated Review

Benos

Tagarakis

Dolias

et al. 2021

Sensors

427

146

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The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.

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Section: Introductionmentioning

confidence: 99%

Machine Learning in Agriculture: A Comprehensive Updated Review

Benos

Tagarakis

Dolias

et al. 2021

Sensors

427

146

View full text Add to dashboard Cite

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“…The application of virtual (or soft) sensing for surface and groundwater quality assessment is emerging. This is evidenced by the low number of publications (16) and the fact that 63% of the articles [22,29,55,85,[94][95][96][97][98][99] were published in the last three years (2019-2021). Therefore, as seen in Figure 4, it is not surprising that artificial neural network (ANN) related algorithms are the most applied ML techniques.…”

Section: Commonly Used Modeling Approachesmentioning

confidence: 99%

“…Since COD levels determine the amount of root oxygen available (in the form of DO), the lack of COD prediction studies could be mainly because the application of virtual sensing for irrigation WQ assessment is relatively new. For instance, only four studies in the papers reviewed explicitly stated irrigation purposes as the use case [55,95,99,102]. On the other hand, the reason for the lack of E. coli prediction studies is because the focus is mainly on the physical and chemical characteristics when determining the suitability of water for crop production [116].…”

Section: Water Quality Parameters Modeledmentioning

confidence: 99%

“…This is huge, particularly for farmers in developing countries, who may not have sufficient resources for irrigation WQ monitoring. Considering that potassium (which is included in the computation of exchangeable sodium percentage) was predicted on two occasions [55,95], updating its total score to one brings the overall monitoring cost estimate to two, which is categorized as low (refer to Section 2.7). In essence, VSs can play a central role in developing innovative WQ monitoring systems.…”

Section: An Update Of the Measurement Cost Estimatementioning

confidence: 99%

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From Fully Physical to Virtual Sensing for Water Quality Assessment: A Comprehensive Review of the Relevant State-of-the-Art

Paepae

Bokoro

Kyamakya

2021

Sensors

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Rapid urbanization, industrial development, and climate change have resulted in water pollution and in the quality deterioration of surface and groundwater at an alarming rate, deeming its quick, accurate, and inexpensive detection imperative. Despite the latest developments in sensor technologies, real-time determination of certain parameters is not easy or uneconomical. In such cases, the use of data-derived virtual sensors can be an effective alternative. In this paper, the feasibility of virtual sensing for water quality assessment is reviewed. The review focuses on the overview of key water quality parameters for a particular use case and the development of the corresponding cost estimates for their monitoring. The review further evaluates the current state-of-the-art in terms of the modeling approaches used, parameters studied, and whether the inputs were pre-processed by interrogating relevant literature published between 2001 and 2021. The review identified artificial neural networks, random forest, and multiple linear regression as dominant machine learning techniques used for developing inferential models. The survey also highlights the need for a comprehensive virtual sensing system in an internet of things environment. Thus, the review formulates the specification book for the advanced water quality assessment process (that involves a virtual sensing module) that can enable near real-time monitoring of water quality.

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“…However, considering the variety of machine learning models, different models have their own advantages and disadvantages in different scenarios. El Bilali et al [23] compared four common machine learning models' prediction performances and found that Random Forest (RF) and Adaptive Boosting models had higher accuracy, and Artificial Neural Network and Support Vector Machine (SVM) models had better generalization ability and lower sensitivity.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Prediction of Chlorophyll-a Variations in Receiving Reservoir of World’s Largest Water Transfer Project—A Case Study in the Miyun Reservoir, North China

Liao

Zang

Wang

et al. 2021

Water

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Although water transfer projects can alleviate the water crisis, they may cause potential risks to water quality safety in receiving areas. The Miyun Reservoir in northern China, one of the receiving reservoirs of the world’s largest water transfer project (South-to-North Water Transfer Project, SNWTP), was selected as a case study. Considering its potential eutrophication trend, two machine learning models, i.e., the support vector machine (SVM) model and the random forest (RF) model, were built to investigate the trophic state by predicting the variations of chlorophyll-a (Chl-a) concentrations, the typical reflection of eutrophication, in the reservoir after the implementation of SNWTP. The results showed that compared with the SVM model, the RF model had higher prediction accuracy and more robust prediction ability with abnormal data, and was thus more suitable for predicting Chl-a concentration variations in the receiving reservoir. Additionally, short-term water transfer would not cause significant variations of Chl-a concentrations. After the project implementation, the impact of transferred water on the water quality of the receiving reservoir would have gradually increased. After a 10-year implementation, transferred water would cause a significant decline in the receiving reservoir’s water quality, and Chl-a concentrations would increase, especially from July to August. This led to a potential risk of trophic state change in the Miyun Reservoir and required further attention from managers. This study can provide prediction techniques and advice on water quality security management associated with eutrophication risks resulting from water transfer projects.

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Groundwater quality forecasting using machine learning algorithms for irrigation purposes

Cited by 206 publications

References 70 publications

Machine Learning in Agriculture: A Comprehensive Updated Review

Machine Learning in Agriculture: A Comprehensive Updated Review

From Fully Physical to Virtual Sensing for Water Quality Assessment: A Comprehensive Review of the Relevant State-of-the-Art

Machine Learning-Based Prediction of Chlorophyll-a Variations in Receiving Reservoir of World’s Largest Water Transfer Project—A Case Study in the Miyun Reservoir, North China

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