Groundwater Level Prediction with Machine Learning to Support Sustainable Irrigation in Water Scarcity Regions

Li, Wanru; Finsa, Mekuanent Muluneh; Laskey, Kathryn Blackmond; Houser, Paul; Douglas-Bate, Rupert

doi:10.3390/w15193473

Cited by 13 publications

(8 citation statements)

References 48 publications

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“…The data requirements as well as the cost for predicting the irrigation level using neural networks are high. Most of the techniques do not provide optimum irrigation planning measures, which affects the quantity of soil nutrients as well as the quality of crops [36]. Processing soil moisture data in real-time dynamics is difficult.…”

Section: B Existing Challenges In Irrigation Level Predictionmentioning

confidence: 99%

An Adaptive Hybrid (1D-2D) Convolution-Based ShuffleNetV2 Mechanism for Irrigation Levels Prediction in Agricultural Fields With Smart IoTs

Xu,

Lakshmi Patibandla,

Arora

et al. 2024

IEEE Access

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Rich natural resources such as fertilizers, environment, groundwater, rivers, and land are abundant in many countries. Agriculture is the primary source of income for the people living in different countries. There have not been shortages of resources like river water and groundwater, in recent decades. But, the lack of knowledge on how to use those valuable resources is the main reason for resource wastage. The amount of water applied to crop fields in a variety of soil, weather, and crop growth stages can be managed and optimized using smart farming. The crop field's soil moisture can be measured using sensors positioned at various observation points, which will show how much water has been retained. Unfortunately, the smart farming system is not capable to receive the soil moisture data provided by the irrigation management due to issues with connectivity or sensor failure. Innovative agricultural approaches can be facilitated by the Internet of Things (IoT) technologies. These IoT nodes have encountered energy limitations and challenging routing techniques as a result of their low capacity. Therefore, it is imperative to resolve the issues by implementing an effective IoTbased irrigation system in the agricultural area. The major steps of the developed model are data collection and prediction. Initially, essential image and sensor data is attained from the benchmark resources. Next, the collected images are provided to the level of the irrigation prediction phase. This phase facilitates the farmers to maximize the crop yields and minimize the production cost. Here, effective irrigation prediction is performed using an Adaptive Hybrid (1D-2D) Convolution-based ShuffleNetV2 model (AHC-ShuffleNetV2). Moreover, the parameters of the suggested AHC-ShuffleNetV2 are optimized using a Fitness-based Piranha Foraging Optimization Algorithm (FPFOA). This increases the performance rates of the proposed model. Later, several experimental analyses are executed in the developed model over classical techniques to display their effectualness rate. When considering the sigmoid activation function, the implemented smart irrigation level prediction framework's RMSE was minimized by 73.15% of POA-ShuffleNetV2, 72.36% of RSA-ShuffleNetV2, 78.94% of MRS-ShuffleNetV2, and 79.47% of PFOA-ShuffleNetV2 respectively. Hence, it is revealed that the designed smart irrigation level prediction model attained low error rates and also achieved higher efficacy than the other baseline techniques.INDEX TERMS Irrigation level prediction, agricultural fields, smart internet of things, fitness-based piranha foraging optimization algorithm, adaptive hybrid (1D-2D) convolution-based shufflenetV2 model.

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Section: B Existing Challenges In Irrigation Level Predictionmentioning

confidence: 99%

An Adaptive Hybrid (1D-2D) Convolution-Based ShuffleNetV2 Mechanism for Irrigation Levels Prediction in Agricultural Fields With Smart IoTs

Xu,

Lakshmi Patibandla,

Arora

et al. 2024

IEEE Access

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“…However, not all sensors contribute useful information for RUL prediction, as some remain constant until failure [36,40,47]. Following the approach outlined in [36], we selectively incorporate data from 14 sensors (sensors 2, 3,4,7,8,9,11,12,13,14,15,17,20,21) into our training and testing processes. Additionally, we apply max-min normalization to the sensor readings, which is expressed by the formula [36]:…”

Section: Data and Preprocessingmentioning

confidence: 99%

“…To overcome the limitations of traditional physics-based and statistics-based methods, researchers are redirecting their focus towards the adoption of artificial intelligence and machine learning (AI/ML) techniques for predicting the RUL. This strategic shift has been prompted by the demonstrated successes of AI/ML applications in diverse domains, including but not limited to cybersecurity [17,18], engineering [19,20], and geology [21,22]. The growing prevalence of data and the continuous advancements in computational power further underscore the potential of AI/ML in increasing the accuracy of RUL prediction.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Attention-Based Hierarchical Transformer for Turbofan Engine Remaining Useful Life Prediction

Fan,

Li,

Chang

2024

Sensors

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The accurate estimation of the remaining useful life (RUL) for aircraft engines is essential for ensuring safety and uninterrupted operations in the aviation industry. Numerous investigations have leveraged the success of the attention-based Transformer architecture in sequence modeling tasks, particularly in its application to RUL prediction. These studies primarily focus on utilizing onboard sensor readings as input predictors. While various Transformer-based approaches have demonstrated improvement in RUL predictions, their exclusive focus on temporal attention within multivariate time series sensor readings, without considering sensor-wise attention, raises concerns about potential inaccuracies in RUL predictions. To address this concern, our paper proposes a novel solution in the form of a two-stage attention-based hierarchical Transformer (STAR) framework. This approach incorporates a two-stage attention mechanism, systematically addressing both temporal and sensor-wise attentions. Furthermore, we enhance the STAR RUL prediction framework by integrating hierarchical encoder–decoder structures to capture valuable information across different time scales. By conducting extensive numerical experiments with the CMAPSS datasets, we demonstrate that our proposed STAR framework significantly outperforms the current state-of-the-art models for RUL prediction.

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“…Certain sensors, for instance, exhibit constant measurements throughout the entire life cycle. To mitigate computational complexity, we adopt the approach outlined in [40], selectively incorporating data from 14 sensors (sensors 2, 3,4,7,8,9,11,12,13,14,15,17,20,21) into our training process. Recognizing the disparate numerical ranges resulting from distinct sensor measurements, we also employ a min-max normalization technique by using the following formula:…”

Section: C-mapss Dataset and Preprocessingmentioning

confidence: 99%

“…As data volume and computing capabilities continue to expand, artificial intelligence and machine learning (AI/ML) have found success in applications across various domains, including cyber security [9,10], geology [11,12], aerospace engineering [13,14], and transportation [15,16]. In parallel, the focus of research on data-driven approaches for RUL estimation is in the process of transitioning from conventional statistical-based probabilistic techniques to AI/ML methods.…”

Section: Introductionmentioning

confidence: 99%

A Bidirectional Long Short-Term Memory Autoencoder Transformer for Remaining Useful Life Estimation

Fan,

Li,

Chang

2023

Mathematics

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Estimating the remaining useful life (RUL) of aircraft engines holds a pivotal role in enhancing safety, optimizing operations, and promoting sustainability, thus being a crucial component of modern aviation management. Precise RUL predictions offer valuable insights into an engine’s condition, enabling informed decisions regarding maintenance and crew scheduling. In this context, we propose a novel RUL prediction approach in this paper, harnessing the power of bi-directional LSTM and Transformer architectures, known for their success in sequence modeling, such as natural languages. We adopt the encoder part of the full Transformer as the backbone of our framework, integrating it with a self-supervised denoising autoencoder that utilizes bidirectional LSTM for improved feature extraction. Within our framework, a sequence of multivariate time-series sensor measurements serves as the input, initially processed by the bidirectional LSTM autoencoder to extract essential features. Subsequently, these feature values are fed into our Transformer encoder backbone for RUL prediction. Notably, our approach simultaneously trains the autoencoder and Transformer encoder, different from the naive sequential training method. Through a series of numerical experiments carried out on the C-MAPSS datasets, we demonstrate that the efficacy of our proposed models either surpasses or stands on par with that of other existing methods.

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Groundwater Level Prediction with Machine Learning to Support Sustainable Irrigation in Water Scarcity Regions

Cited by 13 publications

References 48 publications

An Adaptive Hybrid (1D-2D) Convolution-Based ShuffleNetV2 Mechanism for Irrigation Levels Prediction in Agricultural Fields With Smart IoTs

An Adaptive Hybrid (1D-2D) Convolution-Based ShuffleNetV2 Mechanism for Irrigation Levels Prediction in Agricultural Fields With Smart IoTs

A Two-Stage Attention-Based Hierarchical Transformer for Turbofan Engine Remaining Useful Life Prediction

A Bidirectional Long Short-Term Memory Autoencoder Transformer for Remaining Useful Life Estimation

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