Deep Learning Approach for Forecasting Water Quality in IoT Systems

Nguyen, Thin; Thanh-Hai, Nguyen; Ngôn, Nguyễn Chí

doi:10.14569/ijacsa.2020.0110883

Cited by 35 publications

(18 citation statements)

References 19 publications

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“…For instance, Ref. [11] edge devices are used to sense water data and then transfer the data to the cloud to analyze; data can also be forecast using DL models. (iii) Power and memory consumption are present challenges; heavy-weight DL models consume memory and a lot of power.…”

Section: Edge Devicesmentioning

confidence: 99%

TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications

Alajlan

Ibrahim

2022

Micromachines

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Recently, the Internet of Things (IoT) has gained a lot of attention, since IoT devices are placed in various fields. Many of these devices are based on machine learning (ML) models, which render them intelligent and able to make decisions. IoT devices typically have limited resources, which restricts the execution of complex ML models such as deep learning (DL) on them. In addition, connecting IoT devices to the cloud to transfer raw data and perform processing causes delayed system responses, exposes private data and increases communication costs. Therefore, to tackle these issues, there is a new technology called Tiny Machine Learning (TinyML), that has paved the way to meet the challenges of IoT devices. This technology allows processing of the data locally on the device without the need to send it to the cloud. In addition, TinyML permits the inference of ML models, concerning DL models on the device as a Microcontroller that has limited resources. The aim of this paper is to provide an overview of the revolution of TinyML and a review of tinyML studies, wherein the main contribution is to provide an analysis of the type of ML models used in tinyML studies; it also presents the details of datasets and the types and characteristics of the devices with an aim to clarify the state of the art and envision development requirements.

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Section: Edge Devicesmentioning

confidence: 99%

TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications

Alajlan

Ibrahim

2022

Micromachines

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“…The recurrent neural network (RNN) is a type of ANN model well suited for handling sequential data, such as the time series data in salinity modeling [30][31][32][33]. An LSTM network [34] is a special type of RNN that has been widely used in time series estimation [33,35,36], and has recently been applied to estimate salinity at one location in the Delta [22]. Inside the LSTM layer, there is an input gate, a forget gate, and an output gate.…”

Section: Long-short-term Memory (Lstm) Networkmentioning

confidence: 99%

Multi-Location Emulation of a Process-Based Salinity Model Using Machine Learning

He²,

Bai

et al. 2022

Water

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Advances in machine-learning techniques can serve practical water management needs such as salinity level estimation. This study explores machine learning, particularly deep-learning techniques in developing computer emulators for a commonly used process model, the Delta Simulation Model II (DSM2), used for salinity estimation in California’s Sacramento-San Joaquin Delta (Delta). We apply historical daily input data to DSM2 and corresponding salinity simulations at 28 study locations from 1990 to 2019 to train two machine-learning models: a multi-layer perceptron (MLP) and Long-Short-Term Memory (LSTM) networks in a multi-task learning framework. We assess sensitivity of both networks to the amount of antecedent input information (memory) and training data to determine appropriate memory size and training data length. We evaluate network performance according to several statistical metrics as well as visual inspection. The study further investigates two additional networks, the Gated Recurrent Unit (GRU) and Residual Network (ResNet) in salinity modeling, and compares their efficacy against MLP and LSTM. Our results demonstrate strong performance of the four neural network models over the study period, achieving absolute bias below 4%, plus near-perfect correlation coefficients and Nash–Sutcliffe efficiency coefficients. The high complexity LSTM shows slight performance edge. We further show that deeper and wider versions of MLP and LSTM yield only marginal benefit over their baseline counterparts. We also examined issues related to potential overfitting by the proposed models, training data selection strategies, and analytical and practical implications. Overall, this new study indicates that machine-learning-based emulators can efficiently emulate DSM2 in salinity simulation. They exhibit strong potential to supplement DSM2 in salinity modeling and help guide water resource planning and management practices for the Delta region.

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“…Pearson's correlation coefficient is used to obtain the correlation priors between pH, water temperature, and other water quality parameters to be used in the prediction model. The authors in [96] introduce an architecture composed of an LSTM forecasting model and an IoT system to monitor real time salinity, temperature, pH, and dissolved oxygen water quality from different aquaculture ponds. Since the data is recorded daily, they can build sequential time series that are fed into the forecasting scheme.…”

Section: E Aquaculturementioning

confidence: 99%

Sustainable Marine Ecosystems: Deep Learning for Water Quality Assessment and Forecasting

et al. 2021

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An appropriate management of the available resources within oceans and coastal regions is vital to guarantee their sustainable development and preservation, where water quality is a key element. Leveraging on a combination of cross-disciplinary technologies including Remote Sensing (RS), Internet of Things (IoT), Big Data, cloud computing, and Artificial Intelligence (AI) is essential to attain this aim. In this paper, we review methodologies and technologies for water quality assessment that contribute to a sustainable management of marine environments. Specifically, we focus on Deep Leaning (DL) strategies for water quality estimation and forecasting. The analyzed literature is classified depending on the type of task, scenario and architecture. Moreover, several applications including coastal management and aquaculture are surveyed. Finally, we discuss open issues still to be addressed and potential research lines where transfer learning, knowledge fusion, reinforcement learning, edge computing and decision-making policies are expected to be the main involved agents.

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Deep Learning Approach for Forecasting Water Quality in IoT Systems

Cited by 35 publications

References 19 publications

TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications

TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications

Multi-Location Emulation of a Process-Based Salinity Model Using Machine Learning

Sustainable Marine Ecosystems: Deep Learning for Water Quality Assessment and Forecasting

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