Use long short-term memory to enhance Internet of Things for combined sewer overflow monitoring

Zhang, Duo; Lindholm, Geir; Ratnaweera, Harsha

doi:10.1016/j.jhydrol.2017.11.018

Cited by 222 publications

(107 citation statements)

References 29 publications

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“…Yadav et al [50] used climate data to predict average solar radiation through RNN and proposed an adaptive learning rate for RNN. As an improved version of RNN, long short-term memory (LSTM) replaced it and became a popular time series data prediction technology [51,52]. A gated recurrent unit (GRU) [53] inherits the advantages of LSTM, can automatically learn features and model long-term dependent information, and also shows an improvement in calculation speed.…”

Section: Single Methodsmentioning

confidence: 99%

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

Jin

Yang

Wang

et al. 2020

Sensors

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Smart agricultural sensing has enabled great advantages in practical applications recently, making it one of the most important and valuable systems. For outdoor plantation farms, the prediction of climate data, such as temperature, wind speed, and humidity, enables the planning and control of agricultural production to improve the yield and quality of crops. However, it is not easy to accurately predict climate trends because the sensing data are complex, nonlinear, and contain multiple components. This study proposes a hybrid deep learning predictor, in which an empirical mode decomposition (EMD) method is used to decompose the climate data into fixed component groups with different frequency characteristics, then a gated recurrent unit (GRU) network is trained for each group as the sub-predictor, and finally the results from the GRU are added to obtain the prediction result. Experiments based on climate data from an agricultural Internet of Things (IoT) system verify the development of the proposed model. The prediction results show that the proposed predictor can obtain more accurate predictions of temperature, wind speed, and humidity data to meet the needs of precision agricultural production.

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Section: Single Methodsmentioning

confidence: 99%

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

Jin

Yang

Wang

et al. 2020

Sensors

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“…While DL has stimulated exciting advances in many disciplines and has become the method of choice in some areas, water sciences so far have only had a very limited set of DL applications. Despite scattered early reports of promising DL results Laloy et al, 2017Laloy et al, , 2018Tao et al, 2016;Vandal et al, 2017;Zhang et al, 2018), water scientists seemed to have reservations about these new tools, perhaps with good reasoning. This opinion paper, endorsed by the cohort of authors, argues that there are many opportunities in water sciences where DL can help provide both stronger predictive capabilities and 5 a complementary avenue toward scientific discovery.…”

Section: Overviewmentioning

confidence: 99%

HESS Opinions: Deep learning as a promising avenue toward knowledge discovery in water sciences

Laloy

Albert

Chang

et al. 2018

Preprint

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Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in water science 20 has so far been gradual, but the related fields are now ripe for breakthroughs. This paper proposes that DL-based methods can open up a viable, complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machinelearning algorithms present competing hypotheses that are consistent with data for scientists to further evaluate. Interrogative studies are invoked to interpret DL models. In addition, we lay out several opinions shared by authors: (1) deep learning may bring forth transformative progress to the field of hydrology due to its ability to assimilate big data and identify commonalities 25 and differences; (2) the community may benefit greatly from a variety of shared datasets and open competitions; (3) big hydrologic data can be obtained via various ways including data compilation and working with citizen scientists, which offers the co-benefits of education and stakeholder engagement; (4) water sciences, and hydrology in particular, offer a unique set of challenges that can, in turn, stimulate advances in machine learning; and (5) An urgent need for research is hydrologycustomized methods for interpreting knowledge extracted by deep learning. 30Hydrol. Earth Syst. Sci. Discuss., https://doi.

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“…The LSTM model has an advantage over other ML approaches in capturing the time-series dynamics of discharges and reducing the time consumption and memory storage [17]. Moreover, LSTM also outperforms other neural networks in predicting water table depth in agricultural areas [18], monitoring sewer overflow [19], simulating the reservoir operation [17] and so on. To our knowledge, there has been no previous attempt to deploy the LSTM network on discharge forecasting in small mountainous catchments to assess its performance in flash flood forecasting.The aim of this study is to propose a data-driven discharge forecasting model based on LSTM networks for flash flood forecasting in mountainous catchments.…”

mentioning

confidence: 99%

Flash Flood Forecasting Based on Long Short-Term Memory Networks

Song

Ding

et al. 2019

Water

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Flash floods occur frequently and distribute widely in mountainous areas because of complex geographic and geomorphic conditions and various climate types. Effective flash flood forecasting with useful lead times remains a challenge due to its high burstiness and short response time. Recently, machine learning has led to substantial changes across many areas of study. In hydrology, the advent of novel machine learning methods has started to encourage novel applications or substantially improve old ones. This study aims to establish a discharge forecasting model based on Long Short-Term Memory (LSTM) networks for flash flood forecasting in mountainous catchments. The proposed LSTM flood forecasting (LSTM-FF) model is composed of T multivariate single-step LSTM networks and takes spatial and temporal dynamics information of observed and forecast rainfall and early discharge as inputs. The case study in Anhe revealed that the proposed models can effectively predict flash floods, especially the qualified rates (the ratio of the number of qualified events to the total number of flood events) of large flood events are above 94.7% at 1-5 h lead time and range from 84.2% to 89.5% at 6-10 h lead-time. For the large flood simulation, the small flood events can help the LSTM-FF model to explore a better rainfall-runoff relationship. The impact analysis of weights in the LSTM network structures shows that the discharge input plays a more obvious role in the 1-h LSTM network and the effect decreases with the lead-time. Meanwhile, in the adjacent lead-time, the LSTM networks explored a similar relationship between input and output. The study provides a new approach for flash flood forecasting and the highly accurate forecast contributes to prepare for and mitigate disasters.Water 2020, 12, 109 2 of 15 concluded no model could make reliable flash flood forecasts in spite of the plausible results of physically-based distributed hydrological models. Many studies showed that distributed hydrological models have advantages over lumped hydrological models and data-driven models, but they are computationally inefficient and need high-resolution sophisticated input data (e.g., DEM, land-use and soil maps, and soil characteristics) [5]. Hence, their applicability is limited in mountainous catchments. The expected benefits of using high-resolution distributed models might be masked by the increasing uncertainties at small scales [6]. Lumped hydrological models for flash flood forecasting are limited by their coarse resolution and inadequate description of rainfall spatial distribution, which has a great impact on the catchment response [2]. In addition, physically-based hydrological models depend heavily on their boundary conditions, which are often poorly defined [7]. It is difficult to describe flash flood generation and propagation by a deterministic approach due to the complexity of its processes. Numerous studies indicate the gap of physically-based hydrological models in short-term flood prediction [8].With the advancements in sys...

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Use long short-term memory to enhance Internet of Things for combined sewer overflow monitoring

Cited by 222 publications

References 29 publications

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

HESS Opinions: Deep learning as a promising avenue toward knowledge discovery in water sciences

Flash Flood Forecasting Based on Long Short-Term Memory Networks

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