Forecasting of water level in multiple temperate lakes using machine learning models

Zhu, Senlin; Hrnjica, Bahrudin; Ptak, Mariusz; Choiński, Adam; Sivakumar, Bellie

doi:10.1016/j.jhydrol.2020.124819

Cited by 123 publications

(47 citation statements)

References 57 publications

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“…An LSTM network contains different memory blocks which are linked through layers. Each layer includes a set of frequently connected memory pixels and three multiplicative units, namely the input, forget and output gates ( [99], [100] The architecture of the LSTM model is shown in Figure 2b To identify the optimal forecasting strategy, an LSTM network and LSTM layer with 100 hidden units were used, followed by a fully connected layer of size 30. The hyperparameters of the LSTM layers were kept at their default values.…”

Section: ) Long Short-term Memory (Lstm)mentioning

confidence: 99%

Estimation of SPEI Meteorological Drought Using Machine Learning Algorithms

et al. 2021

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Accurate estimation of drought events is vital for the mitigation of their adverse consequences on water resources, agriculture and ecosystems. Machine learning algorithms are promising methods for drought prediction as they require less time, minimal inputs, and are relatively less complex than dynamic or physical models. In this study, a combination of machine learning with the Standardized Precipitation Evapotranspiration Index (SPEI) is proposed for analysis of drought within a representative case study in the Tibetan Plateau, China, for the period of 1980-2019. Two timescales of 3 months (SPEI-3) and 6 months (SPEI-6) aggregation were considered. Four machine learning models of Random Forest (RF), the Extreme Gradient Boost (XGB), the Convolutional neural network (CNN) and the Long-term short memory (LSTM) were developed for the estimation of the SPEIs. Seven scenarios of various combinations of climate variables as input were adopted to build the models. The best models were XGB with scenario 5 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed and relative humidity) and RF with scenario 6 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed, relative humidity and sunshine) for estimating SPEI-3. LSTM with scenario 4 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed) was relatively better for SPEI-6 estimation. The best model for SPEI-6 was XGB with scenario 5 and RF with scenario 7 (all input climate variables, i.e., scenario 6 + solar radiation). Based on the NSE index, the performances of XGB and RF models are classified as good fits for scenarios 4 to 7 for both timescales. The developed models produced satisfactory results and they could be used as a rapid tool for decision making by water-managers.

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Section: ) Long Short-term Memory (Lstm)mentioning

confidence: 99%

Estimation of SPEI Meteorological Drought Using Machine Learning Algorithms

et al. 2021

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“…In these studies, the learning machine techniques are the most used, mainly in works that have the objective of predicting river flow [2], [3] In [4], water availability was predicted by forecasting the water level with the application of the Variable Infiltration Capacity (VIC) hydrological model for the Mekong River in Asia. In [5], a Feed Forward Neural Network (FFNN) was applied and compared with Long Short-Term Memory (LSTM). Its aim was also to forecast water levels, but applied to Polish lakes.…”

Section: Related Workmentioning

confidence: 99%

“…The applied LSTM layer is the current state-of-the-art. It is a very applied tool for forecasting time series and widely described and applied in other works [4], [5], [13].…”

Section: ) Dnn Architecturementioning

confidence: 99%

Daily Water Flow Forecasting via Coupling Between SMAP and Deep Learning

et al. 2020

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Hydrological models are essential tools to forecast daily water resources' availability, which are used to plan the short-term electrical systems' operation. However, there is a trade-off when choosing a given model. Complex models may provide good results depending on very complicated analytical and optimization procedures beyond sophisticated data, whereas simpler models offer reasonable results with much more amenable tuning approaches. To improve the quality of simpler models this paper proposes the coupling of the Soil Moisture Accounting Procedure (SMAP) hydrological model with a Deep Learning architecture based on Conv3D-LSTM. In the proposed methodology, the SMAP is first optimized to obtain general parameters of the hydrographic basin. This optimized model's output is used as input to the Conv3D-LSTM estimator to provide the final results. This gray estimator model can generate fast and accurate results. Studies whit the goal of forecast the natural flow seven days ahead are carried out for two large Brazilian hydroelectric plants to validate the method. The results obtained by the architecture are better than those obtained with decoupled techniques.

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“…Sun et al, 2020). For example, S Zhu et al (2020) used feed forward neural network and deep learning technique to predict monthly lake water level in 69 temperate lakes in Poland. The combination of the random forest regression model and a spatially moving window structure proposed by Jing et al (2020), reconstructed the GRACE TWS anomalies (TWSA) by using the global land data assimilation system (GL-DAS).…”

Section: Introductionmentioning

confidence: 99%

Century‐Scale Reconstruction of Water Storage Changes of the Largest Lake in the Inner Mongolia Plateau Using a Machine Learning Approach

Fan

Song

et al. 2021

Water Resources Research

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Lake Hulun is the fifth‐largest lake in China, playing a substantial role in maintaining the balance of the grassland ecosystem of the Mongolia Plateau, which is a crucial ecological barrier in North China. To better understand the changing characteristics of Lake Hulun and the driving mechanisms, it is necessary to investigate the water storage changes of Lake Hulun on extended timescales. The main objective of this study is to reconstruct the water storage time series of Lake Hulun over the past century. We employed a machine learning approach termed the extreme gradient boosting tree (XGBoost) to reconstruct the water storage changes over a one‐century timescale based on the generated bathymetry and satellite altimetry data and investigated the relationships with hydrological and climatic variables in long term. Results show that the water storage changes from 1961 to 2019 were featured by four fluctuation phases, with the highest water storage observed in 1991 (14.02 Gt) and the lowest point in 2012 (5.18 Gt). The century‐scale reconstruction result reveals that the water storage of Lake Hulun reached the highest point in the 1960s within the period of 1910–2019. The lowest stage occurred in the sub‐period of the 1930s–1940s, which was even lower than the alerted shrinkage stage in 2012. The predictive model results indicate the effective performance of the XGBoost model in reconstructing century‐scale water storage variations, with the mean absolute error of 0.68, normalized root mean square error of 0.11, Nash–Sutcliffe efficiency of 0.97, and correlation coefficient of 0.94. The annual fluctuations of water storage were mostly affected by precipitation, followed by vapor pressure, temperature, potential evapotranspiration, and wet day frequency. The dominating characteristics of different variables vary evidently with different sub‐periods. The atmospheric circulations of the Arctic Oscillation, El Nino Southern Oscillation, Pacific Decadal Oscillation, and North Atlantic Oscillation have tight associations with the water storage variations of Lake Hulun, which change with different study periods.

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Forecasting of water level in multiple temperate lakes using machine learning models

Cited by 123 publications

References 57 publications

Estimation of SPEI Meteorological Drought Using Machine Learning Algorithms

Estimation of SPEI Meteorological Drought Using Machine Learning Algorithms

Daily Water Flow Forecasting via Coupling Between SMAP and Deep Learning

Century‐Scale Reconstruction of Water Storage Changes of the Largest Lake in the Inner Mongolia Plateau Using a Machine Learning Approach

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