Deep Learning and Machine Learning in Hydrological Processes, Climate Change and Earth Systems: A Systematic Review

Ardabili, Sina Faizollahzadeh; Mosavi, Amir; Dehghani, Majid; Várkonyi-Kóczy, Annamária R.

doi:10.20944/preprints201908.0166.v1

Cited by 8 publications

(5 citation statements)

References 41 publications

(44 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This trend has been also reported in other research domains, e.g. biofuels, agriculture, hydrology, and production [56][57][58][59][60][61][62][63][64].…”

Section: Building Energy Consumption Predictionsupporting

confidence: 79%

Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart Cities

Ardabili¹,

Mosavi

Várkonyi-Kóczy

2019

Preprint

Self Cite

View full text Add to dashboard Cite

Building energy consumption plays an essential role in urban sustainability. The prediction of the energy demand is also of particular importance for developing smart cities and urban planning. Machine learning has recently contributed to the advancement of methods and technologies to predict demand and consumption for building energy systems. This paper presents a state of the art of machine learning models and evaluates the performance of these models. Through a systematic review and a comprehensive taxonomy, the advances of machine learning are carefully investigated and promising models are introduced.

show abstract

“…This trend has been also reported in other research domains, e.g. biofuels, agriculture, hydrology, and production [56][57][58][59][60][61][62][63][64].…”

Section: Building Energy Consumption Predictionsupporting

confidence: 79%

Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart Cities

Ardabili¹,

Mosavi

Várkonyi-Kóczy

2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…This is true both for industrial research but also, increasingly, in the field of research for informing public policy about the management of collective spaces and resources: examples of the latter range from sustainable management of natural resources (dealing with groundwater availability, irrigation policies, forest management, etc. ), [17,18] resulting in policies for infrastructure funding allocation, to models dealing with predictions of the wider effects of global climate change. [19] That is not to say, however, that this new playground comes without pitfalls and risks-especially for the very fast uptake that this kind of modelling is seeing both in the private and the public spheres.…”

Section: Putting It All Together: How To (Carefully) Move Forwardmentioning

confidence: 99%

Reconciling deep learning and first‐principle modelling for the investigation of transport phenomena in chemical engineering

2023

View full text Add to dashboard Cite

The use of machine learning in chemical engineering has the potential to greatly improve the design and analysis of complex systems. However, there are also risks associated with its adoption, such as the potential for bias in algorithms and the need for careful oversight to ensure the safety and reliability of machine learningpowered systems. This paper explores the opportunities and risks of using machine learning in chemical engineering and provides a perspective on how it may be integrated into engineering practices in a responsible and effective manner. We generated the text of this abstract with GPT-3, OpenAI's large-scale languagegeneration model. Upon generating the draft, we ensured that the language was to our liking, and we take ultimate responsibility for the content of this publication.

show abstract

“…Nondeep learning models usually do not accurately learn the advanced nonlinearity present in the input and output variables [41]. In contrast, deep learning models are widely applied in different tasks, including processing, analyzing, designing, estimating, filtering, and detection tasks [42]. e popular deep learning models applied in different fields of studies are Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Radial Basis Function Networks (RBFN), and Generative Adversarial Network (GAN) [21,25,[43][44][45][46].…”

Section: Deep Learning Modelsmentioning

confidence: 99%

Short-Term Daily Univariate Streamflow Forecasting Using Deep Learning Models

Wegayehu

Muluneh

2022

Advances in Meteorology

View full text Add to dashboard Cite

Hydrological forecasting is one of the key research areas in hydrology. Innovative forecasting tools will reform water resources management systems, flood early warning mechanisms, and agricultural and hydropower management schemes. Hence, in this study, we compared Stacked Long Short-Term Memory (S-LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and Gated Recurrent Unit (GRU) with the classical Multilayer Perceptron (MLP) network for one-step daily streamflow forecasting. The analysis used daily time series data collected from Borkena (in Awash river basin) and Gummera (in Abay river basin) streamflow stations. All data sets passed through rigorous quality control processes, and null values were filled using linear interpolation. A partial autocorrelation was also applied to select the appropriate time lag for input series generation. Then, the data is split into training and testing datasets using a ratio of 80 : 20, respectively. Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2) were used to evaluate the performance of the proposed models. Finally, the findings are summarized in model variability, lag time variability, and time series characteristic themes. As a result, time series characteristics (climatic variability) had a more significant impact on streamflow forecasting performance than input lagged time steps and deep learning model architecture variations. Thus, Borkena’s river catchment forecasting result is more accurate than Gummera’s catchment forecasting result, with RMSE, MAE, MAPE, and R2 values ranging between (0.81 to 1.53, 0.29 to 0.96, 0.16 to 1.72, 0.96 to 0.99) and (17.43 to 17.99, 7.76 to 10.54, 0.16 to 1.03, 0.89 to 0.90) for both catchments, respectively. Although the performance is dependent on lag time variations, MLP and GRU outperform S-LSTM and Bi-LSTM on a nearly equal basis.

show abstract

Deep Learning and Machine Learning in Hydrological Processes, Climate Change and Earth Systems: A Systematic Review

Cited by 8 publications

References 41 publications

Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart Cities

Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart Cities

Reconciling deep learning and first‐principle modelling for the investigation of transport phenomena in chemical engineering

Short-Term Daily Univariate Streamflow Forecasting Using Deep Learning Models

Contact Info

Product

Resources

About