scholarly journals Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes

2019 ◽  
Vol 33 (2) ◽  
pp. 481-514 ◽  
Author(s):  
Georgia Papacharalampous ◽  
Hristos Tyralis ◽  
Demetris Koutsoyiannis
Keyword(s):  
Machine Learning ◽  
Hydrological Processes ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods

2020 ◽  
Author(s):  
Yang Yang ◽  
Ting Fong May Chui
Keyword(s):  
Machine Learning ◽  
Management Practices ◽  
Hydrological Processes ◽  
Rainfall Time Series ◽  
Hydrograph Separation ◽  
Learning Methods ◽  
Drainage Systems ◽  
Factors Affecting ◽  
Machine Learning Methods ◽  
Hydrological Responses

Abstract. Sustainable drainage systems (SuDS) are decentralized stormwater management practices that mimic the natural drainage processes. Their modeling is often challenged by insufficient data and unknown factors affecting the hydrological processes. This study uses machine learning methods to model directly the correlation between hydrological responses and rainfalls at fine temporal scales in two catchments of different sizes. A feature engineering method is developed to extract useful information from rainfall time series and is used in combination with a nested cross-validation procedure to derive high-quality models and to estimate their generalization errors. The SHAP method is adopted to explain the basis of each prediction, which is then used for estimating catchment response time and hydrograph separation. The explanations of the predictions provide valuable insights into the models’ behavior and the involved hydrological processes. Thus, interpreting machine learning models is found as a useful way to study catchment hydrology.

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

2019 ◽  
Author(s):  
Sina Faizollahzadeh ardabili ◽  
Amir Mosavi ◽  
Majid Dehghani ◽  
Annamária R. Várkonyi-Kóczy
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Deep Learning ◽  
Model Performance ◽  
Hydrological Processes ◽  
Learning Methods ◽  
Earth Systems ◽  
Machine Learning Methods ◽  
Ensemble Techniques ◽  
Current State

Artificial intelligence methods and application have recently shown great contribution in modeling and prediction of the hydrological processes, climate change, and earth systems. Among them, deep learning and machine learning methods mainly have reported being essential for achieving higher accuracy, robustness, efficiency, computation cost, and overall model performance. This paper presents the state of the art of machine learning and deep learning methods and applications in this realm and the current state, and future trends are discussed. The survey of the advances in machine learning and deep learning are presented through a novel classification of methods. The paper concludes that deep learning is still in the first stages of development, and the research is still progressing. On the other hand, machine learning methods are already established in the fields, and novel methods with higher performance are emerging through ensemble techniques and hybridization.

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