Deep learning in environmental remote sensing: Achievements and challenges

. Rainfall–runoff modelling is one of the key
challenges in the field of hydrology. Various approaches exist, ranging from
physically based over conceptual to fully data-driven models. In this paper,
we propose a novel data-driven approach, using the Long Short-Term Memory
(LSTM) network, a special type of recurrent neural network. The advantage of
the LSTM is its ability to learn long-term dependencies between the provided
input and output of the network, which are essential for modelling storage
effects in e.g. catchments with snow influence. We use 241 catchments of the
freely available CAMELS data set to test our approach and also compare the
results to the well-known Sacramento Soil Moisture Accounting Model (SAC-SMA)
coupled with the Snow-17 snow routine. We also show the potential of the LSTM
as a regional hydrological model in which one model predicts the discharge
for a variety of catchments. In our last experiment, we show the possibility
to transfer process understanding, learned at regional scale, to individual
catchments and thereby increasing model performance when compared to a LSTM
trained only on the data of single catchments. Using this approach, we were
able to achieve better model performance as the SAC-SMA  +  Snow-17, which
underlines the potential of the LSTM for hydrological modelling applications.

/pdf/rainfall-runoff-modelling-using-long-short-term-memory-lstm-3ffiowa3ei.pdf

Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks

BigData andmachine learning (ML) technologies have the potential to impactmany facets of environment andwatermanagement (EWM). BigData are information assets characterized by high volume, velocity, variety, and veracity. Fast advances in high-resolution remote sensing techniques, smart information and communication technologies, and socialmedia have contributed to the proliferation of BigData inmany EWMfields, such asweather forecasting, disastermanagement, smart water and energymanagement systems, and remote sensing. BigData brings about new opportunities for data-driven discovery in EWM, but it also requires new forms of information processing, storage, retrieval, as well as analytics.ML, a subdomain of artificial intelligence (AI), refers broadly to computer algorithms that can automatically learn fromdata.MLmay help unlock the power of BigData if properly integratedwith data analytics. Recent breakthroughs inAI and computing infrastructure have led to the fast development of powerful deep learning (DL) algorithms that can extract hierarchical features fromdata, with better predictive performance and less human intervention. Collectively BigData andML techniques have shown great potential for data-driven decisionmaking, scientific discovery, and process optimization. These technological advancesmay greatly benefit EWM, especially because (1)many EWMapplications (e.g. early floodwarning) require the capability to extract useful information from a large amount of data in autonomousmanner and in real time, (2)EWMresearches have become highlymultidisciplinary, and handling the ever increasing data volume/types using the traditional workflow is simply not an option, and last but not least, (3) the current theoretical knowledge aboutmany EWMprocesses is still incomplete, but whichmay now be complemented through data-driven discovery. A large number of applications onBigData andML have already appeared in the EWM literature in recent years. The purposes of this survey are to (1) examine the potential and benefits of data-driven research in EWM, (2) give a synopsis of key concepts and approaches in BigData andML, (3) provide a systematic review of current applications, andfinally (4) discussmajor issues and challenges, and recommend future research directions. EWM includes a broad range of research topics. Instead of attempting to survey each individual area, this review focuses on areas of nexus in EWM,with an emphasis on elucidating the potential benefits of increased data availability and predictive analytics to improving the EWMresearch.

https://iopscience.iop.org/article/10.1088/1748-9326/ab1b7d/pdf

How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

Department of the Interior Northeast Climate Adaptation Science Center; Midwest Glacial Lakes Fish Habitat Partnership grant through FWS; NSF Expedition in Computing Grant [1029711]; NSFNational Science Foundation (NSF) [EAR-PF-1725386]; Digital Technology Center at the University of MinnesotaUniversity of Minnesota System; Department of the Interior North Central Climate Adaptation Science Center; North Temperate Lakes Long-Term Ecological Research [NSF DEB-1440297]; Global Lake Ecological Observatory Network [NSF 1702991]

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2019WR024922

Process-Guided Deep Learning Predictions of Lake Water Temperature

We suggest that there is a potential danger to the hydrological sciences community in not recognizing how transformative machine learning will be for the future of hydrological modeling. Given the recent success of machine learning applied to modeling problems, it is unclear what the role of hydrological theory might be in the future. We suggest that a central challenge in hydrology right now should be to clearly delineate where and when hydrological theory adds value to prediction systems. Lessons learned from the history of hydrological modeling motivate several clear next steps toward integrating machine learning into hydrological modeling workflows.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020WR028091

What Role Does Hydrological Science Play in the Age of Machine Learning

. 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 hydrology has so far been gradual, but the field is now
ripe for breakthroughs. This paper suggests that DL-based methods can open up a
complementary avenue toward knowledge discovery in hydrologic sciences. In
the new avenue, machine-learning algorithms present competing hypotheses that
are consistent with data. Interrogative methods are then invoked to interpret
DL models for scientists to further evaluate. However, hydrology presents
many challenges for DL methods, such as data limitations, heterogeneity
and co-evolution, and the general inexperience of the hydrologic field with
DL. The roadmap toward DL-powered scientific advances will require the
coordinated effort from a large community involving scientists and citizens.
Integrating process-based models with DL models will help alleviate data
limitations. The sharing of data and baseline models will improve the
efficiency of the community as a whole. Open competitions could serve as the
organizing events to greatly propel growth and nurture data science education
in hydrology, which demands a grassroots collaboration. The area of
hydrologic DL presents numerous research opportunities that could, in turn,
stimulate advances in machine learning as well.

/pdf/hess-opinions-incubating-deep-learning-powered-hydrologic-2nuz7eii7s.pdf

HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community

. 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 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, machine-learning 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 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 hydrology-customized methods for interpreting knowledge extracted by deep learning.

/pdf/hess-opinions-deep-learning-as-a-promising-avenue-toward-2orqamvpn2.pdf

Xiaodong Li

Papers

HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community

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