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

Sun, Alexander Y.; Scanlon, Bridget R.

doi:10.1088/1748-9326/ab1b7d

Cited by 311 publications

(166 citation statements)

References 180 publications

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“…As a DL model, SAE is able to automatically extract higher-level features in the environmental variables with unsupervised pre-training than traditional ML models. These higher-level features are more robust to outliers in input data and can better reflect the inherent nature of environmental variables [68,69]. Then, these higher-level features can be utilized to predict RE more effectively and accurately in the fine-tuning process [56].…”

Section: Discussionmentioning

confidence: 99%

Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison

Zhu

et al. 2020

Sustainability

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While a number of machine learning (ML) models have been used to estimate RE, systematic evaluation and comparison of these models are still limited. In this study, we developed three traditional ML models and a deep learning (DL) model, stacked autoencoders (SAE), to estimate RE in northern China’s grasslands. The four models were trained with two strategies: training for all of northern China’s grasslands and separate training for the alpine and temperate grasslands. Our results showed that all four ML models estimated RE in northern China’s grasslands fairly well, while the SAE model performed best (R2 = 0.858, RMSE = 0.472 gC m−2 d−1, MAE = 0.304 gC m−2 d−1). Models trained with the two strategies had almost identical performances. The enhanced vegetation index and soil organic carbon density (SOCD) were the two most important environmental variables for estimating RE in the grasslands of northern China. Air temperature (Ta) was more important than the growing season land surface water index (LSWI) in the alpine grasslands, while the LSWI was more important than Ta in the temperate grasslands. These findings may promote the application of DL models and the inclusion of SOCD for RE estimates with increased accuracy.

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Section: Discussionmentioning

confidence: 99%

Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison

Zhu

et al. 2020

Sustainability

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“…Hal ini terkait dengan uji coba pemodelan dan selanjutnya juga kemampuan dari analis dalam menyampaikan hasil kajian bagi pengguna. Hasil analisis bisa memberikan dampak yang positif dalam mengambil keputusan, memberi solusi yang optimal, juga pengetahuan yang mendalam, akan tetapi lebih dari itu keberhasilan dari hasil analisis sangat bergantung pada pengetahuan yang dapat tersampaikan tepat waktu dan tepat sasaran (Sun dan Scanlon, 2019).…”

Section: A Penguasaan Teknologiunclassified

Disruptive Technology Through Satellite Imagery Big Data in Disaster Risk Reduction: Opportunity and Challenge

Melati

2020

JSTMB

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The development of current massive technology plays an important role in the field of disaster risk reduction and disaster management. In particular, the issue of disruptive technology in which the emerging of new technologies comes with big disruption in many fields. For instance, the development on using satellite imagery big data for disaster management to reduce the disaster risk.The unique characteristics of big data i.e. volume, velocity, and variety stimulate this study to obtain more information on the big data processing and analyze the opportunities and challenges of satellite imagery in the case of disaster risk reduction through literature reviews. The huge amount of data and the ability for real-time analysis provide capabilities on time series analysis and possitive impact on disaster detection, monitoring, and prediction. However, such technology creates disruption due to the change on using and anlysing the data based on cloud environment. Issues on the technology uses, data security might arise. Nonetheless, the use of satellite imagery big data will remain vastly developed and is able to promote the development of satellite imagery technology.

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“…The motivations for using AI for forested ecosystem related research, including disturbances due to wildfire, insects, and disease, were discussed in an earlier paper (Schmoldt 2001), while Olden et al (2008) further argued for the use of ML methods to model complex problems in ecology. The use of ML models in the environmental sciences has seen a rapid uptake in the last decade, as evidenced by recent reviews in the geosciences (Karpatne et al 2017), forest ecology (Liu et al 2018), extreme weather prediction (McGovern et al 2017), flood forecasting (Mosavi et al 2018), statistical downscaling (Vandal et al 2019), remote sensing (Lary et al 2016), and water resources (Shen 2018;Sun and Scanlon 2019). Two recent perspectives have also made compelling arguments for the application of deep learning (DL) in Earth system sciences (Reichstein et al 2019) and for tackling climate change (Rolnick et al 2019).…”

Section: Introductionmentioning

confidence: 99%

A review of machine learning applications in wildfire science and management

et al. 2020

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Artiﬁcial intelligence has been applied in wildﬁre science and management since the 1990s, with early applications including neural networks and expert systems. Since then the ﬁeld has rapidly progressed congruently with the wide adoption of machine learning (ML) methods in the environmental sciences. Here, we present a scoping review of ML applications in wildﬁre science and management. Our overall objective is to improve awareness of ML methods among wildﬁre researchers and managers, as well as illustrate the diverse and challenging range of problems in wildﬁre science available to ML data scientists. To that end, we ﬁrst present an overview of popular ML approaches used in wildﬁre science to date, and then review the use of ML in wildﬁre science as broadly categorized into six problem domains, including: 1) fuels characterization, ﬁre detection, and mapping; 2) ﬁre weather and climate change; 3) ﬁre occurrence, susceptibility, and risk; 4) ﬁre behavior prediction; 5) ﬁre eﬀects; and 6) ﬁre management. Furthermore, we discuss the advantages and limitations of various ML approaches relating to data size, computational requirements, generalizability, and interpretability, as well as identify opportunities for future advances in the science and management of wildﬁres within a data science context. In total, we identfied 300 relevant publications up to the end of 2019, where the most frequently used ML methods across problem domains included random forests, MaxEnt, artiﬁcial neural networks, decision trees, support vector machines, and genetic algorithms. As such, there exists opportunities to apply more current ML methods — including deep learning and agent based learning — in the wildﬁre sciences, especially in instances involving very large multivariate datasets. We must recognize, however, that despite the ability of ML methods to learn on their own, expertise in wildﬁre science is necessary to ensure realistic modelling of ﬁre processes across multiple scales, while the complexity of some ML methods, such as deep learning, requires a dedicated and sophisticated knowledge of their application. Finally, we stress that the wildﬁre research and management communities play an active role in providing relevant, high quality, and freely available wildﬁre data for use by practitioners of ML methods.

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

Cited by 311 publications

References 180 publications

Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison

Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison

Disruptive Technology Through Satellite Imagery Big Data in Disaster Risk Reduction: Opportunity and Challenge

A review of machine learning applications in wildfire science and management

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