Deep learning in environmental remote sensing: Achievements and challenges

Q Yuan, H Shen, T Li, Z Li, S Li, Y Jiang, H Xu… - Remote sensing of …, 2020 - Elsevier
Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …

An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research …

ZM Yaseen, SO Sulaiman, RC Deo, KW Chau - Journal of Hydrology, 2019 - Elsevier
Despite the massive diversity in the modeling requirements for practical hydrological
applications, there remains a need to develop more reliable and intelligent expert systems …

Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization

RM Adnan, RR Mostafa, O Kisi, ZM Yaseen… - Knowledge-Based …, 2021 - Elsevier
Accurate runoff estimation is crucial for optimal reservoir operation and irrigation purposes.
In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla …

A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

L Cheng, T Yu - International Journal of Energy Research, 2019 - Wiley Online Library
The new generation of artificial intelligence (AI), called AI 2.0, has recently become a
research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and …

Employing machine learning algorithms for streamflow prediction: a case study of four river basins with different climatic zones in the United States

P Parisouj, H Mohebzadeh, T Lee - Water Resources Management, 2020 - Springer
Streamflow estimation plays a significant role in water resources management, especially for
flood mitigation, drought warning, and reservoir operation. Hence, the current study …

A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset

RC Deo, X Wen, F Qi - Applied Energy, 2016 - Elsevier
A solar radiation forecasting model can be utilized is a scientific contrivance for investigating
future viability of solar energy potentials. In this paper, a wavelet-coupled support vector …

Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq

ZM Yaseen, O Jaafar, RC Deo, O Kisi, J Adamowski… - Journal of …, 2016 - Elsevier
Monthly stream-flow forecasting can yield important information for hydrological applications
including sustainable design of rural and urban water management systems, optimization of …

Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition

M Ali, R Prasad - Renewable and Sustainable Energy Reviews, 2019 - Elsevier
Data-intelligent algorithms designed for forecasting significant height of coastal waves over
the relatively short time period in coastal zones can generate crucial information about …

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

RC Deo, M Şahin - Atmospheric Research, 2015 - Elsevier
The prediction of future drought is an effective mitigation tool for assessing adverse
consequences of drought events on vital water resources, agriculture, ecosystems and …

Application of the artificial neural network model for prediction of monthly standardized precipitation and evapotranspiration index using hydrometeorological …

RC Deo, M Şahin - Atmospheric research, 2015 - Elsevier
The forecasting of drought based on cumulative influence of rainfall, temperature and
evaporation is greatly beneficial for mitigating adverse consequences on water-sensitive …