Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in …

RC Deo, M Şahin - Renewable and Sustainable Energy Reviews, 2017 - Elsevier
Forecasting solar radiation (G) is extremely crucial for engineering applications (eg design
of solar furnaces and energy-efficient buildings, solar concentrators, photovoltaic-systems …

Nonstationary time series transformation methods: An experimental review

R Salles, K Belloze, F Porto, PH Gonzalez… - Knowledge-Based …, 2019 - Elsevier
Data preprocessing is a crucial step for mining and learning from data, and one of its primary
activities is the transformation of data. This activity is very important in the context of time …

Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis

T Chakraborty, I Ghosh - Chaos, Solitons & Fractals, 2020 - Elsevier
Abstract The coronavirus disease 2019 (COVID-19) has become a public health emergency
of international concern affecting 201 countries and territories around the globe. As of April …

Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks

KU Jaseena, BC Kovoor - Energy Conversion and Management, 2021 - Elsevier
The goal of sustainable development can be attained by the efficient management of
renewable energy resources. Wind energy is attracting attention worldwide due to its …

Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks

AI Saba, AH Elsheikh - Process safety and environmental protection, 2020 - Elsevier
Abstract SARS-CoV-2 (COVID-19) is a new Coronavirus, with first reported human infections
in late 2019. COVID-19 has been officially declared as a universal pandemic by the World …

[HTML][HTML] An integrated statistical-machine learning approach for runoff prediction

AK Singh, P Kumar, R Ali, N Al-Ansari… - Sustainability, 2022 - mdpi.com
Nowadays, great attention has been attributed to the study of runoff and its fluctuation over
space and time. There is a crucial need for a good soil and water management system to …

A short‐term load forecasting method based on GRU‐CNN hybrid neural network model

L Wu, C Kong, X Hao, W Chen - Mathematical problems in …, 2020 - Wiley Online Library
Short‐term load forecasting (STLF) plays a very important role in improving the economy
and stability of the power system operation. With the smart meters and smart sensors widely …

Prediction of hourly air temperature based on CNN–LSTM

J Hou, Y Wang, J Zhou, Q Tian - Geomatics, Natural Hazards and …, 2022 - Taylor & Francis
The prediction accuracy of hourly air temperature is generally poor because of random
changes, long time series, and the nonlinear relationship between temperature and other …

A hybrid approach of adaptive wavelet transform, long short-term memory and ARIMA-GARCH family models for the stock index prediction

M Zolfaghari, S Gholami - Expert Systems with Applications, 2021 - Elsevier
Modelling and forecasting the stock price constitute an important area of financial research
for both academics and practitioners. This study seeks to determine whether improvements …

Forecasting river water temperature time series using a wavelet–neural network hybrid modelling approach

R Graf, S Zhu, B Sivakumar - Journal of Hydrology, 2019 - Elsevier
Accurate and reliable water temperature forecasting models can help in environmental
impact assessment as well as in effective fisheries management in river systems. In this …