Artificial neural network approaches for disaster management: A literature review

S Guha, RK Jana, MK Sanyal - International Journal of Disaster Risk …, 2022 - Elsevier
Disaster management (DM) is one of the leading fields that deal with the humanitarian
aspects of emergencies. The field has attracted researchers because of its ever-increasing …

A review of the chemical extraction of chitosan from shrimp wastes and prediction of factors affecting chitosan yield by using an artificial neural network

A Hosney, S Ullah, K Barčauskaitė - Marine Drugs, 2022 - mdpi.com
There are two viable options to produce shrimp shells as by-product waste, either within the
shrimp production phases or when the shrimp are peeled before cooking by the end user …

[HTML][HTML] Early ecological security warning of cultivated lands using RF-MLP integration model: A case study on China's main grain-producing areas

S Zou, L Zhang, X Huang, FB Osei, G Ou - Ecological Indicators, 2022 - Elsevier
The evaluation and early warning of the ecological security of cultivated land are crucial to
food security and social stability in the post-epidemic era. In this study, we construct a …

Spatiotemporal analysis and predicting rainfall trends in a tropical monsoon-dominated country using MAKESENS and machine learning techniques

MM Monir, M Rokonuzzaman, SC Sarker, E Alam… - Scientific Reports, 2023 - nature.com
Spatiotemporal rainfall trend analysis as an indicator of climatic change provides critical
information for improved water resource planning. However, the spatiotemporal changing …

Improving monthly rainfall forecast in a watershed by combining neural networks and autoregressive models

A Pérez-Alarcón, D Garcia-Cortes… - Environmental …, 2022 - Springer
The main aim of the rain forecast is to determine rain occurrence conditions in a specific
location. This is considered of vital importance to assess the availability of water resources …

Potential of artificial intelligence-based techniques for rainfall forecasting in Thailand: A comprehensive review

M Waqas, UW Humphries, A Wangwongchai… - Water, 2023 - mdpi.com
Rainfall forecasting is one of the most challenging factors of weather forecasting all over the
planet. Due to climate change, Thailand has experienced extreme weather events, including …

Improving Value-at-Risk forecast using GA-ARMA-GARCH and AI-KDE models

K Syuhada, V Tjahjono, A Hakim - Applied Soft Computing, 2023 - Elsevier
The classical autoregressive moving average (ARMA) and generalized autoregressive
conditional heteroskedastic (GARCH) models have been widely adopted to forecast Value …

Prediction of monthly precipitation using various artificial models and comparison with mathematical models

Y Kassem, H Gökçekuş, AAS Mosbah - Environmental Science and …, 2023 - Springer
Precipitation (PP) prediction is an interesting topic in the meteorology or hydrology field
since it is directly related to agriculture, the management of water resources in hydrologic …

Islanding detection and power quality disturbance classification in multi DG based microgrid using down sampling empirical mode decomposition and multilayer …

AR Choudhury, P Nayak, RK Mallick… - International Journal of …, 2024 - degruyter.com
Abstract Power Quality, Equipment and Personnel safety of any distributed generation (DG)
system connected to utility Grid merely depends on accurate detection of Islanding and non …

Do quadratic and Poisson regression models help to predict monthly rainfall?

Y Kassem, H Gökçekuş - Desalination and Water Treatment, 2021 - Elsevier
Agricultural water scarcity in the primarily rainfed agricultural system of Jigawa State in
Nigeria is more related to the variability of rainfall. Rainfed subsistence farming systems in …