New Zealand climate change information derived by multivariate statistical and artificial neural networks approaches

2001 ◽  
Vol 21 (11) ◽  
pp. 1415-1433 ◽  
Author(s):  
F.S. Mpelasoka ◽  
A.B. Mullan ◽  
R.G. Heerdegen
Keyword(s):  
Climate Change ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
New Zealand ◽  
Multivariate Statistical ◽  
Climate Change Information ◽  
Artificial Neural

Using artificial neural networks to forecast producer price index for New Zealand

2020 ◽  
Vol 7 (3) ◽  
pp. 191
Author(s):  
Lin Lin Zhao ◽  
Bill Wang ◽  
Jasper Mbachu ◽  
Temitope Egbelakin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
New Zealand ◽  
Price Index ◽  
Producer Price ◽  
Producer Price Index ◽  
Artificial Neural

Delineation of a buried volcanic system in Kora prospect off New Zealand using artificial neural networks and its implications

2019 ◽  
Vol 161 ◽  
pp. 56-75 ◽  
Author(s):  
Priyadarshi Chinmoy Kumar ◽  
Kalachand Sain ◽  
Animesh Mandal
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
New Zealand ◽  
Volcanic System ◽  
Artificial Neural

Using artificial neural networks to model the suitability of coastline for breeding by New Zealand fur seals (Arctocephalus forsteri)

2002 ◽  
Vol 148 (2) ◽  
pp. 111-131 ◽  
Author(s):  
Corey J.A Bradshaw ◽  
Lloyd S Davis ◽  
Martin Purvis ◽  
Qingqing Zhou ◽  
George L Benwell
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
New Zealand ◽  
Fur Seals ◽  
Artificial Neural

scholarly journals Application of Artificial Neural Networks to Project Reference Evapotranspiration under Climate Change Scenarios

2021 ◽  
Author(s):  
Junaid Maqsood ◽  
Aitazaz A. Farooque ◽  
Farhat Abbas ◽  
Travis Esau ◽  
Xander Wang ◽  
...  
Keyword(s):  
Climate Change ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
General Circulation ◽  
Reference Evapotranspiration ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Climate Change Scenarios ◽  
Artificial Neural

Abstract Evapotranspiration, one of the major elements of the water cycle, is sensitive to climate change. The main objective of this study was to examine the response of reference evapotranspiration (ET0) under various climate change scenarios using artificial neural networks and a general circulation model (GCM) - the Canadian Earth System Model Second Generation (CanESM2). The Hargreaves method was used to calculate ET0 for western, central, and eastern parts of Prince Edward Island. The two input parameters of the Hargreaves method; daily maximum temperature (Tmax), and daily minimum temperature (Tmin) were projected using CanESM2. The Tmax and Tmin were downscaled with the help of statistical downscaling and simulation model (SDSM) for three future periods 2020s (2011–2040), 2050s (2041–2070), and 2080s (2071–2100) under three representative concentration pathways (RCP’s) including RCP 2.6, RCP P4.5, and RCP 8.5, and the. Temporally, there were major changes in Tmax, Tmin, and ET0 for the 2080s under RCP8.5. The temporal variations in ET0 for all RCPs matched the reports in the literature for other similar locations and for RCP8.5 it ranged from 1.63 (2020s) to 2.29 mm/day (2080s). As a next step, a one-dimensional convolutional neural network (1D-CNN), long-short term memory (LSTM), and multilayer perceptron (MLP) were used for estimating ET0 due to the non-linear behavior of ET0 and the limited meteorological input data. High coefficient of correlation (r > 0.95) values for both calibration and validation periods showed the potential of the artificial neural networks in ET0 estimation. The results of this study will help decision makers and water resource managers to quantify the availability of water in future for the island and to optimize the use of island water resources on a sustainable basis.

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