Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Usa Wannasingha Humphries"'
Autor:
Muhammad Waqas, Adila Naseem, Usa Wannasingha Humphries, Phyo Thandar Hlaing, Muhammad Shoaib, Sarfraz Hashim
Publikováno v:
Farming System, Vol 3, Iss 1, Pp 100114- (2025)
The agricultural sector is vulnerable to climate change (CC). Various climate-related extremes, such as droughts, heat waves, unpredictable rainfall patterns, storms, floods, and an increase in insect pests, have adversely affected farmers' livelihoo
Externí odkaz:
https://doaj.org/article/e9a8d34ee2d643af81dcbf817ec1c8c1
Publikováno v:
Results in Engineering, Vol 24, Iss , Pp 102997- (2024)
Climate variability, trend analysis, and accurate forecasting are vital in a country's effective water resource management and strategic planning. Precipitation and temperature are critical indicators for assessing the effects of climate change (CC)
Externí odkaz:
https://doaj.org/article/452677448be54d28aaa132dd666f00a6
Publikováno v:
MethodsX, Vol 13, Iss , Pp 102792- (2024)
Understanding hydrological processes necessitates the use of modeling techniques due to the intricate interactions among environmental factors. Estimating model parameters remains a significant challenge in runoff modeling for ungauged catchments. Th
Externí odkaz:
https://doaj.org/article/9594f46ab56e4d9187072411eae92895
Publikováno v:
MethodsX, Vol 13, Iss , Pp 102954- (2024)
This study investigates the influence of El Niño Southern Oscillation (ENSO) on monthly precipitation anomaly (PPTA) in Northeast Thailand using 30 years (1993–2022) data obtained from 27 weather stations of the Thai Meteorological Department (TMD
Externí odkaz:
https://doaj.org/article/d58e0593cac0417585c62eb08692b413
Publikováno v:
MethodsX, Vol 13, Iss , Pp 102946- (2024)
The rapid advancement in Artificial Intelligence (AI) and big data has developed significance in the water sector, particularly in hydrological time-series predictions. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks have
Externí odkaz:
https://doaj.org/article/c05a4bdb72214b818d40a20cc6992331
Autor:
Usa Wannasingha Humphries, Muhammad Waqas, Phyo Thandar Hlaing, Porntip Dechpichai, Angkool Wangwongchai
Publikováno v:
Results in Engineering, Vol 23, Iss , Pp 102417- (2024)
The selection of General Circulation Models (GCMs) is critical due to computational limitations and underlying uncertainties. This study provides a comprehensive assessment of three bias correction (BC) methods, namely the delta change method (DT), q
Externí odkaz:
https://doaj.org/article/b0f7e267abcd4f24905dda83d918357e
Autor:
Usa Wannasingha Humphries, Muhammad Waqas, Phyo Thandar Hlaing, Angkool Wangwongchai, Porntip Dechpichai
Publikováno v:
Smart Agricultural Technology, Vol 8, Iss , Pp 100435- (2024)
Climate change (CC) is causing a significant threat to agriculture, a sector complicatedly tied to natural resources. Changes in precipitation patterns, atmospheric water content, and rising temperatures intensely affect global agriculture, especiall
Externí odkaz:
https://doaj.org/article/0401f657c23f46d7956731ac7e9f85f8
Autor:
Usa Wannasingha Humphries, Muhammad Waqas, Phyo Thandar Hliang, Porntip Dechpichai, Angkool Wangwongchai
Publikováno v:
AIP Advances, Vol 14, Iss 8, Pp 085026-085026-26 (2024)
Accurate drought prediction is crucial for enhancing resilience and managing water resources. Developing robust forecasting models and understanding the variables influencing their outcomes are essential. This study developed models that integrate wa
Externí odkaz:
https://doaj.org/article/0f7a754a8e2c4bde97ac3e128631cddf
Autor:
Boobphachard Chansawang, Rahat Zarin, Usa Wannasingha Humphries, Prungchan Wongwises, Muhammad Waqas, Angkool Wangwongchai
Publikováno v:
AIP Advances, Vol 14, Iss 5, Pp 055135-055135-15 (2024)
Geophysical domains typically exhibit intricate, irregular boundaries characterized by fractal-like geometries, while underlying physical processes operate across a broad spectrum of spatial scales. The challenge lies in generating spatial discretiza
Externí odkaz:
https://doaj.org/article/e266c9a088ab467eac1271521f2a0f3c
Autor:
Muhammad Waqas, Usa Wannasingha Humphries, Angkool Wangwongchai, Porntip Dechpichai, Rahat Zarin, Phyo Thandar Hlaing
Publikováno v:
Alexandria Engineering Journal, Vol 86, Iss , Pp 557-576 (2024)
Selecting appropriate input variables for developing a rainfall prediction model is significantly difficult. The present study proposed an innovative framework for input variable selection (IVS) model bootstrapped long short-term recurrent neural net
Externí odkaz:
https://doaj.org/article/a052c72268b84cf49664b411a6afddb2