[HTML][HTML] A basic review of fuzzy logic applications in hydrology and water resources

S Kambalimath, PC Deka - Applied Water Science, 2020 - Springer
In recent years, fuzzy logic has emerged as a powerful technique in the analysis of
hydrologic components and decision making in water resources. Problems related to …

Recent advances in evapotranspiration estimation using artificial intelligence approaches with a focus on hybridization techniques—a review

MY Chia, YF Huang, CH Koo, KF Fung - Agronomy, 2020 - mdpi.com
Difficulties are faced when formulating hydrological processes, including that of
evapotranspiration (ET). Conventional empirical methods for formulating these possess …

[HTML][HTML] Soil water erosion susceptibility assessment using deep learning algorithms

K Khosravi, F Rezaie, JR Cooper, Z Kalantari… - Journal of …, 2023 - Elsevier
Accurate assessment of soil water erosion (SWE) susceptibility is critical for reducing land
degradation and soil loss, and for mitigating the negative impacts of erosion on ecosystem …

A novel framework for risk assessment and resilience of critical infrastructure towards climate change

N Kumar, V Poonia, BB Gupta, MK Goyal - Technological Forecasting and …, 2021 - Elsevier
The persistent extreme weather events (floods, droughts, heatwaves, etc.) are increasing the
risks towards critical infrastructure (CI). Therefore, it is essential to enhance the resilience of …

Machine learning and artificial intelligence to aid climate change research and preparedness

C Huntingford, ES Jeffers, MB Bonsall… - Environmental …, 2019 - iopscience.iop.org
Climate change challenges societal functioning, likely requiring considerable adaptation to
cope with future altered weather patterns. Machine learning (ML) algorithms have advanced …

An interpretable machine learning approach based on DNN, SVR, Extra Tree, and XGBoost models for predicting daily pan evaporation

A El Bilali, T Abdeslam, N Ayoub, H Lamane… - Journal of …, 2023 - Elsevier
Evaporation is an important hydrological process in the water cycle, especially for water
bodies. Machine Learning (ML) models have become accurate and powerful tools in …

Predicting compressive strength of lightweight foamed concrete using extreme learning machine model

ZM Yaseen, RC Deo, A Hilal, AM Abd, LC Bueno… - … in Engineering Software, 2018 - Elsevier
In this research, a machine learning model namely extreme learning machine (ELM) is
proposed to predict the compressive strength of foamed concrete. The potential of the ELM …

Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran

R Moazenzadeh, B Mohammadi… - Engineering …, 2018 - Taylor & Francis
Evaporation accounts for varying shares of water balance under different climatic conditions,
and its correct prediction poses a significant challenge before water resources management …

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 …

Modeling the emission characteristics of the hydrogen-enriched natural gas engines by multi-output least-squares support vector regression: Comprehensive …

T Hai, DH Kadir, A Ghanbari - Energy, 2023 - Elsevier
The hydrogen-enriched natural gas engines (HENGEs) have recently found huge popularity.
Despite the broad range of applications of the HENGE, their environmentally-associated …