Review
Computer Science, Artificial Intelligence
Mustafa Abed, Monzur Alam Imteaz, Ali Najah Ahmed
Summary: This comprehensive study reviews the latest AI techniques for estimating pan evaporation (Ep), an important parameter for water resource management and irrigation planning. It analyzes the input data categories, time steps, properties, and capabilities of different AI models used for estimating Ep across various regions. The study recommends the use of transformer neural networks for Ep estimation due to their unique architecture and promising performance.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Meteorology & Atmospheric Sciences
Tianci Yao, Hongwei Lu, Qing Yu, Wei Feng, Yuxuan Xue
Summary: Pan evaporation analysis in the Qinghai-Tibet Plateau (QTP) using the PenPan model showed agreement with observations, indicating high evaporation in areas with water limitation or strong solar radiation. A widespread decrease in evaporation around 1993 was observed, with exceptions like the continuous increase in the southwest QTP. After 1993, annual evaporation trends accelerated in both the QTP and surrounding areas, with different factors influencing evaporation before and after 1993.
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2022)
Article
Engineering, Environmental
Shohreh Didari, Rezvan Talebnejad, Mehdi Bahrami, Mohammad Reza Mahmoudi
Summary: This study analyzed the effectiveness of the LASSO model in selecting variables for predicting dryland wheat yield in southwestern Iran. The results showed that temperature, evaporation, and extreme temperatures are effective meteorological variables in estimating the yield. The monthly timescale provides the lowest estimation error compared to other timescales.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2023)
Article
Computer Science, Theory & Methods
Wondmagegn Taye Abebe, Demeke Endalie
Summary: Global climate change is affecting water resources and various aspects of life in many countries. In Ethiopia, rainfall is a crucial climate factor that impacts the livelihood and well-being of the majority of the population. Accurate rainfall predictions are vital for agricultural planning, and they also have applications in areas such as farming, early warning systems, drought mitigation, disaster prevention, and insurance policy.
JOURNAL OF BIG DATA
(2023)
Article
Environmental Sciences
Jiaqi Li, Bing Gao
Summary: This study reconstructed the Epan at meteorological stations in China from 1985 to 2017 using the PenPan model, and compared the differences in Epan between urban and adjacent rural stations. The results showed that urbanization increased the annual Epan at urban stations by approximately 50-200 mm compared with rural stations. Urbanization also tended to enhance the trend in Epan during the rapid urbanization period. The increase in vapor pressure deficit and air temperature caused by urbanization was the main reason for the differences in Epan between urban and rural stations.
Article
Environmental Sciences
Yuan Zhang, Bojie Fu, Xiaoming Feng, Naiqing Pan
Summary: Drought is a common climate extreme that negatively affects ecohydrology. The thresholds of drought severity and duration that cause changes in ecohydrological variables are largely unknown. Furthermore, the effects of drought on ecohydrological variables vary under climate change conditions.
Article
Green & Sustainable Science & Technology
Muhammet Omer Dis
Summary: The study fills data gaps by establishing a new meteorological station and using Class A evaporation pan observations, and utilizes an artificial intelligence model to process the continuous evaporation data nonlinearly. Evaluation shows that temperature and wind-driven simulations have the highest correlation and performance compared to solely precipitation-based models in predicting evaporation.
Article
Agronomy
Hong Wang, Fubao Sun, Fa Liu, Tingting Wang, Wenbin Liu, Yao Feng
Summary: Measurements of evaporation from pans have been used to estimate crop water requirements in China. This study compared different models to reconstruct missing daily evaporation data from 1951 to 2020. The results showed that machine learning models (MARS and RF) outperformed the physical model (PenPan), and multiple linear regression (MLR) performed the worst. The missing data were successfully predicted using RF, and the reconstructed evaporation had similar characteristics to the observed data.
AGRICULTURAL WATER MANAGEMENT
(2023)
Article
Engineering, Civil
O. E. Adeyeri, P. Laux, K. A. Ishola, W. Zhou, I. A. Balogun, Z. D. Adeyewa, H. Kunstmann
Summary: In this study, two different homogenisation methods were used to homogenise the precipitation and air temperature data in the Lake Chad Basin. The results showed the existence of unnatural breakpoints in the raw data, and the homogenisation methods significantly improved the quality of the climate data. The study highlights the importance of robust homogenisation in climate and hydrological studies to reduce errors and enhance the reliability of the information.
JOURNAL OF HYDROLOGY
(2022)
Article
Geosciences, Multidisciplinary
Ammar Albalasmeh, Osama Mohawesh, Mamoun Gharaibeh, Sanjit Deb, Lindsey Slaughter, Ali El Hanandeh
Summary: The generalized regression neural network (GRNN) can be used to predict the saturated hydraulic conductivity (K-sat) of soil, and soil texture and electrical conductivity can be used as the influential features in the prediction model. The GRNN model using a small dataset and limited features can provide reliable predictions of K-sat.
Article
Water Resources
Ahmed Elbeltagi, Mustafa Al-Mukhtar, N. L. Kushwaha, Nadhir Al-Ansari, Dinesh Kumar Vishwakarma
Summary: This study evaluates the predictability of coupling the additive regression model (AR) with four ensemble machine-learning algorithms for estimating pan evaporation rates. The results show that the AR-M5P model estimates evaporation with higher accuracy than others when combining wind speed, relative humidity, and the minimum and mean temperatures as input parameters. The outcomes of this study prove the superior performance of the hybridized methods in addressing complex hydrological relationships and can be employed for other environmental problems.
APPLIED WATER SCIENCE
(2023)
Article
Geosciences, Multidisciplinary
Ali Barzkar, Mohammad Najafzadeh, Farshad Homaei
Summary: This study established SPEI values for various climates using three robust Artificial Intelligence (AI) models: Gene Expression Programming (GEP), Model Tree (MT), and Multivariate Adaptive Regression Spline (MARS). The M5 version of MT was found to provide the most accurate SPEI prediction across all climatic situations in comparison with GEP and MARS techniques. Reliability analysis for all synoptic stations indicated that as the probability of exceedance values declined to below 75%, drought situations varied from Normal to Very Extreme Humidity.
Article
Green & Sustainable Science & Technology
Salma Zaim, Mohamed El Ibrahimi, Asmae Arbaoui, Abderrahim Samaouali, Mouhaydine Tlemcani, Abdelfettah Barhdadi
Summary: This study proposes the use of artificial neural networks and XGBoost algorithm for modeling hourly global solar radiation in a humid climate. Important meteorological data are selected and validated, and two ANN models and one XGBoost model are chosen with similar performances, with coefficient of determination values of 98% and 97% respectively. Statistical indicators prove to be effective in assessing the accuracy and fidelity of each model. The intent of the modeling in terms of accuracy, simplicity, and fidelity is a crucial factor in selecting the model algorithm to adopt.
Article
Thermodynamics
Parham Jafari, Saeed Sarmadi, Shahin Tasoujian, Hadi Ghasemi
Summary: This study introduces a general AI platform to guide the discovery of hierarchical structures for extreme thermal management of high-performance photonics/electronics, which can effectively predict heat flux in different structures. This predictive platform provides a foundation for addressing the ongoing challenge of thermal management in a broad spectrum of technologies including electronics, hypersonic aviation, and electric vehicles.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2021)
Article
Agronomy
Alban Echchelh, Tim Hess, Ruben Sakrabani, Stephane Prigent, Alexandros Stefanakis
Summary: Produced water (PW) is the primary waste stream from oil and gas extraction, with half of global PW volume currently managed through controversial methods. Reusing PW for crop irrigation in dry regions can create environmental, economic, and social value, but challenges remain due to high salinity, sodicity, and alkalinity. Despite higher costs compared to disposal, successful PW irrigation requires specific treatment and management strategies to maintain soil health and crop yield.
AGRICULTURAL WATER MANAGEMENT
(2021)
Article
Water Resources
Abdullah A. Alsumaiei, Ryan T. Bailey
HYDROLOGICAL PROCESSES
(2018)
Article
Water Resources
Abdullah A. Alsumaiei, Ryan T. Bailey
HYDROLOGICAL PROCESSES
(2018)
Article
Environmental Sciences
Abdullah A. Alsumaiei
Article
Geosciences, Multidisciplinary
Duaa Almousawi, Jaber Almedeij, Abdullah A. Alsumaiei
ARABIAN JOURNAL OF GEOSCIENCES
(2020)
Article
Environmental Sciences
Dawod Aldosari, Jaber Almedeij, Abdullah A. Alsumaiei
ENVIRONMENTAL AND ECOLOGICAL STATISTICS
(2020)
Article
Environmental Sciences
Abdullah A. Alsumaiei, Mosaed S. Alrashidi
Article
Meteorology & Atmospheric Sciences
Abdullah A. Alsumaiei