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Quantifying Mineral Resources and Their Uncertainty Using Two Existing Machine Learning Methods
Mineral resources are typically quantified by estimating the grade–tonnage curve for different resource categories. Statutory resource assessment...
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What Ångström—Prescott equation tells us about the cloud and clear-sky climatologies?
The Ångström–Prescott equation defines generically the relationship between solar energy available at ground level and sunshine duration. From the...
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Evaluating the Runoff in the Don Basin: The Need to Change the Paradigm of Hydrological Calculations
AbstractThe results of analysis of variations in the mean annual and minimal runoff in the autumn–winter and summer dry seasons in the Don Basin are...
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Diagnosis and removal of trend component in groundwater elevation data by using experimental semivariograms: An application to Mahdia shallow aquifer system of Tunisia
Groundwater is a precious source in arid and semi-arid regions of the world. Potentiometric level data show a non-stationary behavior due to the...
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On the Estimation of Time Varying AR Processes
In time series analysis auto regressive (AR) modelling of zero mean data is widely used for system identification, signal decorrelation, detection of...
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Climate change impacts on wind power generation
Wind energy is a virtually carbon-free and pollution-free electricity source, with global wind resources greatly exceeding electricity demand....
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Heavy tail distribution and Deuterium excess for drought assessment case of Djelfa- watershed (Algeria)
Global warming has had significant effects on the hydrological cycle. In North Africa, these effects have resulted in a continued decrease in the...
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Investments under non-stationarity: economic evaluation of adaptation pathways
Investment decisions about capital-intensive, long-lived infrastructure are challenging due to uncertainty about their future performance,...
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Prediction of flood frequency under a changing climate, the case of Hare watershed, Rift Valley Basin of Ethiopia
Climate change is causing unpredictable fluctuation in the rainfall patterns and, frequent heavy rainfall that led to catastrophic flooding and a...
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Exploring hybrid models for forecasting \(CO_2\) emissions in drought-prone Somalia: a comparative analysis
Climate change poses significant challenges globally, demanding accurate forecasting methodologies to comprehend and address its consequences. This...
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Analysis of Scalar Time Series
The basics of theory of random processes including the concepts of stationarity, ergodicity, and linearity are given in simple form to help user to...
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Characterizing non-stationary compound extreme events in a changing climate based on large-ensemble climate simulations
The dependence structure of temperature-precipitation compound events is analyzed across Canada using three datasets derived from Canadian Regional...
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Modelling and predicting annual rainfall over the Vietnamese Mekong Delta (VMD) using SARIMA
Climate and rainfall are extremely non-linear and complicated phenomena, which require numerical modelling to simulate for accurate prediction. We...
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Assessing climate change impacts in the Cauvery Basin using evapotranspiration projections and its implications on water management
This study analyses the variability in climate change projections under different Shared Socio-economic Pathway (SSP) scenarios to understand the...
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Non-stationary low flow frequency analysis under climate change
Analysis of low river flows provides important information for effective management of water resources in a region. Despite the critical importance...
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Improving Drought Prediction Accuracy: A Hybrid EEMD and Support Vector Machine Approach with Standardized Precipitation Index
This work combines the Support Vector Machine (SVM) model with Ensemble Empirical Mode Decomposition (EEMD) to present a novel method for drought...
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K-Fold Cross-Validation: An Effective Hyperparameter Tuning Technique in Machine Learning on GNSS Time Series for Movement Forecast
In deformation analysis, irregularly spaced data, extreme values, and anomalies in time series can lead to misleading simulations for forecast...