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Article
Prediction of Irrigation Water Quality Indices Using Random Committee, Discretization Regression, REPTree, and Additive Regression
This study aims to evaluate the performance of four ensemble machine learning methods, i.e., Random Committee, Discretization Regression, Reduced Error Pruning Tree, and Additive Regression, to estimate water ...
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Article
Open AccessForecasting monthly pan evaporation using hybrid additive regression and data-driven models in a semi-arid environment
Exact estimation of evaporation rates is very important in a proper planning and efficient operation of water resources projects and agricultural activities. Evaporation is affected by many driving forces char...
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Article
Modeling of pan evaporation based on the development of machine learning methods
For the effective planning and management of water resources and the implementation of related strategies, evaporation losses must be estimated properly, especially in regions that are prone to drought. Change...
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Article
Modelling the IDF curves using the temporal stochastic disaggregation BLRP model for precipitation data in Najaf City
Precipitation data for long period with short time scales extending from limited minute to daily time steps is the principal requirement in water resources engineering for planning, design, and operation of wa...
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Article
Prediction of lead (Pb) adsorption on attapulgite clay using the feasibility of data intelligence models
This study investigates the performance of support vector machine (SVM), multivariate adaptive regression spline (MARS), and random forest (RF) models for predicting the lead (Pb) adsorption by attapulgite cla...
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Article
Modeling the monthly pan evaporation rates using artificial intelligence methods: a case study in Iraq
In arid areas, the estimation of evaporation rates plays a considerable role on both water resources management and agricultural activities. Hence, it is of utmost importance to determine the best model to pre...
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Article
Random forest, support vector machine, and neural networks to modelling suspended sediment in Tigris River-Baghdad
Suspended sediment is one of the most influential parameters on the water bodies’ pollution. It can carry different pollutants with different concentration through the suspension movement in the flow. Therefor...
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Article
Future predictions of precipitation and temperature in Iraq using the statistical downscaling model
Iraq is facing a critical water crisis that has ever experienced. This necessitates a wise management for present and future water resources. Future water availability is mainly influenced by the impacts of cl...
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Article
Modelling the root zone soil moisture using artificial neural networks, a case study
Surface soil moisture constitutes a major component in the Earth’s water cycle. In many cases, modelling and predicting soil moisture represent a serious problem in water resources field due to the problematic...
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Article
Runoff and sediment yield modeling by means of WEPP in the Bautzen dam catchment, Germany
Soil erosion by water is one of the most widespread forms of soil degradation in Europe. There are many undesirable consequences of soil erosion due to water such as loss of water storage capacity in reservoi...
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Article
Assessing the Impacts of Climate Change on Hydrology of the Upper Reach of the Spree River: Germany
The aim of this study was to assess the potential impacts of future climate change on the hydrological response in the upper reach of the Spree River catchment using the Soil and Water Assessment Tool (SWAT). ...
