ArticleEnvironmental science and pollution research international2020
Application of hybrid ANN-whale optimization model in evaluation of the field capacity and the permanent wilting point of the soils.
Article in Environmental science and pollution research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Development of a new hybrid model to enhance streamflow estimation using artificial neural network and reptile search algorithm.Scientific reports · 2025Article
- A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation.Environmental science and pollution research international · 2021Article
- Modeling groundwater quality by using hybrid intelligent and geostatistical methods.Environmental science and pollution research international · 2020Article
- Letter to the editor "comparing artificial intelligence techniques for chlorophyll-a prediction in US lakes".Environmental science and pollution research international · 2020Article
- Letter to the editor "Modeling daily suspended sediment load using improved support vector machine model and genetic algorithm".Environmental science and pollution research international · 2020Article
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Abstract
Field capacity (FC) and permanent wilting point (PWP) are two important properties of the soil when the soil moisture is concerned. Since the determination of these parameters is expensive and time-consuming, this study aims to develop and evaluate a new hybrid of artificial neural network model coupled with a whale optimization algorithm (ANN-WOA) as a meta-heuristic optimization tool in defining the FC and the PWP at the basin scale. The simulated results were also compared with other core optimization models of ANN and multilinear regression (MLR). For this aim, a set of 217 soil samples were taken from different regions located across the West and East Azerbaijan provinces in Iran, partially covering four important basins of Lake Urmia, Caspian Sea, Persian Gulf-Oman Sea, and Central-Basin of Iran. Taken samples included portion of clay, sand, and silt together with organic matter, which were used as independent variables to define the FC and the PWP. A 80-20 portion of the randomly selected independent and dependent variable sets were used in calibration and validation of the predefined models. The most accurate predictions for the FC and PWP at the selected stations were obtained by the hybrid ANN-WOA models, and evaluation criteria at the validation phases were obtained as 2.87%, 0.92, and 2.11% respectively for RMSE, R
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