The results of this study revealed that during long-term cultivation, the size of the areas with high and very high salinity and alkalinity classes were increased in the studied regions, especially in pistachio cultivation during irrigation with inadequate water and playa margin lands.
Based on the developed hybrid intelligence models, the ANFIS hybrid can be effective for predicting soil properties. Improved mapping of soil properties with hybrid ANFIS models is desirable for several applications such as precision agricultural, soil preservation and weather, drought and flood forecasting.
The results of this study revealed that during long-term cultivation, the size of the areas with high and very high salinity and alkalinity classes were increased in the studied regions, especially in pistachio cultivation during irrigation with inadequate water and playa margin lands.
The soil science community needs to communicate about soils and the use of soil information to various audiences, especially to the general public and public authorities. In this global review article, we synthesis information pertaining to museums solely dedicated to soils or which contain a permanent exhibition on soils.
The soil science community needs to communicate about soils and the use of soil information to various audiences, especially to the general public and public authorities. In this global review article, we synthesis information pertaining to museums solely dedicated to soils or which contain a permanent exhibition on soils.
Land use change and SOCS maps that identify areas of high risk of degradation can be used for sustainable land management and decreasing the effects of these changes on the environment. This study assessed the spatial and temporal distribution of land use transformation and SOCS depletions.
Land use change and SOCS maps that identify areas of high risk of degradation can be used for sustainable land management and decreasing the effects of these changes on the environment. This study assessed the spatial and temporal distribution of land use transformation and SOCS depletions.
Estimating sediment load of rivers is one of the major problems in river engineering that has been using various data mining algorithms and variables. This study investigates the usefulness of geo-morphometric factors and machine learning models for predicting suspended sediment load in several river basins.
Estimating sediment load of rivers is one of the major problems in river engineering that has been using various data mining algorithms and variables. This study investigates the usefulness of geo-morphometric factors and machine learning models for predicting suspended sediment load in several river basins.
Artificial neural network and nonlinear autoregressive models are very powerful methods for accurate prediction of respiratory mortality and mobility with at least three inputs. These findings strongly support the need for policymakers to set targets to reduce carbon monoxide and nitrogen monoxide concentrations in the environment.