Estimating the Aquifer’s Renewable Water to Mitigate the Challenges of Upcoming Megadrought Events

Water Resources Management - In arid and semi-arid regions of the world, the occurrence of prolonged drought events (megadroughts) associated with climate change can seriously affect the balance...     

Preparation and characterisation of diopside-based glass–ceramic foams

Foaming and crystallisation behaviours of compacted glass powders based on a diopside glass–ceramic composition were investigated using the sintering route. The foaming agent was 2 wt.% SiC particles. The effect of PbO on the foaming ability of glasses was investigated. The results showed that the addition of PbO not only improved the foaming ability, by improving the wettability of the glass–SiC particles but also increased the crystallisation temperature and widened the temperature interval between the dilatometric softening point and the onset of crystallisation. The glass–SiC wetting angle was decreased from 85° for the lead-free glass, to 55° for the glass that contains 15 wt.% PbO.     

A Novel Idea for Groundwater Resource Management during Megadrought Events

Water Resources Management - Due to the effects of global climate change on duration, frequency and number of drought events, the occurrence of prolonged droughts, referred to as...     

Correction to: A Novel Idea for Groundwater Resource Management during Megadrought Events

Water Resources Management - The original version of this article unfortunately contains an oversight in the spelling of name of one of the cited authors.     

Application of NN-ARX Model to Predict Groundwater Levels in the Neishaboor Plain, Iran

There is no doubt that groundwater is an important and vital source of water supply in arid and semi-arid areas. Therefore, prediction of groundwater level fluctuations is necessary for planning conjunctive use in these areas. This research was aimed to predict groundwater levels in the Neishaboor plain using Neural Network – AutoRegressive eXtra input (NN-ARX) and Static-NN models. The NN-ARX model determines a nonlinear ARX model of a dynamic system by training a hidden layer neural network with the Levenberg-Marquardt algorithm. In this model the current outputs depend not only on the current inputs, but also on the inputs and outputs at the pervious time periods. The available observation wells in the study area were clustered according to their fluctuation behavior using the “Ward” method, which resulted in six areal zones. Then, for each cluster, an observation well was selected as its representative, and for each zone, values of monthly precipitation, temperature and groundwater extraction were estimated. The best input of the Static-NN model was identified using combination of Gamma Test and Genetic Algorithm. Also, Gamma Test is applied to identify the length of the training dataset. The results showed that the NN-ARX model was suitable and more practical. The performance indicators (R ²?=?0.97, RMSE?=?0.03 m, ME?=?--0.07 m and R ²?=?0.81, RMSE?=?0.35 m, ME?=?0.60 m, respectively for the best and worst performance of model) reveals the effectiveness of this model. Moreover, these results were compared with the results of a static-NN model using t-test, which showed the superiority of the NN-ARX over the static-NN.