Annual precipitation forecast for west, southwest, and south provinces of Iran using artificial neural networks

Rainfed agriculture plays an important role in the agricultural production of the southern and western provinces of Iran. In rainfed agriculture, the adequacy of annual precipitation is considered as an important factor for dryland field and supplemental irrigation management. Different methods can be used for predicting the annual precipitation based on climatic and non-climatic inputs. Among which artificial neural networks (ANN) is one of these methods. The purpose of this research was to predict the annual precipitation amount (millimeters) in the west, southwest, and south of Islamic Republic of Iran with the total area of 394,259?km², by applying non-climatic inputs according to the long-time average precipitation in each station (millimeters), 47.5?mm precipitation since the first of autumn (day), t _47.5, and other effective parameters like coordinate and altitude of the stations, by using the artificial neural networks. In order to intelligently estimate the annual amount of precipitation in the study regions (ten provinces), feedforward backpropagation artificial neural network model has been used (method I). To predict the annual precipitation amount more accurately, the region under study was divided into three sub-regions, according to the precipitation mapping, and for each sub-region, the neural networks were developed using t _47.5 and long-time average annual precipitation in each station (method II). It is concluded that neural networks did not significantly increase the prediction accuracy in the study area compared with multiple regression model proposed by other investigators. However, in case of ANN, it is better to use a structure of 2–6–6–10–1 and Levenberg–Marquardt learning algorithm and sigmoid logistic activation function for prediction of annual precipitation.     

Caractérisation géochimique des fluides associés aux minéralisations PbZn de Bou-Dahar (Maroc)

The Bou-Dahar PbZn Mississippi Valley deposits located in the eastern part of the High Atlas Range (Morocco) are hosted by a Liassic reefal complex. Fluid inclusion and ‘crush-leach’ data show that two distinct fluids were involved in the mineralisation deposition: a warmer, more saline fluid (180?°C, $>25\text{wt\%}$ NaCl equivalent) and a cooler, less saline fluid (70?°C, 16 wt% equivalent NaCl). Mixing of these two fluids resulted in the precipitation of the ore. The solute composition of the ore-forming brine suggests that the MVT mineralising fluids were probably a mixture of halite-dissolution fluids and evaporated seawater. To cite this article: S. Adil et al., C. R. Geoscience 336 (2004).