Neural networks for karst groundwater management: case of the Lez spring (Southern France)

Abstract

Karst aquifers provide water resources for a large part of the Mediterranean population and water resources become a strategic problem during summer when the population increases due to tourism. To help managers to optimize the exploitation of karst aquifer waters this paper studies the ability of a neural model to efficiently simulate and forecast water table levels a few months ahead during the dry season. The neural model is a recurrent multilayer perceptron that learns the relations between inputs (mainly rainfall and pumping discharge) and output (water table level). After a training step using several years of data, the model is assessed on a never seen year to be validated. Particular attention is devoted to the dry season (May–September). The model achieved good forecast of the maximal observed drawdown. To investigate the usefulness of the model as an operational tool, various scenarios of future rainfalls and future pumping are proposed and associated forecasts are analysed. Scenarios are the following: zero-rainfall scenario and mean forecast rainfall 1 week ahead; in both cases, the model proposes accurate estimations of the maximal drawdown. In a second step, pumping scenarios are investigated: a mean pumping scenario during the dry season and the maximum pumping scenario taking into account the actual pumps. One more time, it is demonstrated that the maximal drawdown can be efficiently forecast for the dry season. The methodology is generic and can thus be used with profit by managers on other karst aquifers.