The size and distribution of water demand within a given structural unit is the basis for the proper operation and planning of the expansion and modernization of the water supply system’s elements. In rural areas, particularly in municipalities adjacent to urban-industrial agglomerations, a change in the use of tap water has been increasingly observed. The water consumption for animal breeding or agricultural use, typical of these areas, has been decreasing and even disappearing. Water has been increasingly used for domestic purposes in single- and multi-family housing as well as for other purposes such as watering lawns and filling residential swimming pools. Taking this into account, this paper presents observations regarding daily water consumption in a municipality adjacent to Wrocław together with an analysis of the possibility of using the exponential smoothing method for the short-term forecasting of daily water consumption. The analyses presented in this paper were carried out using STATISTICA 13 software.

Today, the uncontrolled abstraction of surface water and groundwater resources has created adverse consequences, which include: extinction of living organisms, land subsidence, salinity of coastal aquifers, increased pumping energy. Therefore, the need to manage available water resources is felt more than ever. Among the various water uses (agriculture, drinking, and industry), agriculture accounts for the bulk of water consumption. Due to the climate change and the growing population, determining the appropriate strategy and technology for irrigation is necessary. In the current study, a simulation model is used to numerically simulate the dynamics of daily soil moisture during the potato crop growing season and to estimate crop production and economic benefits. For climatic data, daily observations of a meteorological station have been used. Results and analyses have been presented for all cases of micro and traditional irrigation methods and agricultural management strategies of non-stress irrigation, low irrigation, and rainfed cultivation. The results showed that in the non-stress irrigation method, crop production and net profit are almost equal in both traditional and micro methods. In the low irrigation method, microtechnology has made crop production and net profit 1.75 times more than traditional technology, which indicates the impact of irrigation technology on crop production.

The configuration of the smart irrigation system was designed on the basis of data specific to the parameters concerning characteristics of the plant and the pedological properties of the local soil (permeability, pH, humidity, porosity, etc.), including the meteorological factors. In the Chlef area, the water availability is dependent on meteorological data. The objective of this work is to estimate irrigation water needs in crop gardening (potato) based on a smart irrigation system (SIS). Thus, to ensure an equilibrated growth of crops, we have developed a system with parameters, such as soil moisture and soil temperature, which are the input variables of this smart irrigation system. This system was applied for the irrigation of potatoes (‘Bartina’ variety), planted in the agricultural experimental station of Lard El Beida at Chlef. The results obtained in terms of production yield led to a conclusion that the smart irrigation system allows achieving production of 124.83% with lower water consumption (–19.31%), compared to that of a drip irrigation system. Moreover, the granulometric analysis of the potato tuber size showed that 80.83% of the production is within the size range between 30 mm and 55 mm. By comparison, we observed that 77.4% of products obtained from drip irrigation follow a uniform distribution. We conclude that this smart irrigation system is very economical in terms of water use for gardening crops. Given these encouraging results, it would be wiser to generalize its application and implement it to guarantee food self-sufficiency in the water-deficient regions.

The growing demand for fresh water and its scarcity are the major problems encountered in semi-arid cities. Two different techniques have been used to assess the main determinants of domestic water in the Sedrata City, North-East Algeria: prin-cipal component analysis (PCA) and artificial neural networks (ANNs). To create the ANNs models based on the PCA, twelve explanatory variables are initially investigated, of which nine are socio-economic parameters and three physical char-acteristics of building units. Two optimum ANNs models have been selected where correlation coefficients equal to 0.99 in training, testing and validation phases. In addition, results demonstrate that the combination of socio-economic parameters with physical characteristics of building units enhances the assessment of household water consumption.