No abstract

Multi-Criteria Analysis (MCA) has found many applications in both technical and research sectors. MCA is a way to break the problem into more practicable elements, to permit data and decisions to be judgements to support the elements, and make the right decision. The aim of this paper is to analyze, compare, and make decisions of various current, and future scenarios of different quantifiable indicators for different considerations and various socio-economic aspects. Furthermore, this analysis is used to improve or at least to preserve the environment and natural resources in the basin. In this study an application by a real data set is made, these data are evaluated and extracted from classified satellite images of Loukkos basin, the classification of this satellite images regroups several classes of the data set such as agglomeration, dams, watercourses, croplands, bare soils and forests … etc. In reality, these data come from different sources like watershed information system (drinking water supply, irrigation system), transportation infrastructures (roads, dams), natural resources (Water, soils, and vegetation), human activities (agriculture, urbanization, and industry) and different socio-economic factors (demography). The main objective of this work is sorting the environmental indicators using the ELECTRE TRI tool, where ten alternatives are considered. We focus the classification of the real data set into the altitude, and the combined surface area factors. The obtained results prove that the classification is stable and the multi-criteria approach ELECTRE-TRI is suitable to a better sorting of the environmental indicators for the Loukkos Basin located in Morocco.

Moroccan economy is largely based upon rainfall, use of water resources and crop productivity, for that it’s considered as an agricultural country. It’s more required and more important for any farmer to forecast rainfall prediction in order to analyze crop productivity. Predicting the atmosphere or forecasting the state of the weather is considered as challenge for scientific research. The prediction of rainfall monthly or/and seasonal time scales is the application of science and technology to invent and to schedule the agriculture strategies. Recently different research articles achieve to forecast and/or predict rainfall monthly or seasonal time scales using different techniques. The methodology followed in this work, be focused on automating time series analysis to forecast / predict precipitation daily, monthly or seasonal in Aguelmam Sidi Ali basin in Morocco for last 32 years ago from 1975 to 2007. We first have to study the rainfall data theoretically using the simplest form statistical analysis, which is the univariate analysis, as long as only one variable is involved in our case study. To get the selected and suitable model of time series to automate, we used different autocorrelation methods based on various criterion such as: Akaike Information Criterion (AIC), estimation of parameters using Yule-Walker (YW) and Maximum Likelihood Estimation (MLE). The results of our experiment show that it is possible using our system to obtain accurate rainfall prediction, with a more details and with a very fast way. It shows also that it’s possible to predict for next months or next years. To minimize the risk of floods and natural disasters within a basin in general and within the Aguelmam Sidi Ali basin in particular, accurate and timely rainfall forecasting is required.

The primary objective of this current paper is to design, develop and automate an approach called the Hypsometrical Approach (HA). This approach automated and developed as a decision support system using environmental indicators for managing and planning water resources. It servers to analyze and to make comparison of various current and future scenarios of different quantifiable indicators for any consideration and for various socio-economic aspects. It is also used as a decision tool to improve or at least to preserve environment and natural resources. HA needs to draw its data from different sources like satellite images and watershed information system such as watershed characteristics including equipment infrastructures (Drinking water supply, irrigation system), transportation infrastructures (Roads, dams), natural resources (Water, soils, and vegetation), human activities (Agriculture, urbanization and industry) and different socio-economic factors (Demography). Globally in this paper the automation of this Hypsometrical Approach is divided into two main parts, the first part based on identifying and extracting data pixel by pixel from classified satellite images using python programming language, and the second part related to the development of a system allowing users to generate and visualize different curves called hypsometrical curves developed using Java programming language. We can combine any hypsometrical curve with arithmetic operations (addition, multiplication, subtraction and division) in order to assess some other indicators such as water resources, watershed storage capacities, vegetation, soils and forest potentials curves. Briefly, the objective of automating hypsometrical approach is to make efficient decision to improve the socio-economic level and enhance sustainable development.