Application of Artificial Neural Networks to Predict Solonchaks Index Derived from Fuzzy Logic: A Case Study in North Algeria

Soil salinization, particularly under irrigation in the arid regions of North Africa, represents a major constraint to sustainable agricultural development. This study investigates the Chott El Hodna region in Algeria, a Ramsar-classified wetland severely affected by salinization. Two representative soil profiles (P1 and P2) were initially characterized, revealing chemical properties dominated by calcium-chloride and calcium-sulfate types. Based on these findings, 26 additional profiles with moderate levels of gypsum, limestone, and soluble salts were analyzed. The limited number of profiles reflects the environmental homogeneity of the area, allowing the study site to be considered a pilot zone. Fuzzy logic was employed to classify soils, identify intergrade soils, and determine their degree of membership to Solonchaks within the Calcisol class, addressing the lack of precision in conventional classifications. Results indicate that 50% of soils are Solonchaks, 46.15% are Calcisols, and 3.85% are intergrades. Principal Component Analysis (PCA) revealed that soil solution chemistry is mainly governed by the dissolution of evaporite minerals (gypsum, halite, anhydrite) and the precipitation of carbonate phases (calcite, aragonite, dolomite). Statistical analyses using Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) demonstrated that ANN achieved superior predictive performance for the Solonchak index (Is), with R2 = 0.70 and RMSE = 0.17, compared with R2 = 0.41 for MLR. This study proposes a robust framework combining fuzzy logic and ANN to improve the classification of saline wetland soils, particularly by identifying intergrade soils, thus providing a more precise numerical classification than conventional approaches.