Use of Statistical Tools and Artificial Intelligence for Hydrosedimentological Analysis in Experimental Units in Semiarid Brazil

Abstract

Objective: To compare statistical and artificial intelligence tools to assist in the analysis and influence of hydrosedimentological variables on runoff generation and sediment yield in experimental units in the Brazilian semiarid region. Theoretical framework: The increasing frequency of extreme weather events in areas prone to natural disasters has led to economic, social, and loss of life due to the lack of urban planning, disorderly occupation in densely populated areas, intensive use of natural resources, and difficulty in maintaining and conserving natural ecosystems (CARMO & LIMA, 2020). Continuous monitoring of hydrosedimentological processes is necessary for natural resource planning and management. There is a need for expanding long-term environmental monitoring in Brazil, which is not simple and costly. In hard-to-reach areas, investment in tools and variable analysis for secure estimation of flow and erosion is necessary (AIRES, 2022). Method: Data obtained from the São João do Cariri Experimental Basin - SJCEB (13.6 km²) were used through conventional rain gauges and automatic data collection platforms from three micro-watersheds (0.16 – 1.63 ha) and two experimental plots (100 m²) located in the semiarid region of Paraíba, Northeast Brazil. For the analyses, 147 rain events that generated surface runoff in at least one of the five experimental units of the SJCEB were used, with data on area (m²), vegetation cover (0 no cover and 1 with vegetation cover over the entire area), average slope (%), daily precipitation (mm), period without rainfall (days), runoff depth (mm), and sediment yield (kg.ha⁻¹), from 2001 to 2008. The statistical and artificial intelligence tools used in the analyses were: simple linear regression, Pearson correlation, and Self-Organizing Maps (SOM). The models were implemented in the R programming language. Results and conclusion: In the statistical summary of the studied variables, precipitation ranged from 2.2 mm to 133.0 mm, with a mean of 21.1 mm. The period without rainfall ranged from 0 to 240 days, with an average of 16.2 days. Slope varied from 3.40% to 7.50%, with a mean of 5.7%. The average vegetation cover was 0.44, with a median of 0.60. The Pearson correlation analysis revealed that precipitation showed a positive and significant correlation with surface runoff and sediment yield variables, ranging from moderate to strong. Simple linear regression was unable to provide satisfactory answers to assist in the analysis of runoff and sediment yield, therefore considered inefficient for hydrosedimentological regionalization. The unsupervised SOM network, by presenting complex relationships among the studied variables, detected that sediment yield is particularly influenced by the interactions with precipitation, vegetation cover, and slope. Thus, it can be inferred that models based on unsupervised neural networks are attractive alternatives, as they were able to extract complex and nonlinear relationships among the analyzed variables. When there are complex cause-and-effect relationships between variables of hydrosedimentological processes, the use of nonlinear and multivariate models, such as artificial neural networks. Implications of the research: Manipulations in experimental areas have favored the measurement of flow and quantification of sediment yield. However, the high costs to maintain physical structures and the presence of specialized personnel for monitoring, processing, and dissemination of collected data are factors that may make it impractical to maintain and expand environmental monitoring networks in Brazil. In ungauged areas, the prediction of flow and erosion is necessary, and can be performed through mathematical, statistical, or artificial intelligence tools that facilitate the identification of priority areas for intervention. Manipulations in experimental areas have favored the measurement of runoff and sediment yield, but the high costs of maintaining the physical structures and the presence of specialized personnel to monitor, process and disseminate the collected data are factors that make maintenance and the expansion environmental monitoring networks in Brazil. In non-instrumented areas, forecasting and analysis of runoff and erosion becomes necessary, using mathematical, statistical or artificial intelligence tools that favor the identification of priority areas for intervention. Originality/value: Use of different statistical and artificial intelligence tools for hydrosedimentological regionalization in small experimental units in the Brazilian semiarid region, with emphasis on the application of unsupervised learning neural networks (SOM).