Application of smart technologies for predicting soil erosion patterns.

Area of Science:

• Environmental Science
• Soil Science
• Machine Learning Applications

Background:

• Accurate soil erosion susceptibility assessment is crucial for land management and conservation.
• Traditional methods for erosion assessment can be costly and time-consuming for large areas.
• Water-induced erosion significantly impacts agricultural lands, necessitating efficient evaluation techniques.

Purpose of the Study:

• To evaluate four data-driven optimization algorithms combined with artificial neural networks for soil erosion susceptibility assessment.
• To compare the performance of biogeography-based optimization (BBO), earthworm optimization algorithm (EWA), symbiotic organisms search (SOS), and whale optimization algorithm (WOA) when integrated with multilayer perceptron (MLP) models.
• To identify the most effective method for assessing erosion susceptibility in cultivated lands.

Main Methods:

• Utilized four optimization algorithms: BBO, EWA, SOS, and WOA.
• Integrated these algorithms with artificial neural network (ANN) models, specifically multilayer perceptron (MLP).
• Employed 14 geographic and environmental criteria for assessment, with data split into 70% for training and 30% for testing.

Main Results:

• All four tested methods (BBO-MLP, EWA-MLP, SOS-MLP, WOA-MLP) demonstrated high accuracy, with AUC values exceeding 0.92.
• The BBO-MLP model achieved the highest AUC values (0.999 for training, 0.9327 for testing).
• SOS-MLP also showed excellent performance with AUC values of 0.9973 (testing) and 0.9296 (training).

Conclusions:

• The integration of optimization algorithms with ANNs provides a powerful and efficient tool for erosion susceptibility mapping.
• The BBO-MLP model is highly effective for identifying areas prone to soil erosion.
• These data-driven approaches offer a valuable alternative to traditional methods for large-scale erosion assessment.