Background: Food security is considered a pivotal factor for the sustainable development of communities and focus on this issue in rural areas. More specifically, it is of paramount importance in developing countries. Accordingly, this descriptive-analytical study aimed to evaluate the status of food security in rural areas of Iran. The main originality of the present study is to assess the strategic future-oriented vision for food security in addition to the evaluation of the current status of the studied area. Methodology: Data were collected using the standard questionnaire of U.S. Department of Agriculture (USDA) and a researcher-made close-ended questionnaire. The validity and reliability of the research instrument were confirmed by a panel of specialists and Cronbach’s alpha coefficients, respectively. In addition, data analysis was performed using SPSS₂₄ (to analyze the descriptive statistics) and Fuzzy Technique for Order Preference by Similarity to Ideal Situation (FTOPSIS). Results: According to the results, 80% of the villagers suffered from food insecurity (25% in low food insecurity situation, 42% in moderate food insecurity situation, and 13% in severe food insecurity situation). Evaluation of the factors affecting food insecurity demonstrated that economic (standardized weight of 0.566), stability (standardized weight of 0.559), and availability (standardized weight of 0.558) were the most important components affecting food insecurity. Moreover, in a systemic approach, the growth of migration from rural to urban areas, pressure on water and soil resources, and the occurrence of environmental hazards are of the most significant consequences of food insecurity. In addition, due to the excessive use of underground water for cucurbits, which is the dominant cultivation pattern in this region, the groundwater level has dropped sharply in some villages farther from the city which can be alarming for exacerbating food insecurity in the near future. Conclusion: Since the low-income level of villagers, lack of job diversity, and lack of access to adequate food are the main reasons for food insecurity, in this respect, it is suggested that more attention be paid to the development of occupations and job diversity in these regions by decision-makers and policy-makers.

Regarding evaluating disaster risks in Iran’s West Kurdistan area, the multi-layer perceptron (MLP) neural network was upgraded with two novel techniques: backtracking search algorithm (BSA) and biogeography-based optimization (BBO). Utilizing 16 landslide conditioning elements such as elevation (aspect), plan (curve), profile (curvature), geology, NDVI (land use), slope (degree), stream power index (SPI), topographic wetness index (TWI), rainfall, and sediment transport index (STI), and 504 landslides as target variables, a large geographic database is constructed. Applying the techniques mentioned above to the synthesis of the MLP results in the suggested BBO-MLP and BSA-MLP ensembles. As accuracy standards, we benefit from mean absolute error, mean square error, and area under the receiving operating characteristic curve to assess the utilized models, we have also designed a scoring system. The MLP’s accuracy increases thanks to the application of the BBO and BSA algorithms. Comparing the BBO with the BSA, we find that the former achieves higher average MLP optimization ranks (20, 15, and 14). A further finding showed that the BBO is superior to the BSA at maximizing the MLP.