One of the fundamental challenges in municipal waste management is ensuring the long-term stability of landfills. Slope instability in these structures can lead to irreparable consequences, including environmental pollution, infrastructure destruction, and public health threats. Therefore, accurate assessment and prediction of slope behavior in these structures is of particular importance. In this study, a new method based on fuzzy logic has been proposed to assess and predict slope stability in landfills. Fuzzy logic, as a powerful tool in modeling complex and uncertain systems, allows for more accurate analysis of landfill behavior. In order to conduct the present study, various steps were taken, including data collection, modeling with fuzzy logic, determining model coefficients, and model validation. In the first step, field data were collected from various excavations such as slope geometry, soil mechanical properties, groundwater level, loading due to waste, etc. Then, a fuzzy logic model was developed to analyze slope stability. In this model, input parameters such as slope, elevation, soil type, and groundwater level were described as linguistic variables and converted into fuzzy numbers using membership functions, and finally, fuzzy rules were defined to express the relationships between inputs and output (slope stability or instability). In the next step, field and laboratory data were used to determine the coefficients of the fuzzy model, and using optimization methods, the model parameters were adjusted in such a way that the model output was consistent with the observed data. In the last step, the developed model was validated using new data. For this purpose, the model results were compared with the observed data and the accuracy and reliability of the model were evaluated. The results show that the developed model will be able to predict the probability of slope instability with high accuracy and identify areas with high risk of instability in landfills. Also, it was determined that by analyzing the sensitivity of the model, important factors that affect slope stability can be identified. According to the model results, appropriate solutions can be provided to improve slope stability, such as changing the slope angle, soil reinforcement, and drainage. As a general conclusion, based on the observations of the present study, it can be said that using fuzzy logic in analyzing the slope stability of landfills is a new and efficient approach that can significantly contribute to improving urban waste management and reducing risks from slope instability.

This study investigated the mechanical properties of rammed earth (RE) stabilized with cement or lime and reinforced with straw. Specifically, the compressive and tensile strengths of 15 different mix designs were analyzed, including unstabilized RE, RE stabilized with lime or cement (at 4 % and 8 % by weight of soil), and RE reinforced with straw (at 0.5 % and 1.0 % by weight of soil), along with various combinations of stabilized and unstabilized RE reinforced with straw. Mechanical properties were further assessed through ultrasonic testing and scanning electron microscopy (SEM). Additionally, a data-driven fuzzy logic model was developed to estimate the mechanical properties of RE, addressing a key gap in the application of fuzzy logic to RE construction. The results showed that stabilizing RE with cement and lime increased its 28-day dry compressive strength by 365% to 640% and 109% to 237%, respectively. The addition of straw generally reduced compressive strength. The stress-strain curves indicated that the elastic modulus of RE stabilized with cement and lime increased by up to 350% and 11 %, respectively. The 28-day dry tensile strength of the samples ranged from 0.17 to 0.56 MPa. Furthermore, the addition of stabilizers improved tensile strength by approximately 88 % to 224 %, while straw enhanced the tensile strength of unstabilized RE by about 35 %. Ultrasonic and SEM analyses provided valuable insights into the mechanical properties of RE. Additionally, the fuzzy logic model proved useful, yielding satisfactory results in predicting the properties of RE, particularly when using the centroid defuzzification method. The study concluded that RE materials when properly cured and effectively stabilized with cement, lime, and straw, can achieve acceptable mechanical properties and offer sustainable benefits.

Landslides are one of the most significant natural geological hazards, capable of causing extensive damage to lives, infrastructure, and property. These events are often triggered by specific geological and environmental conditions that can be monitored utilizing advanced technologies such as Wireless Sensor Networks (WSNs). This study introduces a novel itinerary planning approach for WSNs, employing the Fuzzy Logic-based Particle Swarm Optimization (FLPSO) technique, which integrates Fuzzy Logic and Particle Swarm Optimization methodologies. The primary objective of this approach is to minimize the energy consumption in large-scale WSNs, thereby enhancing their efficiency for landslide detection systems. The proposed method improves on traditional network grouping methods by optimizing energy usage across sensor nodes. A case study was conducted in Shiradi village, Mangalore, India, an area characterized by high annual rainfall and changing climatic patterns. Over a year, data was collected and analyzed to evaluate the system's potential for accurate landslide hazard predictions. The soil suction stress was calculated using laboratory tests, incorporating various geotechnical and unsaturated soil parameters specific to the study area. The experimental results demonstrated that energy-efficient nodes not only have a longer operational lifespan and greater adaptability to environmental changes, but also exhibit superior performance compared to current methods, with improvements of 14.15% in Packet Delivery Ratio (PDR), 11.15% in Energy Delay Product (EDP), 10.15% in Packet Loss Ratio (PLR), 22.1% in task delay, and 20.1% in throughput.

Natural hazard processes, as an inherent component of mountain environments, react sensitively to global warming. The main drivers of these changes are alterations in the amount, intensity or type of precipitation, glacier melting, or thawing of permafrost ice. The hazard responses can involve a change in hazard intensity or frequency (increasing or decreasing), a shift in their location or, a shift from one type of hazard to another. As climate change impacts vary in space and time, this variability must be considered when planning measures to protect populations and infrastructure from hazardous processes. To support this, we developed a method for assessing the climate sensitivity of small individual rock releases and larger rockfall processes. The method is based on a fuzzy logic approach and uses highly resolved climate scenario data, allowing application on a regional or even larger scale. The application in a study area of 700 km2 in the central Valais (Switzerland) shows that the impacts of climate change on natural hazard processes can vary quite substantially across small spatial scales. Generally, an increase in rockfall frequency and magnitude is simulated under future warming scenarios, especially at higher altitudes. However, at lower elevations and on south-exposed slopes, a decrease in freeze-thaw cycles leads to a decrease in material availability. This knowledge is essential in discussions of how climate change should be considered in hazard and disaster management.

Uncontrolled wildfires pose a significant threat, potentially causing extensive damage to biodiversity, soil quality and human resources. It's crucial to swiftly detect and predict these wildfires to minimize their catastrophic consequences. To address this, our research introduces a wildfire prediction model that ranks cities based on risk leveraging multi-criteria decision-making (MCDM) to systematically assess conflicting factors in decision-making. This model integrates wildfire risks into a city's resilience strategy, utilizing fuzzy set theory to manage imprecise data and uncertainties. As part of this approach, we compile a new dataset encompassing weather patterns, vegetation types, terrain features and population density across various Californian cities. Ultimately, the model assesses and ranks the wildfire risk for each city in California.

The estimation of the flow coefficient is a vital hydrological procedure that holds considerable importance in flood prediction, water resource management, and flood mitigation. The precise estimation of the flow coefficient is imperative in mitigating flood-related damages, administering flood alert mechanisms, and regulating water discharge. It is hard to accurately determine the flow coefficient without a good understanding of the river basin's hydrology, climate, topography, and soil characteristics. A range of methodologies have been documented in the most recent body of literature for flow coefficient modeling. The majority of these methods, however, depend on opaque techniques that lack generalizability. Therefore, this research employed three distinct methodologies-specifically, the Adaptive Neural Fuzzy Inference System (ANFIS), the Simple Membership Function, and the Fuzzy Rules Generation Technique (SMRGT) are all examples of fuzzy inference systems, and Artificial Neural Network (ANN), to achieve its objectives. The Aksu River Basin in Antalya, Turkey, was chosen as the study area. The models underwent multiple permutations of precipitation (P), temperature (T), relative humidity (Rh), wind speed (Ws), land use (LU), and soil properties (Sp) data that were tailored to the particular study region. The study analyzed the results using various performance metrics of the model such as mean absolute error (MAE), Nash-Sutcliffe efficiency coefficient (NSE), root mean square error (RMSE), and correlation coefficient (R2). The results indicate that the SMRGT method resulted in a remarkable degree of accuracy in forecasting the flow coefficient, as demonstrated with the minimal RMSE and MAE values and high correlation coefficient values. The study's findings suggest that the SMRGT method was applied effectively in hydrological analysis to estimate the flow coefficient, contributing to more accurate flood prediction, water resource management, and flood mitigation strategies.

Assessing the spatial distribution of the erosion process is considered a critical initial step to provide valuable insights to decision-makers for devising an effective erosion mitigation strategy to reduce erosion damages. This research was conducted based on a revised universal soil loss equation (RUSLE) model integrated with the geographic information environment (GIS) within the Wadi El Ghareg watershed located in the Menzel Bourguiba region in northeastern Tunisia to simulate the spatial distribution of erosion across the basin which has been experiencing adverse effects of climate change, characterized by periods of drought and heavy rainfall. The RUSLE incorporates several variables, including rainfall erosivity (R), soil erodibility (K), cover management (C), slope length (LS), and conservation practices (P), serving as key predisposition parameters in this research. For the validation process of the applied model, 200 points were selected to create an inventory map; the points were selected based on satellite images and field surveys. The obtained thematic maps were normalized by fuzzy logic and overlaid using the model equation in the GIS. The results identified the most severely eroded areas requiring immediate erosion control measures. Hence, the results reveal that about 1.71% of the area is covered under severe erosion risk, 0.13% area under high erosion risk, 0.26% area under moderate erosion risk, 0.27% area under low erosion risk, and 97.63% of the area under very low erosion risk. The accuracy of the model was evaluated based on the receiver operating characteristic curves (ROC) and the areas under the curves (AUC). The result showed that this model had an excellent predictive accuracy for soil erosion susceptibility, with AUC values of 0.967. The final produced map will be used as a basis for suggesting a framework that can help make practical policy recommendations to fight against erosion in the context of sustainable management of the watershed.

This study aimed to outline areas at risk of the occurrence of eucalypt defoliator caterpillars and their relationship with variations in the spectral behaviour of canopies and soil attributes. The study was conducted in three eucalypt plantation areas of the Bracell Bahia company, located in the state of Bahia, Brazil. Initially, the spatiotemporal distribution of climatic variables and water balance was evaluated. Subsequently, using geographic information systems and fuzzy logic, occurrence risk zoning was developed for defoliator caterpillars associated with different classes of eucalypt crop aptitude. After defining the areas at risk of pest occurrence, specific plots in different aptitude classes were selected to assess, intra-plot, characteristics that can increase or reduce the risk of defoliator caterpillar attack and damage intensity, as well as the plant's ability to recover after a controlled outbreak. Information on the spectral behaviour of the canopies and the availability of nutrients in the soil was used in this step. Zone modelling enabled the delineation of areas and periods at a higher risk of pest occurrence for the different aptitude conditions of the eucalypt crop. The intra-plot methodology proved that the class of eucalypt crop aptitude with the greatest potential for recovering vegetative vigour 60 days after caterpillar damage was the apt class. This result can be attributed to K limitation and the high spatial resolution of the PlanetScope orbital sensor.