A GIS-based Novel Approach for Sustainable Sanitary Landfill Site Selection Using Integrated Fuzzy AHP and Machine Learning Algorithms

Disposal of collected waste is the least preferable way of sustainable solid waste management. But most of the cities in developing nations prefer to use open dumping in an inappropriate and non-scientific way, causing negative impacts on the environment as well as human health. This study offers a novel approach for scientific landfill site selection and sustainable waste management in Aligarh city, India. This could be possible through relevant data collection, selection of suitable models for criterion weighting, and model validation. In order to prepare a suitable landfill site selection map, a GIS-based ensemble FAHP-SVM and FAHP-RF model was implemented. Considering the previous studies and the characteristics and the study area, a total of eighteen thematic layers (decision criteria) were selected. The result reveals that land value, nearness to residential roads, nearness to hospitals and clinics, distance from waste bins, and NDBI having a fuzzy weight of > 0.10, indicates significant factors; whereas land elevation, land slope, surface temperature, soil moisture index, NDVI and urban classification having a fuzzy weight of 0, indicates these criteria have no importance for the present study. The result further reveals that FAHP-RF with an AUC value of 0.9182 is the more accurate model in comparison to FAHP-SVM. According to the final result of weight-based overlay, a total of seven potential landfill sites were identified, out of which three sites were determined as most suitable by considering current land cover, environmental and economic concerns, and public opinions. This study proposed a zonal division model based on the location of suitable landfill sites for sustainable waste management in the study area. The findings of this study may provide a guideline to the decision-makers and planners for optimal landfill site selection in other cities of developing countries.

Design on adaptive weighted guidance law for underwater intelligent vehicle tracking target

In future naval warfare, the accurate attack of underwater intelligent vehicles on targets will become more and more important. Therefore, it is very important to study the guiding method which has the key influence on tracking targets. In the guidance process of intelligent navigation system, the estimation of guidance information by the agent is influenced by confrontation and interference, which often results in the decrease of the guidance accuracy of UUV under the single guidance law. In this paper, the estimated information is classified according to the different types of guidance target information combining to the advantages of three guidance methods, namely fixed advance angle, extended proportional guidance law and adaptive sliding mode guidance law. Then the fuzzy weight coefficient calculation method is used to get the weight coefficients of all kinds of information to form the adaptive weighted guidance law. The target tracking effects of each guidance method is simulated and compared in the simulation environment of the target maneuver and the target non-maneuver respectively.

Path Selection using Fuzzy Weight Aggregated Sum Product Assessment

The search for safe evacuation routes is an important issue to save flood victims so they can reach the evacuation centre. This research is a simulation of searching for safe and fast travel evacuation route that have 24 alternative routes. Every road that will be transverse has a limit with certain criteria. Calculate of the weight of the constraints using the Multi-Criteria Decision Making (MCDM) method, namely the Analytical Hierarchy Process (AHP) andWeight Aggregated Sum Product Assessment (WASPAS) based on Fuzzy logic. The criteria of obstacle that qualitative for obscurity so that it makes sense fuzzy will provide supportive input for the MCDM problem. The Fuzzy AHP method is applied to calculate the weight of an application while the Fuzzy WASPAS (WASPAS-F)method is used to determine the safest alternative route. By using the Fuzzy AHP and WASPAS-F methods, a safe and fast pathway weights 0.662

Quality prediction and control of cable harness wiring using extension theory and a backpropagation neural network

Given that the cable harness wiring quality (CHWQ) of complex mechatronic products is affected by multiple factors and it is difficult to solve the resulting control problems, this paper proposes a quality prediction method for cable harness wiring using extension theory and a backpropagation (BP) neural network. First, a quality prediction framework is designed based on the factors influencing the composition and analysis of the CHWQ. Second, we establish a quality evaluation index system based on five aspects and design a first-level factor set and a second-level factor set. Based on the single factor index for various parameters and evaluation of data after dimensionless parameter processing, we use extension theory and the entropy weight method to determine the membership matrix and the fuzzy weight vector for the first-level evaluation. Additionally, the single factor and the synthetic fuzzy weight vector are determined using the entropy weight method and the analytic hierarchy process (AHP), respectively, and quality grade evaluation based on the fuzzy synthetic evaluation (FSE) method is completed. Finally, we use a three-layer feedforward neural network to predict the quality status of a specific phased array radar system. The results demonstrate that the proposed method can produce increased prediction accuracy.

Two Approximation Models of Fuzzy Weight Vector from a Comparison Matrix

In this study, our uncertain judgment on multiple items is denoted as a fuzzy weight vector. Its membership function is estimated from more than one interval weight vector. The interval weight vector is obtained from a crisp/interval comparison matrix by Interval Analytic Hierarchy Process (AHP). We redefine it as a closure of the crisp weight vectors which approximate the comparison matrix. The intuitively given comparison matrix is often imperfect so that there could be various approaches to approximate it. We propose two of them: upper and lower approximation models. The former is based on weight possibility and the weight vector with it includes the comparison matrix. The latter is based on comparison possibility and the comparison matrix with it includes the weight vector.

Decision-Maker’s Risk Preference Based Intuitionistic Fuzzy Multiattribute Decision-Making and Its Application in Robot Enterprises Investment

At present, the utilization of hesitation information of intuitionistic fuzzy numbers is insufficient in many methods which were proposed to solve the intuitionistic fuzzy multiple attribute decision-making problems. And also there exist some flaws in the intuitionistic fuzzy weight vector constructions in many research papers. In order to solve these insufficiencies, this paper defined three construction equations of weight vectors based on the risk preferences of decision-makers. Then we developed an intuitionistic fuzzy dependent hybrid weighted operator (IFDHW) and proposed an intuitionistic fuzzy multiattribute decision-making method. Finally, the effectiveness of this method is verified by a robot manufacturing investment example.