scholarly journals Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 335 ◽  
Author(s):  
Alireza Arabameri ◽  
Thomas Blaschke ◽  
Biswajeet Pradhan ◽  
Hamid Reza Pourghasemi ◽  
John P. Tiefenbacher ◽  
...  
Keyword(s):  
Low Income ◽  
Multiple Criteria Decision Making ◽  
Predictive Performance ◽  
Gully Erosion ◽  
Susceptibility Mapping ◽  
Rate Curve ◽  
Conditioning Factors ◽  
Soft Computing Techniques ◽  
Order Preference ◽  
Topsis Technique

Gully erosion is a problem; therefore, it must be predicted using highly accurate predictive models to avoid losses caused by gully development and to guarantee sustainable development. This research investigates the predictive performance of seven multiple-criteria decision-making (MCDM), statistical, and machine learning (ML)-based models and their ensembles for gully erosion susceptibility mapping (GESM). A case study of the Dasjard River watershed, Iran uses a database of 306 gully head cuts and 15 conditioning factors. The database was divided 70:30 to train and verify the models. Their performance was assessed with the area under prediction rate curve (AUPRC), the area under success rate curve (AUSRC), accuracy, and kappa. Results show that slope is key to gully formation. The maximum entropy (ME) ML model has the best performance (AUSRC = 0.947, AUPRC = 0.948, accuracy = 0.849 and kappa = 0.699). The second best is the random forest (RF) model (AUSRC = 0.965, AUPRC = 0.932, accuracy = 0.812 and kappa = 0.624). By contrast, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model was the least effective (AUSRC = 0.871, AUPRC = 0.867, accuracy = 0.758 and kappa = 0.516). RF increased the performance of statistical index (SI) and frequency ratio (FR) statistical models. Furthermore, the combination of a generalized linear model (GLM), and functional data analysis (FDA) improved their performances. The results demonstrate that a combination of geographic information systems (GIS) with remote sensing (RS)-based ML models can successfully map gully erosion susceptibility, particularly in low-income and developing regions. This method can aid the analyses and decisions of natural resources managers and local planners to reduce damages by focusing attention and resources on areas prone to the worst and most damaging gully erosion.

scholarly journals A Comparative Study on Decision Making Methods with Interval Data

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Aditya Chauhan ◽  
Rahul Vaish
Keyword(s):  
Decision Making ◽  
Input Data ◽  
Multiple Criteria Decision Making ◽  
Interval Data ◽  
Cross Entropy ◽  
Ideal Solution ◽  
Order Preference ◽  
Topsis Technique ◽  
Selection Of ◽  
Novel Algorithm

Multiple Criteria Decision Making (MCDM) models are used to solve a number of decision making problems universally. Most of these methods require the use of integers as input data. However, there are problems which have indeterminate values or data intervals which need to be analysed. In order to solve problems with interval data, many methods have been reported. Through this study an attempt has been made to compare and analyse the popular decision making tools for interval data problems. Namely, I-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), DI-TOPSIS, cross entropy, and interval VIKOR (VlseKriterijumska Optimiza-cija I Kompromisno Resenje) have been compared and a novel algorithm has been proposed. The new algorithm makes use of basic TOPSIS technique to overcome the limitations of known methods. To compare the effectiveness of the various methods, an example problem has been used where selection of best material family for the capacitor application has to be made. It was observed that the proposed algorithm is able to overcome the known limitations of the previous techniques. Thus, it can be easily and efficiently applied to various decision making problems with interval data.

scholarly journals A Multiple Criteria Decision Making Method Based on Relative Value Distances

2015 ◽  
Vol 40 (4) ◽  
pp. 299-315 ◽  
Author(s):  
Huan-jyh Shyur ◽  
Liang Yin ◽  
Hsu-shih Shih ◽  
Chi-bin Cheng
Keyword(s):  
Decision Making ◽  
Solution Method ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Decision Makers ◽  
Relative Value ◽  
Seeking Behavior ◽  
Order Preference ◽  
Topsis Technique ◽  
The Ideal

Abstract This paper proposes a new multiple criteria decision-making method called ERVD (election based on relative value distances). The s-shape value function is adopted to replace the expected utility function to describe the risk-averse and risk-seeking behavior of decision makers. Comparisons and experiments contrasting with the TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) method are carried out to verify the feasibility of using the proposed method to represent the decision makers’ preference in the decision making process. Our experimental results show that the proposed approach is an appropriate and effective MCDM method.

scholarly journals ANALYSIS OF THE PRE-INJECTION CONFIGURATION IN A MARINE ENGINE THROUGH SEVERAL MCDM TECHNIQUES

Brodogradnja ◽  
2021 ◽  
Vol 72 (4) ◽  
pp. 1-17
Author(s):  
Carlos Gervasio Rodríguez ◽  
◽  
María Isabel Lamas ◽  
Juan de Dios Rodríguez ◽  
Claudio Caccia ◽  
...  
Keyword(s):  
Multiple Criteria Decision Making ◽  
Solution Technique ◽  
Weighted Sum Method ◽  
Injection Duration ◽  
Injection Quantity ◽  
Performance And Emissions ◽  
Marine Diesel ◽  
Order Preference ◽  
Topsis Technique ◽  
Sum Vector

The present manuscript describes a computational model employed to characterize the performance and emissions of a commercial marine diesel engine. This model analyzes several pre-injection parameters, such as starting instant, quantity, and duration. The goal is to reduce nitrogen oxides (NOx), as well as its effect on emissions and consumption. Since some of the parameters considered have opposite effects on the results, the present work proposes a MCDM (Multiple-Criteria Decision Making) methodology to determine the most adequate pre-injection configuration. An important issue in MCDM models is the data normalization process. This operation is necessary to convert the available data into a non-dimensional common scale, thus allowing ranking and rating alternatives. It is important to select a suitable normalization technique, and several methods exist in the literature. This work considers five well-known normalization procedures: linear max, linear max-min, linear sum, vector, and logarithmic normalization. As to the solution technique, the study considers three MCDM models: WSM (Weighted Sum Method), WPM (Weighted Product Method) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). The linear max, linear sum, vector, and logarithmic normalization procedures brought the same result: -22º CA ATDC pre-injection starting instant, 25% pre-injection quantity and 1-2º CA pre-injection duration. Nevertheless, the linear max min normalization procedure provided a result, which is different from the others and not recommended.

scholarly journals Multi-Criteria Decision Making Using TOPSIS Method Under Fuzzy Environment. Application in Spillway Selection

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 637 ◽  
Author(s):  
Vasiliki Balioti ◽  
Christos Tzimopoulos ◽  
Christos Evangelides
Keyword(s):  
Decision Making ◽  
Multiple Criteria Decision Making ◽  
Fuzzy Topsis ◽  
Topsis Method ◽  
Northern Greece ◽  
Analytic Hierarchy ◽  
Order Preference ◽  
Optimum Type ◽  
Topsis Technique ◽  
Selection Of

The selection of an appropriate spillway has a significant effect to the construction of a dam and several procedures and considerations are needed. In the past, this selection of the type of the spillway was arbitrary and sometimes with bad results. Recently the Multiple Criteria Decision Making theory has given the possibility to make a decision about the optimum form of a spillway under complex circumstances. In this paper, the above method is used and especially the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method for the selection of a spillway for a dam in the district of Kilkis in Northern Greece—‘Dam Pigi’. As the criteria were fuzzy and uncertain, the Fuzzy TOPSIS method is introduced together with the AHP (Analytic Hierarchy Process), which is used for the evaluation of criteria and weights. Five types of spillways were selected as alternatives and nine criteria. The criteria are expressed as triangular fuzzy numbers in order to formulate the problem. Finally, using the Fuzzy TOPSIS method, the alternatives were ranked and the optimum type of spillway was obtained.

Imprecise DEA Model Based on TOPSIS

2011 ◽  
Vol 63-64 ◽  
pp. 723-727 ◽  
Author(s):  
Ji Heng Xu ◽  
Ling Li ◽  
Jian Yong Liu ◽  
Cheng Qun Fu ◽  
Ji Lin Zheng
Keyword(s):  
Ideal Point ◽  
Multiple Criteria Decision Making ◽  
Decision Makers ◽  
Dea Model ◽  
Model Based ◽  
Relative Closeness ◽  
Subjective Preference ◽  
Order Preference ◽  
Set Up ◽  
Topsis Technique

In view of the defect that TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) can not deal with imprecise data properly in multiple criteria decision making. And the weights are often hard to reflect the fact because of subjective preference of decision makers. We proposed a hybrid model combines TOPSIS and imprecise DEA model to improve the disadvantages. Ideal DMU and anti-ideal DMU were built, and the corresponding positive ideal point and negative ideal point were established. Imprecise DEA was set up and an upper-lower limit method is utilized to formulate the imprecise efficiency scores of the two hypothetical DMUs. Based on the distances between DMU0 and the two hypothetical DMUs respectively, imprecise relative closeness was formulated to rank the superiority of all DMUs. The imprecise DEA model based on TOPSIS can avoid too subjective weights and make the evaluation more rational. A numerical example indicated the efficiency of the hybrid measure.

scholarly journals GIS-Based Machine Learning Algorithms for Gully Erosion Susceptibility Mapping in a Semi-Arid Region of Iran

2020 ◽  
Vol 12 (15) ◽  
pp. 2478 ◽  
Author(s):  
Xinxiang Lei ◽  
Wei Chen ◽  
Mohammadtaghi Avand ◽  
Saeid Janizadeh ◽  
Narges Kariminejad ◽  
...  
Keyword(s):  
State Of The Art ◽  
Gully Erosion ◽  
Susceptibility Mapping ◽  
Machine Learning Algorithms ◽  
Training Dataset ◽  
Validation Process ◽  
Conditioning Factors ◽  
Susceptibility Model ◽  
Semi Arid Region ◽  
Better Than

In the present study, gully erosion susceptibility was evaluated for the area of the Robat Turk Watershed in Iran. The assessment of gully erosion susceptibility was performed using four state-of-the-art data mining techniques: random forest (RF), credal decision trees (CDTree), kernel logistic regression (KLR), and best-first decision tree (BFTree). To the best of our knowledge, the KLR and CDTree algorithms have been rarely applied to gully erosion modeling. In the first step, from the 242 gully erosion locations that were identified, 70% (170 gullies) were selected as the training dataset, and the other 30% (72 gullies) were considered for the result validation process. In the next step, twelve gully erosion conditioning factors, including topographic, geomorphological, environmental, and hydrologic factors, were selected to estimate gully erosion susceptibility. The area under the ROC curve (AUC) was used to estimate the performance of the models. The results revealed that the RF model had the best performance (AUC = 0.893), followed by the KLR (AUC = 0.825), the CDTree (AUC = 0.808), and the BFTree (AUC = 0.789) models. Overall, the RF model performed significantly better than the others, which may support the application of this method to a transferable susceptibility model in other areas. Therefore, we suggest using the RF, KLR, and CDT models for gully erosion susceptibility mapping in other prone areas to assess their reproducibility.

MULTIPLE CRITERIA CONSTRUCTION MANAGEMENT DECISIONS CONSIDERING RELATIONS BETWEEN CRITERIA / DAUGIATIKSLIAI STATYBOS VALDYMO SPRENDIMAI ATSIŽVELGIANT Į RODIKLIŲ TARPUSAVIO PRIKLAUSOMYBĘ

2010 ◽  
Vol 16 (1) ◽  
pp. 109-125 ◽  
Author(s):  
Jurgita Antuchevičienė ◽  
Edmundas Kazimieras Zavadskas ◽  
Algimantas Zakarevičius
Keyword(s):  
Decision Making ◽  
Mahalanobis Distance ◽  
Construction Management ◽  
Distance Measure ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Ideal Solution ◽  
Order Preference ◽  
Topsis Technique

Decision making in construction management has been always complicated especially if there were more than one criterion under consideration. Multiple criteria decision making (MCDM) has been often applied for complex decisions in construction when a lot of criteria were involved. Traditional MCDM methods, however, operate with independent and conflicting criteria. While in every day problems a decision maker often faces interactive and interrelated criteria. Accordingly, the need of improving and supplementing the methodology of compromise decisions arose. It was proposed to supplement TOPSIS (Technique for the Order Preference by Similarity to Ideal Solution) method and integrate the Mahalanobis distance in the usual algorythm of TOPSIS. Mahalanobis distance measure offered an option to take the correlations between the criteria into considerations while making the decision. A case study of building redevelopment in Lithuanian rural areas was presented that demonstrated the application of the proposed methodology. The case study proved that the proposed TOPSIS‐M (TOPSIS applying Mahalanobis distance measure) method could have substantial influence in carrying the proper decision. Santrauka Statybos valdymo spendimų priėmimas visuomet yra komplikuotas, ypač jei turime atsižvelgti į daugelį rodiklių. Kompleksiniams statybos sprendimams, kurie apibūdinami daugeliu rodiklių, taikomi daugiatiksliai sprendimų priėmimo metodai (MCDM ‐ Multiple Criteria Decision Making). Šie metodai skirti sprendimams priimti tuomet, kai vertinami konfliktuojantys bei nepriklausomi rodikliai. Tačiau realiose situacijose, priešingai, nuolat susiduriame su saveikaujančiais ir tarpusavio priklausomybę turinčiais rodikliais. Dėl šios priežasties kyla poreikis patobulinti sprendimų metodologiją. Straipsnyje siūloma papildyti variantų racionalumo nustatymo metoda TOPSIS (Technique for the Order Preference by Similarity to Ideal Solution), taikant Mahalanobio metoda atstumams nustatyti. Mahalanobio atstumų nustatymo metodas suteikia galimybę įvertinti koreliacinės rodiklių priklausomybės priimant daugiatikslį sprendimą. Siūlomos metodologijos taikymas įliustruojamas sprendžiant apleistų pastatų Lietuvos kaimo vietovėse racionalaus sutvarkymo uždavinį. Pateiktas pavyzdys patvirtina, kad TOPSIS‐M metodo (t. y. TOPSIS naudojant Mahalanobio atstuma) taikymas gali turėti esminę įtaka priimant sprendimą.

scholarly journals Integration of an InSAR and ANN for Sinkhole Susceptibility Mapping: A Case Study from Kirikkale-Delice (Turkey)

2021 ◽  
Vol 10 (3) ◽  
pp. 119
Author(s):  
Hakan A. Nefeslioglu ◽  
Beste Tavus ◽  
Melahat Er ◽  
Gamze Ertugrul ◽  
Aybuke Ozdemir ◽  
...  
Keyword(s):  
High Speed ◽  
Fundamental Problem ◽  
Vertical Displacement ◽  
Deformation Monitoring ◽  
Susceptibility Mapping ◽  
Susceptibility Map ◽  
Engineering Structures ◽  
Conditioning Factors ◽  
High Deformation ◽  
Settlement Site

Suitable route determination for linear engineering structures is a fundamental problem in engineering geology. Rapid evaluation of alternative routes is essential, and novel approaches are indispensable. This study aims to integrate various InSAR (Interferometric Synthetic Aperture Radar) techniques for sinkhole susceptibility mapping in the Kirikkale-Delice Region of Turkey, in which sinkhole formations have been observed in evaporitic units and a high-speed train railway route has been planned. Nine months (2019–2020) of ground deformations were determined using data from the European Space Agency’s (ESA) Sentinel-1A/1B satellites. A sinkhole inventory was prepared manually using satellite optical imagery and employed in an ANN (Artificial Neural Network) model with topographic conditioning factors derived from InSAR digital elevation models (DEMs) and morphological lineaments. The results indicate that high deformation areas on the vertical displacement map and sinkhole-prone areas on the sinkhole susceptibility map (SSM) almost coincide. InSAR techniques are useful for long-term deformation monitoring and can be successfully associated in sinkhole susceptibility mapping using an ANN. Continuous monitoring is recommended for existing sinkholes and highly susceptible areas, and SSMs should be updated with new results. Up-to-date SSMs are crucial for the route selection, planning, and construction of important transportation elements, as well as settlement site selection, in such regions.

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