Fuzzy Logic Model to Assess Desertification Intensity Based on Vulnerability Indices

Sadeghiravesh, Mohammad Hassan; Khosravi, Hassan; Abolhasani, Azam; Ghodsi, Maryam; Mosavi, Amir

doi:10.12700/aph.18.3.2021.3.1

Cited by 13 publications

(6 citation statements)

References 8 publications

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“…Due to the complexity of land degradation, there are no universal guidelines for selecting the exact variables that influence this phenomenon. But, according to different studies done in this field, the most usual variables which influence LD are soil salinity (Jabbar & Chen, 2008; Sadeghiravesh et al, 2021), quantitative and qualitative changes in groundwater (Nasirian et al, 2019), reduction and change in vegetation cover (Masoudi et al, 2018; Kirui et al, 2021), rainfall (Zhang et al, 2020), slope inclination (Sadeghiravesh et al, 2021), altitude (Abdel‐Kader, 2019), and land‐use (Bestelmeyer et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

et al. 2022

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Although land degradation (LD) is known as a severe environmental problem, spatial predictive modelling of this phenomenon remains a challenge. This research aimed to develop a new conceptual framework to predict LD susceptibility based on net primary production (NPP) and machine learning approaches. The annual NPP over the period 2001–2020 were obtained using MOD17A3 and the trend of NPP changes was considered to investigate the occurrence sites of LD within Qazvin Plain, in Qazvin Province, Iran, under a semiarid climate, with an area of about 9500 km2. An inventory map of LD was generated based on the LD study sites. The locations were randomly split‐sampled as training (70%) and testing (30%) datasets to evaluate the efficiency of the built models. Fifteen geo‐environmental factors were considered as LD predictive variables such as altitude, slope, land use, and temperature. Four advanced machine‐learning techniques were performed to model LD susceptibility. Finally, the predictive efficiency of the models was measured utilizing the area under the (ROC) curve Area Under the ROC Curve(AUC) and true skill as statics (TSS). The results indicated that the randomForest (RF), with the AUC = 0.81 and TSS = 0.5, showed the highest efficiency for predicting LD in the Qazvin Plain followed by boosted regression tree (BRT) with AUC = 0.76 and TSS = 0.47, support vector machine (SVM) with AUC = 0.71 and TSS = 0.39, and classification and regression tree (CART) with AUC = 0.63 and TSS = 0.31. The findings illustrated that altitude was the most influential variable within RF, BRT, and SVM while rainfall showed the most important contribution in modelling based on the CART algorithm. This study proposed a new modelling framework that is easily replicable in different contexts for the assessment of LD modelling and analysis.

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Section: Methodsmentioning

confidence: 99%

A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

et al. 2022

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“…GRU or Gated Recurrent Unit is a form of RNN that induces the Processing of different kinds of sequential information such as text variables, audio format, and time series information. The use of gated mechanisms of GRU is necessary for selectively passing and updating the information present in the hidden layers in DL [25]. Such a measure takes into consideration the extent of the flow of information through the [16].…”

Section: Analysis Of Bi-gru In Crop Yield Predictionmentioning

confidence: 99%

Crop Yield Prediction using Machine Learning and Deep Learning Techniques

Venkata Rama Rao Kolipaka

2023

IJRITCC

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Crop yield prediction has been designated as a major predictive analysis technique that increases the potential of the agricultural industry. The utilisation of such a measure has been important for the farmers to understand the yields of crops during the particular season from a data analytical point of view. Such an aspect has fallen under the concept of predictive analysis which allows the farmers, agricultures, and farming businessmen to make strategic decisions in terms of cultivation. The application of predictive analysis has been useful for understanding the specific set of crops to be sown during the season, and the types of fertilisers to be applied to the crops for an increased output. Risk analysis with the help of predictive modelling of crops helps in the improvement of the overall agriculture business and increases the potential of the farmers to improve their revenue collection. Once they have the potential of understanding the specific parameters of agriculture, the decision making for reducing the risks, increasing the overall gain from the crops, and such other aspects can be easily known. Predictive analysis allows the farmers to gain an expansive amount of knowledge regarding the weather conditions in the future, the quality of the soil for growing the crops, the nutrients required which are to be replenished for increasing the crop field, and several such parameters. Machine Learning or ML and Deep Learning or DL methods have been seen to be extremely important for data analysis and predictions. Several kinds of tools and techniques such as neural networking, Bi GRU, Maxout classifiers, and others have been applied within the agricultural industry. The study would lead to an extensive analysis of the different kinds of Machine Learning and Deep Learning techniques used for increasing the crop yields by prediction analysis. Such a measure would prove to be extremely important to make significant decisions regarding the importing and exporting of crops, and the pricing structure for the grains to be sold in the market. The distribution of crops and also making fruitful decisions regarding future crop plantations can also be inspected with the help of the ML and DL tools.

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“…AI procedures are generally based on machine learning (ML), pattern recognition (PR) and deep learning (DL), which further consist of artificial neural networks (ANNs), fuzzy logic, genetic programming (GP), etc. [ 16 , 17 , 31 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. The application of these AI techniques in structural engineering are traced back to the early 1980s, where they were first used in the compliance checking of design codes [ 51 , 52 ] and expert interactive design of concrete columns (EIDOCC) [ 53 , 54 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Lateral Load Carrying Capacity of Reinforced Concrete Rectangular Columns: Gene Expression Programming

et al. 2022

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This research presents a novel approach of artificial intelligence (AI) based gene expression programming (GEP) for predicting the lateral load carrying capacity of RC rectangular columns when subjected to earthquake loading. To achieve the desired research objective, an experimental database assembled by the Pacific Earthquake Engineering Research (PEER) center consisting of 250 cyclic tested samples of RC rectangular columns was employed. Seven input variables of these column samples were utilized to develop the coveted analytical models against the established capacity outputs. The selection of these input variables was based on the linear regression and cosine amplitude method. Based on the GEP modelling results, two analytical models were proposed for computing the flexural and shear capacity of RC rectangular columns. The performance of both these models was evaluated based on the four key fitness indicators, i.e., coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), and root relative squared error (RRSE). From the performance evaluation results of these models, R2, RMSE, MAE, and RRSE were found to be 0.96, 53.41, 38.12, and 0.20, respectively, for the flexural capacity model, and 0.95, 39.47, 28.77, and 0.22, respectively, for the shear capacity model. In addition to these fitness criteria, the performance of the proposed models was also assessed by making a comparison with the American design code of concrete structures ACI 318-19. The ACI model reported R2, RMSE, MAE, and RRSE to be 0.88, 101.86, 51.74, and 0.39, respectively, for flexural capacity, and 0.87, 238.74, 183.66, and 1.35, respectively, for shear capacity outputs. The comparison depicted a better performance and higher accuracy of the proposed models as compared to that of ACI 318-19.

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Fuzzy Logic Model to Assess Desertification Intensity Based on Vulnerability Indices

Cited by 13 publications

References 8 publications

A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

Crop Yield Prediction using Machine Learning and Deep Learning Techniques

Predicting the Lateral Load Carrying Capacity of Reinforced Concrete Rectangular Columns: Gene Expression Programming

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