Table 6 - uploaded by Arnold Bregt
Content may be subject to copyright.
Source publication
Land suitability classification is important in planning and managing sustainable land use. Most approaches to land suitability analysis combine a large number of land and soil parameters, and are time-consuming and costly. In this study, a potentially useful technique (combined feature selection and fuzzy-AHP method) to increase the efficiency of...
Context in source publication
Context 1
... determining the most important parameters by different feature selection methods, the pairwise comparison matrices were calculated for the selected parameters. These weights are given in Tables 6 and 7. Land suitability maps for different feature selection methods are shown in Figure 5. ...
Similar publications
Forest disease is one of the most important factors affecting tree growth and product quality, reducing economic values of forest ecosystem goods and services. In order to prevent and control forest diseases, accurate detection in a timely manner is essential. Unmanned aerial vehicles (UAVs) are becoming an important tool for acquiring multispectra...
In tree species classifications, different spectral bands feature different importance, and the manner of determining the importance of one band is a problem that needs to be solved. In this study, eight bands of the WorldView-2 fusion data were used as information sources, and a recursive feature elimination based on maximum likelihood (MLC-RFE) w...
Remote-sensing technology has been a useful tool for mapping and characterizing forest cover types and species composition, providing valuable information for effective forest management. This study investigates the application of a genetic algorithm (GA)-based approach on Normalized Difference Vegetation Index (NDVI) to separate local forest commu...
Background. Despite its importance, time-motion and technical-tactical analyses are rarely applied in Taekwondo by coaches and in technical assistance because of the lack of technical-tactical protocols for real-time analysis. Problem and aim. This study aimed to elaborate and validate a new technical-tactical protocol in Taekwondo for real-time an...
In object-oriented remote sensing image classification experiments, the dimension of the feature space is often high, leading to the “dimension disaster”. If a reasonable feature selection method is adopted, the classification efficiency and accuracy of the classifier can be improved. In this study, we took GF-2 remote sensing imagery as the resear...
Citations
... The artificial intelligencebased architecture has better accuracy compared to a traditional statistical algorithm for crop yield prediction on a local to global scale, in recent times (Radočaj & Jurišić, 2022;Whitmire et al., 2021). Various reviews showed that crop LSA concerning with many multicriteria evaluation techniques, i.e., order performance by similarity to ideal solution (AHP, Rashidi & Sharifian, 2022) (TOPSIS, Bagherzadeh & Gholizadeh, 2016), analytical network process (ANP, Mohammadi et al., 2015), maximum entropy (MaxEnt, Estes et al., 2013), simple additive weighting (SAW, Seyedmohammadi et al., 2018), artificial neural network (ANN, Jiao & Liu, 2007;Pourkhabbaz et al., 2014), criteria important through inter-criteria correlation (CRITIC, Mishra et al., 2021), genetic algorithm methods (Hamzeh et al., 2016), adaptive neuro-fuzzy inference system (ANFIS, Houshyar et al., 2017), and hybrid neural-fuzzy model (Dang et al., 2019), was used for socioecological development at individual farm level. Multi-criteria evaluation technique along with machine learning can work as a great tool for LSA (Suruliandi et al., 2021). ...
A total land suitability analysis was carried out through FuzzyAHP technique
for rice and potato crops in West Bengal, India. Around 21 most relevant crop
suitability parameters were selected and classified under five primary criteria, such
as terrain distribution parameter, static soil parameter, available soil nutrient,
agriculture practice parameter, and local variation parameter for the study. The
factors such as NDVI and SAVI values were estimated from Sentinel 2B images in
“SNAP” toolbox software environment, whereas soil nutrients were estimated
through standard laboratory methods. Individual parameter weights were assigned
through the FuzzyAHP technique for sub-criteria as well as for primary criteria. The final crop suitability map was developed showing nearly 20% of the total area as
highly suitable for rice crop, whereas nearly 39% of the area was found suitable for
the potato crop. Comparing the prediction map with yield distribution, it was found
that the southwest region of the study area is very suitable for both rice and potato
crop with higher crop yields in the range of 5 t/ha and 20 t/ha, respectively. Six
different machine learning models, namely random forest, support vector machine,
AdaBoost, extreme gradient boosting, logistic regression, and naïve Bayes, were
utilized for validation of the suitability maps. The support vector machine (SVM)
learning model with the highest AUC (~80%) was found efficient for testing both rice and potato crop suitability. The economic status of farmers can be rejuvenated by selecting the best crop rotation through land suitability analysis.
... They extracted the historical data related to the cultivation and land use pattern in the desired area using satellite images and remote sensing and hired it as input information in a hydrological model. In the research of Hamzeh et al. (2016), by using the fuzzy-hierarchical decision-making structure and satellite images, including images related to land topography, salinity, alkalinity, moisture and soil texture, to make a decision to choose suitable areas for barley cultivation in the Shavor plain of Khuzestan. ...
This paper investigated how the meta-heuristic models can be used to facilitate the use of satellite data and estimation of evapotranspiration (ET) images. Focusing on estimating daily ET directly from received images of the electromagnetic bands of Landsat 8 satellite utilizing meta-heuristic models, authors used daily ET images estimated by the SEBAL algorithm to calibrate and verify these models. The results of this research showed that the ANN model with DC and RMSE of 0.98 and 0.09025 mm/day, respectively, is more accurate compared to the ACO (with DC = 0.65 and RMSE = 1.45 mm/day) and PSO (with DC = 0.23 and RMSE = 1.60 mm/day) models in the verification stage in estimating daily ET images. The ACO model compared to the PSO model is more accurate in estimating ET images with DC of 0.65 and 0.23 in the verification step, respectively. While removing half of the training data, the accuracy of the PSO model surpasses the ACO model with DC of 0.0.85 and 0.80, respectively. Also, the ANN model is more accurate than the other two models in estimating ET, both when considering all the data and half of the training data (with DC = 0.98 and RMSE = 0.09 mm/day).
... Because many parameters are involved in studying drought, a feature selection algorithm was used to reduce the number of parameters and select the most predictive ones. Among different feature selection models, the Best search model was used (Text S1) (Hamzeh et al., 2016). It is an artificial intelligence method with high accuracy that, like the greedy hill climbing algorithm, makes use of local changes in the search space. ...
Forecasting drought and determining relevant data to predict drought are an important topic for decision-makers and planners. It is critical to predicting drought in the south of Fars province, an important agricultural center in Iran located in arid and semi-arid climates. The purpose of this study was to generate a drought map in 2019 using 12 parameters: altitude, aridity index, erosion, groundwater depth, land use, PET (Potential evapotranspiration), precipitation days, precipitation, slope, soil texture, soil salinity, and distance to river, and predict drought maps in 2030 and 2040 using the cellular automata (CA)-Markov model spatially. The fuzzy method was first used to homogenize the data. Next, by evaluating each parameter, the weight of each parameter was calculated using the analytic hierarchy process (AHP), and a map of drought-prone areas was generated. The results of the fuzzy-AHP method showed that the eastern and southeastern regions of the study area were prone to drought. The four most predictive parameters in causing drought, i.e., aridity index, PET, precipitation, and soil texture, were selected using the Best search method and were then chosen as the input to determine drought mapping using the fuzzy and AHP methods. Finally, the CA-Markov model was used to predict future drought maps, and the results showed that in 2030 and 2040 the drought situation in the east and south of the study area would intensify.
... Using the random search algorithm also allowed us to determine the most important waves for detecting plants contaminated by heavy metals and the predicting THQ values (Table S2). This approach is known to have a high degree of accuracy in selecting the most important parameters (Hamzeh et al., 2016). According to the results, it is clear that the spectra selected by the random search method are similar to those by the PCA method, and these Fig. 4 Schematic distribution of weights allocated to different wavelengths obtained as first, second, and third principal components for the 4 target contaminants. ...
Given the tendency of heavy metals to accumulate in soil and plants, the purpose of this study was to determine the contamination levels of Cd, Ni, Pb, and Zn on peppers (leaves and fruit) grown in contaminated soils in industrial centers. For this purpose, we measured the uptake of the four heavy metals by peppers grown in the heavy metal contaminated soils throughout the four growth stages: two-leaf, growth, flowering, and fruiting, and calculated various vegetation indices to evaluate the heavy metal contamination potentials. Electromagnetic waves were also applied for analyzing the responses of the target plants to various heavy metals. Based on the relevant spectral bands identified by principal component analysis (PCA) and random search methods, a regression method was then employed to determine the most optimal spectral bands for estimating the target hazard quotient (THQ). The THQ was found to be the highest in the plants contaminated by Pb (THQ = 62) and Zn (THQ = 5.07). The results of PCA and random search indicated that the spectra at the bands of b570, b650, and b760 for Pb, b400 and b1030 for Ni, b400 and b880 for Cd, and b560, b910, and b1050 for Zn were the most optimal spectra for assessing THQ. Therefore, in future studies, instead of examining the amount of heavy metals in plants by chemical analysis in the laboratory, the responses of the plants to the electromagnetic waves in the identified bands can be readily investigated in the field based on the established correlations.
... Further, this actualizes the need for scaling the learning model's performance. Feature extraction is a technique for determining a significant subgroup of features utilizing various statistical measures for model construction [1]. It can impact a machine learning model's performance by enlisting a substantial feature subset for boosting the performance and categorizing the variability. ...
The innovation in science and technical knowledge has prompted an enormous amount of information for the agrarian sector. Machine learning has risen with massive processing techniques to perceive new contingencies in agricultural development. Machine learning is a novel onset for the investigation and determination of unpredictable agrarian issues. Machine learning models actualize the need for scaling the learning model's performance. Feature selection can impact a machine learning model's performance by defining a significant feature subset for increasing the performance and identifying the variability. This paper explains a novel hybrid feature extraction procedure, which is an aggregation of the correlation-based filter (CFS) and random forest recursive feature elimination (RFRFE) wrapper framework. The proposed feature extraction approach aims to identify an optimal subclass of features from a collection of climate, soil, and groundwater characteristics for constructing a crop-yield forecasting machine learning model with better performance and accuracy. The model's precision and effectiveness are estimated (i) with all the features in the dataset, (ii) with essential features obtained using the learning algorithm's inbuilt 'feature_importances' method, and (iii) with the significant features obtained through the proposed hybrid feature extraction technique. The validation of the hybrid CFS and RFRFE feature extraction approach in terms of evaluation metrics, predictive accuracies, and diagnostic plot performance analysis in comparison with random forest, decision tree, and gradient boosting machine learning algorithms are found to be profoundly satisfying.
... While the common approach is to use a 2 class (suitable/unsuitable) approach for modelling crop suitability [44][45][46][47], we propose a method that models four suitability classes (optimal, moderate, marginal and limited) as a 2 class system may over-estimate climate impacts by not scaling the suitability. Scaled four-class (high, moderate, marginal and unsuitable) suitability models are an alternative for determining suitability classes of agricultural crops from machine learning algorithms [31,[48][49][50][51]. To model the four suitability classes of the four crops, we applied the eXtreme Gradient Boosting (XGBoost) machine learning approach to the variables. ...
Climate change is projected to impact food production stability in many tropical countries through impacts on crop potential. However, without quantitative assessments of where, by how much and to what extent crop production is possible now and under future climatic conditions, efforts to design and implement adaptation strategies under Nationally Determined Contributions (NDCs) and National Action Plans (NAP) are unsystematic. In this study, we used extreme gradient boosting, a machine learning approach to model the current climatic suitability for maize, sorghum, cassava and groundnut in Ghana using yield data and agronomically important variables. We then used multi-model future climate projections for the 2050s and two greenhouse gas emissions scenarios (RCP 2.6 and RCP 8.5) to predict changes in the suitability range of these crops. We achieved a good model fit in determining suitability classes for all crops (AUC = 0.81–0.87). Precipitation-based factors are suggested as most important in determining crop suitability, though the importance is crop-specific. Under projected climatic conditions, optimal suitability areas will decrease for all crops except for groundnuts under RCP8.5 (no change: 0%), with greatest losses for maize (12% under RCP2.6 and 14% under RCP8.5). Under current climatic conditions, 18% of Ghana has optimal suitability for two crops, 2% for three crops with no area having optimal suitability for all the four crops. Under projected climatic conditions, areas with optimal suitability for two and three crops will decrease by 12% as areas having moderate and marginal conditions for multiple crops increase. We also found that although the distribution of multiple crop suitability is spatially distinct, cassava and groundnut will be more simultaneously suitable for the south while groundnut and sorghum will be more suitable for the northern parts of Ghana under projected climatic conditions.
... Topographic indicators, such as slope, were successfully standardized using fuzzy membership functions [78]. The same method was also applied for the standardization of soil criteria [79] and the combination of soil and climate criteria [80] in agriculture land suitability calculations. Membership function parameters were marked as a and d for the definition of support, alongside b and c for the definition of the core of the membership function [81]. ...
Soybean is regarded as one of the most produced crops in the world, presenting a source of high-quality protein for human and animal diets. The general objective of the study was to determine the optimal soybean land suitability and conduct its mapping based on the multicriteria analysis. The multicriteria analysis was based on Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) integration, using Sentinel-2 multitemporal images for suitability validation. The study area covered Osijek-Baranja County, a 4155 km 2 area located in eastern Croatia. Three criteria standardization methods (fuzzy, stepwise and linear) were evaluated for soybean land suitability calculation. The delineation of soybean land suitability classes was performed by k-means unsupervised classification. An independent accuracy assessment of calculated suitability values was performed by a novel approach with peak Normalized Difference Vegetation Index (NDVI) values, derived from four Sentinel-2 multispectral satellite images. Fuzzy standardization with the combination of soil and climate criteria produced the most accurate suitability values, having the top coefficient of determination of 0.8438. A total of 14.5% of the study area (602 km 2) was determined as the most suitable class for soybean cultivation based on k-means classification results, while 64.3% resulted in some degree of suitability.
... Fuzzy logic also allows us to describe features that cannot be expressed in numbers. It allows us to describe occurrences of an ambiguous nature that cannot be described in binary terms [22][23][24]. ...
The quality of technical services is one of the main criteria for assessing the service processes of agricultural machinery, and it has a significant impact on the decision-making process when choosing a service provider. Technical service quality has a significant role in maintaining agricultural machinery in optimal technical condition, thus ensuring its high reliability and durability. The purpose of this study is to present a decision support method for choosing the right agricultural machinery service facility. The method is based on fuzzy inference. The choice of service workshop is based on decision criteria individually accepted by farmers (experts). The method was checked by way of research carried out among 25 farmers facing the choice of a service facility. The decision-making process allows for ranking the decision criteria and decision-makers. The results of the presented research can be used by farm owners and service companies to plan their development directions.
... Pairwise comparison method is frequently use to estimate the weights of the criteria in the GIS-based multiple-criteria decision-making applications (Malczewski and Rinner, 2015). This method has been tested and applied in land suitability analyses (Stoms et al., 2002;Hamzeh et al., 2016;Bozdağ et al., 2016), environmental impact assessment studies (Bojórquez-Tapia et al., 2002;Rikhtegar et al, 2014) and natural resource management (Hessburg et al., 2013). ...
... Generating detailed site specific land resources database, therefore, helps in avoiding the mismatch between the actual requirement and what is actually implemented in the field by identifying its inherent potentials and constraints (AbdelRahman et al. 2016;Mousavi et al. 2017). This eventually helps to develop an ecologically and economically sustainable land use (Hamzeh et al. 2016;Wali et al. 2016) by generating spatial planning and management (El Baroudy 2016;Maddahi et al. 2017) approaches. Fuzzy set methods are potential to address uncertainties, incompleteness and vagueness associated with land resources information (Ceballos-Silva and Lopez-Blanco 2003;Elaalem et al. 2011;Elaalem 2012). ...
... Analytical hierarchy process supported with Geographical Information System (GIS)-based multi-criteria decision analysis (MCDA) techniques are capable of handling multiple and heterogeneous factors (Esa and Assen 2017;Houshyar et al. 2017). They provide structured and spatially explicit evaluation framework (Yalew et al. 2016b), time saving, objective and nonbiased decisions (Hamzeh et al. 2016;Ayoade 2017;Mokarram and Hojati 2017). Cognizance of this background, this paper presents a GIS-based MCDA model for rain-fed teff crop-suitability evaluation in Northern semi-arid highlands of Ethiopia. ...
Teff Eragrostis tef (Zucc.) crop production is essential since it is a major food crop in Ethiopia. This paper presents a teff land suitability evaluation model in Northern semi-arid Ethiopia by using GIS-based multi-criteria decision making analysis (MCDMA) in order to identify areas that best suit sustainable production of the crop. The results showed that 37.59, 56.99 and 5.42% of the area are moderately (S2), marginally (S3) and currently not suitable (N1) for teff crop production. Mean weight diameter, available water capacity, total nitrogen, available phosphorus, exchangeable bases, length of growing period and slope are the major constraints causing serious hurdle for teff production. However, with the application of organic and inorganic fertilizer (especially of nitrogen, phosphorus and potassium fertilizers), tillage, soil and water management practices 2.05, 55.85 and 13.51% changes in suitability from N1 to S3, S3 to S2 and S2 to S1 suitability levels respectively is occurred. This indicates that the area is promising if investments in land and soil managements are made though climate and topographic factors supported by some soil physical and morphological properties are permanently limiting its suitability. GIS-based AHP model is superior for land suitability evaluation of teff crop production in areas with various soil and landscape positions.
