Fuzzy-logic modeling of land suitability for hybrid poplar across the Prairie Provinces of Canada

Machine learning methods for the modelling and optimisation of biogas production from anaerobic digestion: a review

Biogas plant operators often face huge challenges in the monitoring, controlling and optimisation of the anaerobic digestion (AD) process, as it is very sensitive to surrounding changes, which often leads to process failure and adversely affects biogas production. Conventional implemented methods and mechanistic models are impractical and find it difficult to model the nonlinear and intricate interactions of the AD process. Thus, the development of machine learning (ML) algorithms has attracted considerable interest in the areas of process optimization, real-time monitoring, perturbation detection and parameter prediction. This paper provides a comprehensive and up-to-date overview of different machine learning algorithms, including artificial neural network (ANN), fuzzy logic (FL), adaptive network-based fuzzy inference system (ANFIS), support vector machine (SVM), genetic algorithm (GA) and particle swarm optimization (PSO) in terms of working mechanism, structure, advantages and disadvantages, as well as their prediction performances in modelling the biogas production. A few recent case studies of their applications and limitations are also critically reviewed and compared, providing useful information and recommendation in the selection and application of different ML algorithms. This review shows that the prediction efficiency of different ML algorithms is greatly impacted by variations in the reactor configurations, operating conditions, influent characteristics, selection of input parameters and network architectures. It is recommended to incorporate mixed liquor volatile suspended solids (MLVSS) concentration of the anaerobic digester (ranging from 16,500 to 46,700 mg/L) as one of the input parameters to improve the prediction efficiency of ML modelling. This review also shows that the combination of different ML algorithms (i.e. hybrid GA-ANN model) could yield better accuracy with higher R2 (0.9986) than conventional algorithms and could improve the optimization model of AD. Besides, future works could be focused on the incorporation of an integrated digital twin system coupled with ML techniques into the existing Supervisory Control and Data Acquisition (SCADA) system of any biogas plant to detect any operational abnormalities and prevent digester upsets.

Geospatial technology in agroforestry: status, prospects, and constraints

Agroforestry has an indispensable role in food and livelihood security in addition to its capacity to combat the detrimental effects of climate change. However, agroforestry has not been properly promoted and exploited due to lack of precise extent, geographical distribution, and carbon sequestration (CS) assessment. The recent advent of geospatial technologies, as well as free availability of spatial data and software, can provide new insights into agroforestry resources assessment, decision-making, and policy development despite agroforestry's small spatial extent, isolated nature, and higher structural and functional complexity of agroforestry. In this review, the existing application of geospatial technologies together with its constraints and limitations as well as the potential future application for agroforestry has been discussed. The review reveals that the application of optical remote sensing in agroforestry includes spatial extent mapping, production of tree species spectral signature, CS assessment, and suitability mapping. Simultaneously, the recent surge in the use of synthetic aperture radar in conjunction with algorithms based on vegetation photosynthesis and optical data enables a more accurate estimation of gross primary productivity at different scales. However, unmanned aerial vehicles equipped with sensors, such as multispectral, LiDAR, hyperspectral, and thermal, offer a considerably higher potential and accuracy than satellite-based datasets. In the future, the health monitoring of agroforestry systems can be a key concern that may be addressed by utilizing hyperspectral and thermal datasets to analyze plant biochemistry, chlorophyll fluorescence, and water stress. Additionally, current (GEDI, ECOSTRESS) and future space agency missions (BIOMASS, FLEX, NISAR, TRISHNA) have enormous potential to shed fresh light on agroforestry systems.

Fuzzy-based models' performance on qualitative and quantitative land suitability evaluation for cotton cultivation in Sarayan County, South Khorasan Province, Iran

Using appropriate models in the land use planning process will help increase the accuracy and precision of decisions made by designers. The aim of this study was to investigate and compare fuzzy-based models (fuzzy set theory, fuzzy-AHP, and fuzzy-ANP) to evaluate the suitability of cotton cultivation in Sarayan region (located in eastern Iran). Twenty-eight land units were selected. Weighted arithmetic means of characteristics were performed in representative soil profiles of each unit. Landform-related characteristics were directly entered into the land suitability evaluation modeling. The land index was calculated using three selective qualitative land suitability model guidelines. Qualitative and quantitative land suitability was estimated. The validity of models was determined by r², RMSE, GMER, and MAPE indicators between predicted and actual production. Soil texture, pH, calcium carbonate equivalent, drainage, organic matter, salinity and sodicity, slope, and gypsum are the most important, respectively. Also, the fuzzy-ANP method is more efficient than other models due to its higher r² (0.98) and lower RMSE (4.31) and MAPE (0.56) and GMER (0.99) closer to 1. The value of cotton production using fuzzy, fuzzy-AHP, and fuzzy-ANP methods was calculated as 1085 to 4235, 1235 to 4318, and 1391 to 4452 tons per hectare, respectively. The high efficiency of the fuzzy-ANP model is due to the characteristics of the lands used in the evaluation process that are not independent of each other and this model considers them. Examining these models in different weather conditions and combining with the other computational intelligence methods in future experiments are recommended.

Evaluation of Scale Management Suitability Based on the Entropy-TOPSIS Method

The evaluation of scale management suitability provides a comprehensive assessment of the various factors driving farmland management conditions. This research objectively evaluated the conditions for scale management suitability by applying the entropy-TOPSIS method with the aim of effectively balancing the space for agricultural production, the development of towns, and ecological protection. First, to ensure a balance between agricultural production, urban development, and ecological protection, 13 indexes were selected to represent the following three areas: natural factors, socioeconomic factors, and characteristics of cultivated land factors. The original matrix was standardized to evaluate the suitability of natural resources, the social economy, and cultivated land conditions, and a comprehensive suitability evaluation of scale management in the Jiangjin District of Chongqing was conducted. The research results divide the study area into four regions based on the level of scale management suitability. Examining the spatial distribution, the level of scale management suitability decreased gradually from north to south, regions at the high and middle levels of scale management suitability were concentrated in the northern area beside the Yangtze river, and the regions at a low level were concentrated in the southern mountain area. This research can provide a reference for the rational utilization of land resources and land use policymaking.

Comprehensive assessment of paddy soil quality under land consolidation: a novel perspective of microbiology

Soil quality assessment is an important means to demonstrate how effective land consolidation is. However, the existing assessment system is not sufficient to reflect actual soil quality. So, the purpose of this study is to integrate abiological and biological indicators into a comprehensive assessment to evaluate the paddy soil quality under different land consolidation practices. Soil samples were collected from 35 paddy sites under different land consolidation practices including land merging, land leveling (LL), ditch construction (DC) and application of organic fertilizer (AO). A total of 10 paddy sites were selected under conventional tillage (CT) from non-land consolidation area as a control group in Y county, China. The results indicated that soil organic matter (OM), total nitrogen (TN), available phosphorus, bacterial functional diversity (BFD), bacterial and fungal abundances were significantly improved. Fields under LL, among all the land consolidation practices, might still face the risk of land degradation caused by low TN, OM and microbial diversity. High microbial biomass, BFD and OM were significantly higher in fields under AO in nutrient cycle. According to the results of comprehensive assessment, the samples with severe heavy metal contamination and low microbial diversity were generally concentrated in CT. These results indicated that land consolidation was an efficient technique to improve soil quality and could achieve higher quality of agricultural products.

A Study of the Spatial Difference of the Soil Quality of The Mun River Basin during the Rainy Season

The Mun River basin is one of the main grain-producing areas of Thailand, and the rainy season is the main period for crop planting after being idle during the dry season. However, the soil conditions are variable, so an assessment of soil quality during the rainy season is necessary for improving soil condition and crop production. The aim of this study was to conduct a soil quality assessment based on soil samples. To attain that, a minimum data set theory was used to screen evaluation indicators and geographically weighted regression was performed to obtain spatial interpolations of indicators, while the fuzzy logic model was used to determine the soil quality results. The results showed that the contents of indicators had similar spatial trends as their contents declined from the western to the eastern region of the basin. The soil quality results showed that the poor soil was in the middle of the basin, where the main land use is paddy fields, and the good soil was in the southwest of the basin, where forests and dry fields are widely distributed. The results indicated that the soil quality in the Mun River basin varied greatly, especially for farmland, so these findings will be helpful for improving soil conditions and grain production in the Mun River basin.

Multi-Criteria Decision Analysis for the Land Evaluation of Potential Agricultural Land Use Types in a Hilly Area of Central Vietnam

Land evaluation is a process that is aimed at the sustainable development of agricultural production in rural areas, especially in developing countries. Therefore, land evaluation involves many aspects of natural conditions, economic, and social issues. This research was conducted in a hilly region of Central Vietnam to assess the land suitability of potential agricultural land use types that are based on scientific and local knowledge. In the frame of this research, Participatory Rural Appraisal (PRA); Analytical Hierarchy Analysis (AHP); Geographic Information System (GIS); and, scoring based scientific literature and local knowledge were applied for Multi-Criteria Decision Analysis (MCDA) for land use evaluation. The results of the PRA survey reveal that five plants offer great agricultural potential in the research area, namely rice, cassava, acacia, banana, and rubber. The land suitability of each plant type varies, depending on physical conditions as well as economic and social aspects. Acacia and cassava represent the most suitable plant types in the research area. Recommendations regarding agricultural land use planning in the A Luoi district are brought forward based on the land evaluation results. The combination of scientific and local knowledge in land assessment based on GIS technology, AHP, and PRA methods is a promising approach for land evaluation.

What makes the Asian bush mosquito Aedes japonicus japonicus feel comfortable in Germany? A fuzzy modelling approach

Background

The Asian bush mosquito Aedes japonicus japonicus is an invasive species native to East Asia and has become established in North America and Europe. On both continents, the species has spread over wide areas. Since it is a potential vector of human and livestock pathogens, distribution and dissemination maps are urgently needed to implement targeted surveillance and control in case of disease outbreaks. Previous distribution models for Europe and Germany in particular focused on climate data. Until now, effects of other environmental variables such as land use and wind remained unconsidered.

Results

In order to better explain the distribution pattern of Ae. j. japonicus in Germany at a regional level, we have developed a nested approach that allows for the combination of data derived from (i) a climate model based on a machine-learning approach; (ii) a landscape model developed by means of ecological expert knowledge; and (iii) wind speed data. The approach is based on the fuzzy modelling technique that enables to precisely define the interactions between the three factors and additionally considers uncertainties with regard to the acceptance of certain environmental conditions. The model combines different spatial resolutions of data for Germany and achieves a much higher degree of accuracy than previous published distribution models. Our results reveal that a well-suited landscape structure can even facilitate the occurrence of Ae. j. japonicus in a climatically unsuitable region. Vice versa, unsuitable land use types such as agricultural landscapes and coniferous forests reduce the occurrence probability in climatically suitable regions.

Conclusions

The approach has significantly improved existing distribution models of Ae. j. japonicus for the area of Germany. We generated distribution maps with a resolution of 100 × 100 m that can serve as a basis for the design of control measures. All model input data and scripts are open source and freely available, so that the model can easily be applied to other countries or, more generally, to other species.

Ecological Vulnerability Assessment Based on Fuzzy Analytical Method and Analytic Hierarchy Process in Yellow River Delta

The Yellow River Delta (YRD), located in Yellow River estuary, is characterized by rich ecological system types, and provides habitats or migration stations for wild birds, all of which makes the delta an ecological barrier or ecotone for inland areas. Nevertheless, the abundant natural resources of YRD have brought huge challenges to the area, and frequent human activities and natural disasters have damaged the ecological systems seriously, and certain ecological functions have been threatened. Therefore, it is necessary to determine the status of the ecological environment based on scientific methods, which can provide scientifically robust data for the managers or stakeholders to adopt timely ecological protection measures. The aim of this study was to obtain the spatial distribution of the ecological vulnerability (EV) in YRD based on 21 indicators selected from underwater status, soil condition, land use, landform, vegetation cover, meteorological conditions, ocean influence, and social economy. In addition, the fuzzy analytic hierarchy process (FAHP) method was used to obtain the weights of the selected indicators, and a fuzzy logic model was constructed to obtain the result. The result showed that the spatial distribution of the EV grades was regular, while the fuzzy membership of EV decreased gradually from the coastline to inland area, especially around the river crossing, where it had the lowest EV. Along the coastline, the dikes had an obviously protective effect for the inner area, while the EV was higher in the area where no dikes were built. This result also showed that the soil condition and groundwater status were highly related to the EV spatially, with the correlation coefficients −0.55 and −0.74 respectively, and human activities had exerted considerable pressure on the ecological environment.

Prognoses of diameter and height of trees of eucalyptus using artificial intelligence

Models of individual trees are composed of sub-models that generally estimate competition, mortality, and growth in height and diameter of each tree. They are usually adopted when we want more detailed information to estimate forest multiproduct. In these models, estimates of growth in diameter at 1.30m above the ground (DBH) and total height (H) are obtained by regression analysis. Recently, artificial intelligence techniques (AIT) have been used with satisfactory performance in forest measurement. Therefore, the objective of this study was to evaluate the performance of two AIT, artificial neural networks and adaptive neuro-fuzzy inference system, to estimate the growth in DBH and H of eucalyptus trees. We used data of continuous forest inventories of eucalyptus, with annual measurements of DBH, H, and the dominant height of trees of 398 plots, plus two qualitative variables: genetic material and site index. It was observed that the two AIT showed accuracy in growth estimation of DBH and H. Therefore, the two techniques discussed can be used for the prognosis of DBH and H in even-aged eucalyptus stands. The techniques used could also be adapted to other areas and forest species.

Fuzzy logic applied to prospecting for areas for installation of wood panel industries

Prospecting for suitable areas for forestry operations, where the objective is a reduction in production and transportation costs, as well as the maximization of profits and available resources, constitutes an optimization problem. However, fuzzy logic is an alternative method for solving this problem. In the context of prospecting for suitable areas for the installation of wood panel industries, we propose applying fuzzy logic analysis for simulating the planting of different species and eucalyptus hybrids in Espírito Santo State, Brazil. The necessary methodological steps for this study are as follows: a) agriclimatological zoning of different species and eucalyptus hybrids; b) the selection of the vector variables; c) the application of the Euclidean distance to the vector variables; d) the application of fuzzy logic to matrix variables of the Euclidean distance; and e) the application of overlap fuzzy logic to locate areas for installation of wood panel industries. Among all the species and hybrids, Corymbia citriodora showed the highest percentage values for the combined very good and good classes, with 8.60%, followed by Eucalyptus grandis with 8.52%, Eucalyptus urophylla with 8.35% and Urograndis with 8.34%. The fuzzy logic analysis afforded flexibility in prospecting for suitable areas for the installation of wood panel industries in the Espírito Santo State can bring great economic and social benefits to the local population with the generation of jobs, income, tax revenues and GDP increase for the State and municipalities involved. The proposed methodology can be adapted to other areas and agricultural crops.

A diatom-based biological condition gradient (BCG) approach for assessing impairment and developing nutrient criteria for streams

Over-enrichment leading to excess algal growth is a major problem in rivers and streams. Regulations to protect streams typically incorporate nutrient criteria, concentrations of phosphorus and nitrogen that should not be exceeded in order to protect biological communities. A major challenge has been to develop an approach for both categorizing streams based on their biological conditions and determining scientifically defensible nutrient criteria to protect the biotic integrity of streams in those categories. To address this challenge, we applied the Biological Condition Gradient (BCG) approach to stream diatom assemblages to develop a system for categorizing sites by level of impairment, and then examined the related nutrient concentrations to identify potential nutrient criteria. The six levels of the BCG represent a range of ecological conditions from natural (1) to highly disturbed (6). A group of diatom experts developed a set of rules and a model to assign sites to these levels based on their diatom assemblages. To identify potential numeric nutrient criteria, we explored the relation of assigned BCG levels to nutrient concentrations, other anthropogenic stressors, and possible confounding variables using data for stream sites in New Jersey (n=42) and in surrounding Mid-Atlantic states, USA (n=1443). In both data sets, BCG levels correlated most strongly with total phosphorus and the percentage of forest in the watershed, but were independent of pH. We applied Threshold Indicator Taxa Analysis (TITAN) to determine change-points in the diatom assemblages along the BCG gradient. In both data sets, statistically significant diatom changes occurred between BCG levels 3 and 4. Sites with BCG levels 1 to 3 were dominated by species that grow attached to surfaces, while sites with BCG scores of 4 and above were characterized by motile diatoms. The diatom change-point corresponded with a total phosphorus concentration of about 50μg/L.

Using fuzzy logic analysis for siting decisions of infiltration trenches for highway runoff control

Determining optimal locations for best management practices (BMPs), including their field considerations and limitations, plays an important role for effective stormwater management. However, these issues have been often overlooked in modeling studies that focused on downstream water quality benefits. This study illustrates the methodology of locating infiltration trenches at suitable locations from spatial overlay analyses which combine multiple layers that address different aspects of field application into a composite map. Using seven thematic layers for each analysis, fuzzy logic was employed to develop a site suitability map for infiltration trenches, whereas the DRASTIC method was used to produce a groundwater vulnerability map on the island of Oahu, Hawaii, USA. In addition, the analytic hierarchy process (AHP), one of the most popular overlay analyses, was used for comparison to fuzzy logic. The results showed that the AHP and fuzzy logic methods developed significantly different index maps in terms of best locations and suitability scores. Specifically, the AHP method provided a maximum level of site suitability due to its inherent aggregation approach of all input layers in a linear equation. The most eligible areas in locating infiltration trenches were determined from the superposition of the site suitability and groundwater vulnerability maps using the fuzzy AND operator. The resulting map successfully balanced qualification criteria for a low risk of groundwater contamination and the best BMP site selection. The results of the sensitivity analysis showed that the suitability scores were strongly affected by the algorithms embedded in fuzzy logic; therefore, caution is recommended with their use in overlay analysis. Accordingly, this study demonstrates that the fuzzy logic analysis can not only be used to improve spatial decision quality along with other overlay approaches, but also is combined with general water quality models for initial and refined searches for the best locations of BMPs at the sub-basin level.

Mathematical decision theory applied to land capability: a case study in the community of madrid

In land evaluation science, a standard data set is obtained for each land unit to determine the land capability class for various uses, such as different farming systems, forestry, or the conservation or suitability of a specific crop. In this study, we used mathematical decision theory (MDT) methods to address this task. Mathematical decision theory has been used in areas such as management, finance, industrial design, rural development, the environment, and projects for future welfare to study quality and aptness problems using several criteria. We also review MDT applications in soil science and discuss the suitability of MDT methods for dealing simultaneously with a number of problems. The aim of the work was to show how MDT can be used to obtain a valid land quality index and to compare this with a traditional land capability method. Therefore, an additive classification method was applied to obtain a land quality index for 122 land units that were compiled for a case study of the Community of Madrid, Spain, and the results were compared with a previously assigned land capability class using traditional methods based on the minimum requirements for land attributes.

Fuzzy logic and adaptive neuro-fuzzy inference system for characterization of contaminant exposure through selected biomarkers in African catfish

This study represents a first attempt at applying a fuzzy inference system (FIS) and an adaptive neuro-fuzzy inference system (ANFIS) to the field of aquatic biomonitoring for classification of the dosage and time of benzo[a]pyrene (BaP) injection through selected biomarkers in African catfish (Clarias gariepinus). Fish were injected either intramuscularly (i.m.) or intraperitoneally (i.p.) with BaP. Hepatic glutathione S-transferase (GST) activities, relative visceral fat weights (LSI), and four biliary fluorescent aromatic compounds (FACs) concentrations were used as the inputs in the modeling study. Contradictory rules in FIS and ANFIS models appeared after conversion of bioassay results into human language (rule-based system). A "data trimming" approach was proposed to eliminate the conflicts prior to fuzzification. However, the model produced was relevant only to relatively low exposures to BaP, especially through the i.m. route of exposure. Furthermore, sensitivity analysis was unable to raise the classification rate to an acceptable level. In conclusion, FIS and ANFIS models have limited applications in the field of fish biomarker studies.

A Linguistic Approach to Model Urban Growth

This paper presents a linguistic approach for modeling urban growth. The authors developed a methodological framework which utilizes Fuzzy Set theory to capture and describe the effect of urban features on urban growth and applies Cellular Automata techniques to simulate urban growth. Although several approaches exist that combine Fuzzy Logic and Cellular Automata for urban growth modeling, the authors focused on the ability to use partial knowledge and combine theory-driven and data driven knowledge. To achieve this, a parallel connection between the input variables is introduced which further allows the model to disengage from severe data limitations. In this approach, a number of parallel fuzzy systems are used, each one of which focuses on different types of urban growth factors, different drivers or restrictions of development. The effects of all factors under consideration are merged into a single internal thematic layer that maps the suitability for urbanization for each area, providing thus an information flow familiar to the human conceptualization of the phenomenon. Following, cellular automata techniques are used to simulate urban growth. The proposed methodology is applied in the Mesogeia area in the Attica basin (Athens) for the period 1990-2004 and provides realistic estimations for urban growth.

Assessing the Application of a Geographic Presence-Only Model for Land Suitability Mapping

Recent advances in ecological modeling have focused on novel methods for characterizing the environment that use presence-only data and machine-learning algorithms to predict the likelihood of species occurrence. These novel methods may have great potential for land suitability applications in the developing world where detailed land cover information is often unavailable or incomplete. This paper assesses the adaptation and application of the presence-only geographic species distribution model, MaxEnt, for agricultural crop suitability mapping in a rural Thailand where lowland paddy rice and upland field crops predominant. To assess this modeling approach, three independent crop presence datasets were used including a social-demographic survey of farm households, a remote sensing classification of land use/land cover, and ground control points, used for geodetic and thematic reference that vary in their geographic distribution and sample size. Disparate environmental data were integrated to characterize environmental settings across Nang Rong District, a region of approximately 1,300 sq. km in size. Results indicate that the MaxEnt model is capable of modeling crop suitability for upland and lowland crops, including rice varieties, although model results varied between datasets due to the high sensitivity of the model to the distribution of observed crop locations in geographic and environmental space. Accuracy assessments indicate that model outcomes were influenced by the sample size and the distribution of sample points in geographic and environmental space. The need for further research into accuracy assessments of presence-only models lacking true absence data is discussed. We conclude that the Maxent model can provide good estimates of crop suitability, but many areas need to be carefully scrutinized including geographic distribution of input data and assessment methods to ensure realistic modeling results.

Land availability for biofuel production

Marginal agricultural land is estimated for biofuel production in Africa, China, Europe, India, South America, and the continental United States, which have major agricultural production capacities. These countries/regions can have 320-702 million hectares of land available if only abandoned and degraded cropland and mixed crop and vegetation land, which are usually of low quality, are accounted. If grassland, savanna, and shrubland with marginal productivity are considered for planting low-input high-diversity (LIHD) mixtures of native perennials as energy crops, the total land availability can increase from 1107-1411 million hectares, depending on if the pasture land is discounted. Planting the second generation of biofuel feedstocks on abandoned and degraded cropland and LIHD perennials on grassland with marginal productivity may fulfill 26-55% of the current world liquid fuel consumption, without affecting the use of land with regular productivity for conventional crops and without affecting the current pasture land. Under the various land use scenarios, Africa may have more than one-third, and Africa and Brazil, together, may have more than half of the total land available for biofuel production. These estimations are based on physical conditions such as soil productivity, land slope, and climate.