Linear Combination Method Research Articles

A Bayesian ILC Method for CMB B-mode Posterior Estimation and Reconstruction of Primordial Gravity Wave Signal

Abstract The cosmic microwave background (CMB) radiation B-mode polarization signal contains the unique signature of primordial metric perturbations produced during the inflation. The separation of the weak CMB B-mode signal from strong foreground contamination in observed maps is a complex task, and proposed new-generation low-noise satellite missions compete with the weak signal level of this gravitational background. In this paper, for the first time, we employ a foreground model-independent internal linear combination (ILC) method to reconstruct the CMB B-mode signal using simulated observations over large angular scales of the sky of six frequency bands of the future-generation CMB mission Probe of Inflation and Cosmic Origins (PICO). We estimate the joint CMB B-mode posterior density following the interleaving Gibbs steps of the B-mode angular power spectrum and cleaned map samples using the ILC method. We extend and improve the earlier reported Bayesian ILC method to analyze weak CMB B-mode reconstruction by introducing noise-bias corrections at two stages during the ILC weight estimation. By performing 200 Monte Carlo simulations of the Bayesian ILC method, we find that our method can reconstruct the CMB signals and the joint posterior density accurately over large angular scales of the sky. We estimate the Blackwell–Rao statistics of the marginal density of the CMB B-mode angular power spectrum and use them to estimate the joint density of the tensor-to-scalar ratio r and a lensing power spectrum amplitude A lens . Using 200 Monte Carlo simulations of the delensing approach, we find that our method can achieve an unbiased detection of the primordial gravitational-wave signal r with more than 8σ significance for levels of r ≥ 0.01 for input frequency maps with realistic lensing and foregrounds with constant spectral indices.

Identification of groundwater potential recharge zones using GIS-based multi-criteria decision analysis: A case study of semi-arid midlands Manyara fractured aquifer, North-Eastern Tanzania

The extraction of groundwater has recently increased due to water scarcity as a result of human activities including agriculture, industrial and domestic use. This has accelerated the need to spatially identify groundwater potential zones for artificial aquifer recharge and extraction through boreholes drilling. The current study is aimed at identifying groundwater potential recharge zones (GPRZ) of semi-arid midlands Manyara fractured aquifer using Geographic Information System (GIS) and Multi-criteria Decision Analysis (MCDA) based on Analytic Hierarchy Process (AHP) technique. The study area solely depends on groundwater for human survival through deep and shallow boreholes water extraction. Eight influencing factors including aquifer lithology, slopes, land use/land cover (LULC), soil types, drainage density, geological lineament density, flow accumulation, and topographic wetness index (TWI) were determined and reclassified on a scale of 1–5 in ArcGIS 10.6 environments. These factors were weighted with the help of AHP and integrated with ArcGIS pro based on the weighted linear combination (WLC) method. The results were scaled into five recharge potential classes; very high, high, moderate, low, and very low recharge potential zones. The results indicates that about 1607 km2 (14.7%) of the study area is under very high potential recharge zones, 3982 km2 (36.43%) falls under high recharge zones, 3120 km2 (28.55%) moderate, 1658 km2 (15.17%) low and 562 km2 (5.14%) under very low recharge zone. To assess the accuracy of the result a total of 66 boreholes collected from the field were used and the Receiver Operator Characteristics (ROC) curve generated. The area under the curve (AUC) was found to be 78% signifying moderate to higher accuracy of the model. The presented results provide an inventory of information for the land, water, environmental policy maker's authorities, and other stakeholders to enhancing groundwater resource management within semi-arid midlands Manyara basement fractured aquifer of the internal drainage basin North-Eastern Tanzania.

Earthquake Risk and Vulnerability Assessment Based on Wisner and Fuzzy Analytical Hierarchy Process Models in Port Cities: Case Study of Nowshahr City, Iran

INTRODUCTION: Nowshahr port city can suffer a lot of damages in case of an earthquake due to its location in seismotectonics of Alborz with high seismic hazard, proximity to the sea, and expansion on isolated sediments. Therefore, the recognition and analysis of the extent of earthquake vulnerability in different areas of this port city can reduce damages and losses. This study was conducted to analyze and evaluate the earthquake risk and vulnerability and identify safe areas in the city of Nowshahr in times of earthquake. METHODS: In this study, the earthquake risk of Nowshahr city was analyzed based on the Wisner model and the application of multi-criteria decision making (MCDM) approach using fuzzy analytic hierarchy process (FAHP). Initially, the effective criteria, sub-criteria, and indicators in the earthquake risk and vulnerability components were collected through a systematic review based on previous research, field survey, and experts' opinion, and were then screened and weighed by the Delphi method. Finally, the earthquake risk and vulnerability maps were extracted and drawn using a weighted linear combination method and fuzzy index maps related to each component. FINDINGS: According to the findings, about 97.13% (2,810.229 ha), 2.75% (79.567 ha), and 0.12% (3.441 ha) of Nowshahr city were placed in the low, medium, and high-risk category. CONCLUSION: Analysis of risk maps showed that the earthquake risk was higher in the central and northwestern parts of Nowshahr city than in the other areas. These areas were densely populated with heavy traffic jams, in which located high-risk facilities, such as gas lines and water and sewer networks, that reminded of the necessity of planning and urgent action regarding the improvement of urban settlements and organization of urban spaces in this region.

Partially constrained internal linear combination: A method for low-noise CMB foreground mitigation

Internal Linear Combination (ILC) methods are some of the most widely used multi-frequency cleaning techniques employed in CMB data analysis. These methods reduce foregrounds by minimizing the total variance in the coadded map (subject to a signal-preservation constraint), although often significant foreground residuals or biases remain. A modification to the ILC method is the constrained ILC (cILC), which explicitly nulls certain foreground components; however, this foreground nulling often comes at a high price for ground-based CMB datasets, with the map noise increasing significantly on small scales. In this paper we explore a new method, the partially constrained ILC (pcILC), which allows us to optimize the tradeoff between foreground bias and variance in ILC methods. In particular, this method allows us to minimize the variance subject to an inequality constraint requiring that the constrained foregrounds are reduced by at least a fixed factor, which can be chosen based on the foreground sensitivity of the intended application. We test our method on simulated sky maps for a Simons Observatory-like experiment; we find that for cleaning thermal Sunyaev-Zel'dovich (tSZ) contamination at $\ell \in [3000,4800]$, if a small tSZ residual of 20% of the standard ILC residual can be tolerated, the variance of the CMB temperature map is reduced by at least 50% over the cILC value. We also demonstrate an application of this method to reduce noise in CMB lensing reconstruction.

Improved Continuum Joint Configuration Estimation Using a Linear Combination of Length Measurements and Optimization of Sensor Placement.

This paper presents methods for placing length sensors on a soft continuum robot joint as well as a novel configuration estimation method that drastically minimizes configuration estimation error. The methods utilized for placing sensors along the length of the joint include a single joint length sensor, sensors lined end-to-end, sensors that overlap according to a heuristic, and sensors that are placed by an optimization that we describe in this paper. The methods of configuration estimation include directly relating sensor length to a segment of the joint's angle, using an equal weighting of overlapping sensors that cover a joint segment, and using a weighted linear combination of all sensors on the continuum joint. The weights for the linear combination method are determined using robust linear regression. Using a kinematic simulation we show that placing three or more overlapping sensors and estimating the configuration with a linear combination of sensors resulted in a median error of 0.026% of the max range of motion or less. This is over a 500 times improvement as compared to using a single sensor to estimate the joint configuration. This error was computed across 80 simulated robots of different lengths and ranges of motion. We also found that the fully optimized sensor placement performed only marginally better than the placement of sensors according to the heuristic. This suggests that the use of a linear combination of sensors, with weights found using linear regression is more important than the placement of the overlapping sensors. Further, using the heuristic significantly simplifies the application of these techniques when designing for hardware.

Potential landfill sites selection using GIS-based multi-criteria decision analysis in Dodoma capital city, central Tanzania

Solid waste management is a global challenge, especially in developing countries due to the rapid increase in population and urbanization where the availability of sanitary landfills is inevitable. Determining suitable landfill sites is a fundamental aspect for new and rapidly growing cities. The current study is aimed at selecting potential landfill sites using GIS-based multi-criteria decision analysis in Dodoma capital city. Fifteen criteria including proximity from built-up areas, surface water, boreholes, sensitive sites including social service areas, episodic water channels, protected areas including historical sites, faults, land use/land cover, geology, soil type, elevation, slopes, airport, roads, and earthquake epicentres were integrated with the help of analytical hierarchy process (AHP). The landfill sites’ suitability map was produced based on the weighted linear combination method and assigned suitability classes as highly suitable, suitable, moderately suitable, less suitable, and unsuitable. The overall suitability results show that 41,177 ha (14.7%) of the study area is determined as highly suitable for landfills site location. The remaining 83,930 ha (30%), 84,305 ha (30.2%), and 53,508 ha (19.1%) of the area are suitable, moderately suitable, and less suitable respectively while 16,683 ha (6%) is under the unsuitable zone. From the highly suitable area, eleven candidate landfill sites were selected and prioritized using the AHP technique. The final results show landfill site 3 (10,361.94 ha), 5 (3717.85 ha), and 2 (3535.86 ha) were found to be the most highly suitable sites with eigenvector weight of 0.147, 0.122, and 0.121 respectively. Landfill sites 8, 7, and 6 were lastly considered. Field observation involving expertise from geology, hydrogeology, geophysical, and environment confirmed the suitability of selected sites. Thus, these techniques can be employed in developing countries to locate suitable landfill sites to minimize health and environmental impacts.

Further Results on Stability Analysis for Uncertain Delayed Neural Networks with Reliable Memory Feedback Control

This study proposes further results for the stability analysis issue of uncertain delayed neural networks (UDNNs) via the reliable memory feedback control scheme. First, an improved quadratic function method is introduced for constructing a novel term V 1 x t , which can fully excavate some intrinsic relationships between the delay derivative information and time-delay information. Based on the time-delay-product function (TDPF) and linear convex combination method (LCCM), the information storage is further improved for obtaining new theoretical results. Second, by using resultful integral inequalities and correlation analysis approaches, several relaxed criteria are established with respect to the asymptotical stability of the considered UDNNs. Third, the desired reliable memory feedback controller (RMFC) is achieved, which can ensure the system stability of UDNNs. Lastly, two numerical experiments are given to illustrate the significance of the theoretical results.

Potential assessment of multi-renewable energy farms establishment using spatial multi-criteria decision analysis: A case study and mapping in Iran

Many parts of the world may have suitable conditions and potential to establish two or more renewable energy farms. Given the pervasive use of renewable energy globally, assessing the potential of regions to establish a multi-renewable energy farm is of great importance. This study aimed to assess the potential for the establishment of renewable energy farms (solar, wind, biomass, and geothermal) in the eastern regions of Iran. For the first time, the potential for establishing multi-renewable energy farms in an area has been assessed. For this purpose, a series of environmental and economic criteria were addressed and investigated. Respectively, Analytical Network Process (ANP) and Fuzzy logic were employed for obtaining the required weights as well as accounting for the element of uncertainty among the different criteria. The final suitability maps for identification of the most optimal locations for the institution of renewable energy farms were obtained using Weighted Linear Combination (WLC) method. The study area was classified as highly suitable for the establishment of renewable energy farms, as maintained by the final results, wherein 5, 13, 23, and 19% of the entire study area were selected as eligible placements for the institution of biomass, geothermal, solar, and wind power plants, respectively. In consonance with the final map obtained using a combination of individual suitability maps, a total of 5465 km2 worth of area was categorized as highly suitable for the establishment of renewable energy farms. Results also were indicative of the prominence of the different weights assigned to each criterion on identifying the optimal choices of the region concerning the establishment of renewable energy farms. The results can be further used for and are highly advantageous to various managerial, planning, and decision-making procedures in connection with the development of prospective renewable energy sources.

A multicriteria evaluation approach to set forest restoration priorities based on water ecosystem services

Forest plays an important role in keeping water ecosystem services, such as drinking water provision. Thus, payment for ecosystem services is an essential instrument to promote forest restoration in agricultural watersheds. However, funds are limited and must be well planned to ensure water resources conservation and water ecosystem services improvement. In this context, our study aimed to identify priority areas for forest restoration, based on water ecosystem services in agricultural landscapes. For this, we have developed a decision-making support model for agricultural watersheds (in the Atlantic Forest region), based on mixed approaches, that were multicriteria evaluation (MCE) and Participatory Technique. The model will help decision-makers and stakeholders to set priorities for payment for ecosystem services programs implementation. So, we evaluate its application in watersheds with different forest cover patterns to check if it can be applied to different landscape patterns. The base of the model was the following criteria, that were produced with high-resolution data and ranking in the Participatory Technique context, considering their importance for the study: proximity to spring, slope, soil erodibility, topographic index, and land-use/land-cover (LULC). The criteria were aggregated by the Weighted Linear Combination (WLC) method (an MCE method). The priorities maps showed areas classified as high priority near the rivers (at most 200 m far from rivers), on the greatest slopes (>40%), on soils associated with high potential of erosion, and predominantly in agriculture lands. However, this class presented more percentage of the area associated with native forest in the forested watershed (native forest covers 55% of its area) than in the watershed non-forested (native forest covers 25%). Another important point of the final maps was a high percentage of areas associated with the medium class, which is a characteristic of the WLC method. Thus, areas classified as high and medium priority was defined as targets for forest restoration in the watersheds. We can conclude that for small watersheds, the MCE method, with high-resolution data, supports an appropriate prioritization of areas for forest restoration, aiming at the improvement of water ecosystem services. This way, our model can be applied to various payments for ecosystem services schemes in agricultural landscapes worldwide.

Composing an Assistive Control Strategy Based on Linear Bellman Combination From Estimated User's Motor Goal

In assistive control strategies, we must estimate the user's movement intentions. In previous studies, such intended motions were inferred by linearly converting muscle activities to the joint torques of an assistive robot or classifying muscle activities to identify the most likely movement from pre-designed robot motion classes. However, the assistive performances of these approaches are limited in terms of accuracy and flexibility. In this study, we propose an optimal assistive control strategy that uses estimated user movement intentions as the terminal cost function not only for generating movements for different task goals but to precisely enhance the motion with an exoskeleton robot. The optimal assistive policy is derived by blending the pre-computed optimal control laws based on the linear Bellman combination method. Coefficients that determine how to blend the control laws are derived based on low-dimensional feature values that represent the user's movement intention. To validate our proposed method, we conducted an assisted basketball-throwing task and showed that the performances of our subjects significantly improved.

Symmetry analysis of trimer-based all-dielectric metasurfaces with toroidal dipole modes

Herein, we discuss the conditions for excitation of symmetry-protected toroidal dipole modes in all-dielectric metasurfaces composed of trimer or twin-trimer clusters of dielectric disks. Such metasurfaces permit enhanced light–matter interaction due to spatially confined light in resonant systems with a high-quality factor. To describe characteristics of toroidal modes existing in the clusters, we use the magnetic dipole moments approximation, group-theoretical methods, group representation theory, symmetry-adapted linear combination method, and circuit theory. To validate the obtained theoretical results, we fulfill both full-wave numerical simulations and microwave experiments. In particular, we have shown that the toroidal dipole mode appears as a quasi-dark state of the trimer. It can be excited in the metasurface by the field of a linearly polarized wave, providing the symmetry of the trimer is properly reduced. In the metasurface, the properties of the toroidal dipole mode are determined primarily by the parameters of a single trimer and are not a consequence of the periodicity of the array. The coupling of the toroidal dipole modes in the twin-trimers can appear in both bonding and anti-bonding fashion resulting in different orders of the net toroidal dipole moment. Due to the unique field configuration of these modes, the proposed metasurfaces can be considered as a platform for efficient light–matter interaction for enhanced absorption, non-linear switching, and sensing.

Landfill site selection by integrating fuzzy logic, AHP, and WLC method based on multi-criteria decision analysis.

Rapid population growth integrated with poor governance and urban planning is highly challenging resulting key for the selection of unsuitable landfill sites, particularly in developing counties. Therefore, the aim of this study is to investigate the suitable solid waste landfill sites in the capital of the country as a case study, by the integration of Geographical Information System (GIS) with fuzzy logic, analytical hierarchy process (AHP), and weighted linear combination (WLC) method based on multi-criteria decision-making (MCDM). We chose thirteen (13) criteria (9 factors and 4 constraints) and grouped them into two main categories (environmental and socioeconomic) to achieve the objectives. The AHP was employed to evaluate the relative importance of the factors followed by standardization of criteria factors based on fuzzy set theory. Subsequently, all criteria factors were combined based on AHP and fuzzy logic-WLC method in order to obtain land suitability map. Finally, the sites were identified by the intersection of two combined suitability index layers. The obtained results depicted that the integration of fuzzy logic, AHP, and WLC technique with GIS can produce satisfactory results for the suitable locations of solid waste landfill sites over complex topographic regions. Overall, the land suitability obtained based on fuzzy-WLC is more refined and smooth because of its better segregation and its potential to consider full tradeoff between factors and average risk. The AHP was identified (47km2) as high suitable while fuzzy-WLC generated 36km2 as suitable area. Finally, the intersection of both suitability index map shows numerous suitable landfill sites available in Islamabad city; however, the surface areas of the sites are small at individual level (less than 15ha).

Spatial Analysis and Modelling of Wind Farm Site Suitability in Nasarawa State, North-Central Nigeria

There has been an increasing global and local interest in developing renewable, clean, and cheap energy towards achieving Goal number 7 of the Sustainable Development Goals (SDG). However, decisions involving suitable and sustainable locations for renewable energy projects remain an important task. This study employed Geographic Information System (GIS) and Multi-Criteria Decision Analysis (MCDA) to spatially analyze and model wind farm site suitability in Nasarawa State. The aim is to integrate the environmental, social, and economic aspects of decision-making for identifying sustainable wind farm sites. The study distinguished between two sets of decision criteria: decision constraints and decision factors. The former defined the exclusion zones while the latter were standardized based on fuzzy logic to depict varying degrees of suitability across the State. The MCDA applied the weighted linear combination method, with relative weights generated through pairwise comparisons of the analytic hierarchy process to analyze three policy scenarios: equal weights, environmental/social priority, and economic priority scenario. A combination of resulting composite maps from the constraints and the factors gave the final suitability maps. The resulting suitability index (SI) for the respective policy scenario describes the degrees of suitability: Ideal locations were denoted by one (1) and the not suitable locations by zero (0), with values in-between depicting varying degrees of wind farm site suitability. Based on the SI, priority locations indicating areas with good prospects, in addition to the most suitable parcels of land, were identified and delineated. The composite decision constraint revealed that wind farm projects would not be viable in more than half (57.58%) of the State. Wind speed was the major constraint and accounted for the exclusion of 46.25%, with a mean fuzzy membership value of 0.2008 indicating low suitability across the State. Also, the average acceptable wind farm location for the three-policy scenario was 33.33% of the entire study area. Lafia, Obi, Keana, Awe, Nasarawa-Eggon, Wamba and Kokona LGAs were the identified priority Local Government Areas (LGAs). However, only Lafia, Obi, and Nasarawa-Eggon were consistent with changes in the policy objectives. All the priority LGAs have one or more of the most suitable parcels within their administrative boundaries except for Wamba. Despite the severe limitations of wind speed, substantial parts of Nasarawa State still provide great development potentials for wind energy. The “most suitable” locations in Lafia, Nasarawa-Eggon, and Obi LGAs should have first consideration for the development of wind energy in the State.

Primary crusher site selection in open pit mines: Case study in Sungun copper mine

Facility site selection, (such as Primary or in pit Crusher location) is one of the most important steps in mine design process that has a significant impact on economy of the mining operation. This study covers an attempt to find the optimal location for primary crusher in order to support the expansion of Sungun copper mine, which is the second largest open pit mine in Iran. Recent exploration drilling has led to a considerable increase in the ore reserve. Therefore, Sungun started the studies followed with an expansion plan with the goal to increase the capacity from 14 to 28 Mt/Year within the next 8 years. In this paper, 14 effective parameters for finding the best location for a new crusher in five possible options were considered. The options were evaluated based on weighted linear combination method. The results show that the location in South East part of the open pit is the best option for locating of the new crusher. Finally, based on the economic parameters and copper price forecasts, pit optimization and sensitive analysis are applied. Results show that in the worst case, just 0.17 % of ore reserve is affected by the selected area and the minable reserve can cover 78 years of operation.

Urban growth prediction with parcel based 3D urban growth model (PURGOM)

While cities grow horizontally over natural areas, they also grow vertically with high-rise construction in time. The Floor Area Ratio (FAR) is defined as the ratio of the amount of construction area on a parcel to the parcel area. FAR is one of the essential indicators for detecting and measuring 3D (three-dimensional) change. The amount and trends of change differ for each urban settlement and on its internal dynamics.This study consists of two stages: determining and modeling the variables and their weights that affect the FAR values and generating future estimates. First, the criteria affecting the growth trends between the years of 2012 and 2019 in the study area of Saray were examined as five groups at the parcel level with statistical and spatial analysis. It has been determined that the criteria; accessibility, accessibility in line with planning decisions, zoning and land-use decisions, land values, and the built environment affect the FAR value distribution. As a result of the analysis, the selected criteria were evaluated with Geographical Information Systems (GIS) and the weighted linear combination method. The probable spatial distribution of FAR coefficients of each parcel was found. The FAR coefficients obtained were calibrated by real FAR values for 2019. Future predictions for the years 2030 and 2040 were revealed according to the demand scenario. As a result, it was determined that there is a construction pressure on the urban center and near the transportation routes.The primary purpose of this study is to determine current trends by creating a 3D urban growth model based on parcel-level FAR values, making predictions, and producing decision support tools for city managers.•Determination of criteria and weights according to spatial dynamics of the study area.•Determination of the amount and type of urban growth demand specific to the study area and its compatibility with different scenarios.•Prediction of parcel-based 3D urban growth

Bulanık mantık analitik hiyerarşik süreç (BAHS) ile Sinop İli erozyon duyarlılığının mekansal dağılımının belirlenmesi

Dünya nüfusunun artmasıyla arazi üzerindeki baskı artmaktadır. Doğal bir süreç olan erozyon bu artan baskı ile problem haline gelmiştir. Türkiye’de birçok arazi özellikle tarım arazileri erozyon etkisi altında bulunmaktadır. Bu nedenle erozyon etkisini azaltmak için birçok yöntem ve yaklaşımlar geliştirilmiş ve geliştirilmeye devam etmektedir. Bu çalışma ile Bulanık Mantık Analitik Hiyerarşik Süreç (BAHS) yaklaşımı kullanılarak, 586200 ha alan kaplayan Sinop ili arazilerinin erozyon duyarlılık sınıflarının belirlemesi ve haritalanması amaçlanmıştır. Sinop ili Karadeniz Bölgesi’nde yer almaktadır. İlin kuzeyinde Karadeniz, güneydoğusunda Samsun, güneyinde Çorum, batısında Kastamonu illeri bulunmaktadır. Çalışmada erozyon risk sınıfları belirlenirken hem toplam veri seti (TVS) hem de minimum veri seti (MVS) oluşturulmuş ve sonuçları karşılaştırılmıştır. Erozyon sınıfları oluştururken TVS için jeoloji, bitki örtüsü, arazi kullanımı, eğim, derinlik, yağış ve erodobilite parametreleri kullanılmıştır. MVS oluşturulmasında ise temel bileşenler analizi sonucunda, bitki örtüsü, derinlik ve erodobilite parametreleri ele alınmıştır. TVS ve MVS için yapılan sonuç haritasında doğrusal kombinasyon yöntemi uygulanmış ve bu yöntem uygulanırken ArcGIS coğrafi bilgi sistemi programından yararlanılmıştır. Elde edilen sonuçlara göre, TVS’de çalışma alanının % 33’ü yüksek ve çok yüksek, %36’sı ise düşük düzeyde erozyona duyarlı olduğu belirlenirken, MVS’ye göre ise çalışma alanının % 33’ü yüksek ve çok yüksek ve % 35’i düşük erozyona duyarlı olduğu belirlenmiştir. Böylece TVS ve MVS ile elde edilen sonuçların oldukça birbirine yakın değerler gösterdiği tespit edilmiştir. TVS ve MVS ile yapılmış her iki erozyon duyarlılık haritası da, özellikle bitki örtüsünün zayıf ve eğimin yüksek olduğu alanlar ile işlemeli tarımın yapıldığı araziler erozyona çok yüksek duyarlı oldukları görülmektedir.

Investigating the effect of land use changes on soil erosion using RS-GIS and AHP-Fuzzy based techniques (Case Study: Qaresu Watershed, Ardabil, Iran)

The intensity of soil erosion to occur in a region depends on multiple factors including climatic conditions, elevation, terrain, soil type, and land use. Among these factors, land use is one of the particular importance as it reflects the outsized role of humans in the exacerbation of erosion condition. This study aimed to investigate the effects of land use changes on soil erosion in Qaresu watershed, using Remote Sensing (RS) and Geographical Information System (GIS) techniques, a watershed with an area of ​​4370.8 km2 located in the center of Ardabil province, northwest of Iran. For this purpose, the 1985 and 2015 Landsat images captured by TM and OLI-TIRS sensors were used to develop the land use maps of the watershed area using the maximum likelihood method. The erosion zoning maps were then developed by integrating the maps of land use, slope, lithology, distance from roads, distance from streams, precipitation, and soil using the Weighted Linear Combination (WLC) method after an AHP-based weighting stage. The results showed that in the 30-year period from 1985 to 2015, the region has experienced a decrease in the area of forest, dry farming, and rangeland land uses and an increase in the area of land uses defined as urban, barren, irrigated farming, and water cover. In total, dry farming and rangeland were the largest land-uses in the studied area. According to the developed erosion zoning maps, in 1985, 14.4% and 36.84%, and in 2015, 15.64% and 32.3% of the studied area belonged to high and very high risk zones in terms of erosion potential, respectively. In defined two periods, high risk and very high risk zones were mostly positioned over dry and irrigated farmlands.

Optimization of the Weighted Linear Combination Method for Agricultural Land Suitability Evaluation Considering Current Land Use and Regional Differences

This study was aimed at optimizing the weighted linear combination method (WLC) for agricultural land suitability evaluation (ALSE) through indicator selection, weight determination, and classification of overall suitability scores in Handan, China. Handan is a representative research area with distinct agricultural advantages and regional differences in land use, where the expansion of construction land has led to a rapid decrease of agricultural land in recent years. Natural factors (topography, climate, soil conditions, and vegetation cover) and socioeconomic factors (land use and spatial accessibility) were selected to establish a more comprehensive evaluation system. The index weight was calculated by the mutual information between index suitability and current land use. The consistency index was used to identify the boundary value dividing the overall suitability score into a suitable category and unsuitable category in each sub-region. The results demonstrated that the optimized WLC-ALSE model outperformed the comparison models using conventional methods in terms of the consistency between the evaluation results and current land use. Owing to the increasing limitations of topography, soil conditions, spatial accessibility, and land use, the proportions of suitable land in Zone 1, Zone 2, and Zone 3 were 77.4%, 67.5%, and 30.9%, respectively. The agricultural land unsuitable for agriculture (14.5%) was less than non-agricultural land suitable for agriculture (7.4%), indicating that agricultural land had low growth potential in Handan. Finally, specific recommendations were made to improve agricultural land suitability, alleviate land use conflicts, and further optimize the model. The results can provide effective guidance for WLC-ALSE and land use decision-making for sustainable agriculture.

Planck intermediate results

We describe an extension of the most recent version of the Planck Catalogue of Compact Sources (PCCS2), produced using a new multi-band Bayesian Extraction and Estimation Package (BeeP). BeeP assumes that the compact sources present in PCCS2 at 857 GHz have a dust-like spectral energy distribution (SED), which leads to emission at both lower and higher frequencies, and adjusts the parameters of the source and its SED to fit the emission observed in Planck’s three highest frequency channels at 353, 545, and 857 GHz, as well as the IRIS map at 3000 GHz. In order to reduce confusion regarding diffuse cirrus emission, BeeP’s data model includes a description of the background emission surrounding each source, and it adjusts the confidence in the source parameter extraction based on the statistical properties of the spatial distribution of the background emission. BeeP produces the following three new sets of parameters for each source: (a) fits to a modified blackbody (MBB) thermal emission model of the source; (b) SED-independent source flux densities at each frequency considered; and (c) fits to an MBB model of the background in which the source is embedded. BeeP also calculates, for each source, a reliability parameter, which takes into account confusion due to the surrounding cirrus. This parameter can be used to extract sub-samples of high-frequency sources with statistically well-understood properties. We define a high-reliability subset (BeeP/base), containing 26 083 sources (54.1% of the total PCCS2 catalogue), the majority of which have no information on reliability in the PCCS2. We describe the characteristics of this specific high-quality subset of PCCS2 and its validation against other data sets, specifically for: the sub-sample of PCCS2 located in low-cirrus areas; the Planck Catalogue of Galactic Cold Clumps; the Herschel GAMA15-field catalogue; and the temperature- and spectral-index-reconstructed dust maps obtained with Planck’s Generalized Needlet Internal Linear Combination method. The results of the BeeP extension of PCCS2, which are made publicly available via the Planck Legacy Archive, will enable the study of the thermal properties of well-defined samples of compact Galactic and extragalactic dusty sources.

Identification of groundwater recharge sites in Latur district of Maharashtra in India based on remote sensing, GIS and multi‐criteria decision tools

AbstractIn this study, groundwater potential zones and artificial recharge zones were delineated and favourable sites for placing recharge structures were identified. Different thematic layers were generated using ArcGIS and were scrutinized by a multivariate technique called principal component analysis (PCA). Selected thematic layers and their features were assigned suitable weights on Saaty’s 9‐point scale and their normalized weights were obtained by Saaty’s analytical hierarchy process (AHP). Selected thematic layers were integrated by weighted linear combination method to calculate groundwater potential index (GWPI) and groundwater recharge index (GWRI). Based on calculated GWPI, the study area was delineated into five groundwater potential zones, namely “poor”, “moderate”, “good”, “very good” and “excellent”, which cover 1.61%, 71.99%, 22.77%, 3.54% and 0.09% of the study area, respectively. Also observed that, the study area has an area of 5957.89 km2 of moderate recharge zone. Further 34 artificial groundwater recharge sites are identified for effective groundwater management.