Power Regression Model Research Articles

Estimation of cladode area of Nopalea cochenillifera using digital images

Determination of photosynthetic area of a plant, leaf or cladode is a fundamental tool in study of transpiration intensity, specific leaf area and leaf area index. The objective of this study was to evaluate Nopalea cochenillifera (L.) Salm-Dyck). cladode area, in a non- destructive way, using digital images and test its relation with the variables: product of length and maximum width and real cladode area through regression models. The design used randomized blocks with three replicates and using the N. cochenillifera forage cactus clone, Giant Sweet. To determine the real cladode area of cactus forage, 432 cladodes in different stages of growth were randomly collected (162 primary cladodes, 127 secondary and 143 tertiary cladodes), all free from damage, disease or pest attacks. All cladodes were photographed with a digital camera (Sony Mark, model DSC-P72) generating a sample of 432 1200 x 2500 pixel digital images of N. cochenillifera cladodes. Linear, gamma and power regression models were adjusted to test the relation between the digital cladode area and the explanatory variables real cladode area and product of length by width. Models were evaluated with the following criteria: Coefficient of model determination, Akaike information criterion, sum of squares of residuals and Willmott index. The power model gave the best performance, with explanatory power higher than 99.5%, while the Willmott index exceeded 0.99. Sum of squares of residuals and Akaike information criterion had lower values. The digital cladode area of N. cochenillifera can be explained by the linear dimensions of cladodes in, and independent of, branching order. The digital cladode area (DCA) of N. cochenillifera can be explained as a function of the power model -DCA = LW0.98Sconsidering the product of length by width (LW) with explanatory variable, and by D-CA = RCA1.002considering real cladode area (RCA) with explanatory variable.

Non-destructive method for estimating leaf area of Erythroxylum pauferrense (Erythroxylaceae) from linear dimensions of leaf blades

Background and Aims: Determining the leaf area is essential for studies on growth, propagation, and ecophysiology of forest species. Developing quick, practical, and accurate methods is needed to estimate leaf area without destroying leaves. Therefore, this research aimed to obtain an equation from regression models that meaningfully estimate the leaf area of Erythroxylum pauferrense using linear dimensions of its leaf blades.Methods: For this purpose, 1200 leaves were randomly collected from different plants in the Mata do Pau-Ferro, a state park located in Areia city, Paraíba state, Brazil. Equations were fitted from simple linear, linear without intercept, quadratic, cubic, power, and exponential regression models. Next, the best equation was selected by checking the following assumptions: higher determination coefficient (R²) and Willmott's index (d), lower Akaike information criterion (AIC) and root mean square error (RMSE), as well as the BIAS index closest to zero.Key results: Based on the criteria used, all equations fitted using the product of length by width (L.W) can estimate the leaf area of E. pauferrense.Conclusions: The equation ŷ=0.6740*LW from the linear model without intercept significantly estimates the leaf area of E. pauferrense in a quick and practical way (R²=0.9960; d=0.9953; AIC=1231.61; RMSE=0.4255; BIAS=-0.0130).

Early network properties of the COVID-19 pandemic – The Chinese scenario

ObjectivesTo control epidemics, sites more affected by mortality should be identified. MethodsDefining epidemic nodes as areas that included both most fatalities per time unit and connections, such as highways, geo-temporal Chinese data on the COVID-19 epidemic were investigated with linear, logarithmic, power, growth, exponential, and logistic regression models. A z-test compared the slopes observed. ResultsTwenty provinces suspected to act as epidemic nodes were empirically investigated. Five provinces displayed synchronicity, long-distance connections, directionality and assortativity – network properties that helped discriminate epidemic nodes. The rank I node included most fatalities and was activated first. Fewer deaths were reported, later, by rank II and III nodes, while the data from rank I–III nodes exhibited slopes, the data from the remaining provinces did not. The power curve was the best fitting model for all slopes. Because all pairs (rank I vs. rank II, rank I vs. rank III, and rank II vs. rank III) of epidemic nodes differed statistically, rank I–III epidemic nodes were geo-temporally and statistically distinguishable. ConclusionsThe geo-temporal progression of epidemics seems to be highly structured. Epidemic network properties can distinguish regions that differ in mortality. This real-time geo-referenced analysis can inform both decision-makers and clinicians.

An empirical equation to determine the threshold for rainfall-induced landslides developing to debris flows

This study derives an empirical equation to determine the rainfall threshold that triggers landslides and subsequent debris flows. In this research, the contribution of different factors, especially various sediment layer thicknesses, was assessed on the landslide leading to debris flows. Bed slope, sediment thickness, sediment mean diameter, sediment layer length, rainfall intensity, and time of landslide occurrence were selected as the effective factors. Rainfall simulator device was used to create rainfall on the sediment sample. The effect of these factors on the landslide which leads to debris flow occurrence was investigated using dimensional analysis of experimental data. Then, linear and power regression models were tested by 30% of the total experimental data to estimate the rainfall-induced landslide. Determination coefficient (R2), mean absolute relative error (MARE), and Akaike information criterion (AIC) were applied to determine the best equation. Results revealed that landslide often occurs at a rapid rate in the thicker deposits than the thinner one. The evaluation of linear and power equations demonstrated that two-variable power model was the best empirical equation for estimation of rainfall threshold in initiation of landslide and debris flows. The evaluation criteria including MARE, R2, and AIC were estimated 0.044, 0.984, and − 25.93, respectively. In this equation, bed slope and the ratio of the time of landslide occurrence to the sediment layer length were used as the effective factors. This study can be used for assessment of the threshold of rainfall-induced landslide which finally leads to debris flow hazard.

Compositional kernel learning using tree-based genetic programming for Gaussian process regression

Although Gaussian process regression (GPR) is a powerful Bayesian nonparametric regression model for engineering problems, its predictive performance is highly dependent on a kernel for covariance function of GPR. However, choosing a proper kernel is still challenging even for experts. To choose proper kernel automatically, this study proposes a compositional kernel (CPK) learning using tree-based genetic programming (GEP). The optimal structure of the kernel is defined as a compositional representation based on sums and products of eight base-kernels. The CPK can be encoded as a tree-structure, so that tree-based GEP is employed to discover an optimal tree-structure of the CPK. To avoid overly complex solution in GEP, the proposed method introduced a dynamic maximum tree-depth technique. The novelty of the proposed method is to utilize more flexible and efficient learning capability to learn the relationship between input and output than existing methods. To evaluate the learning capability of the proposed method, seven test functions were firstly investigated with various noise levels, and its predictive accuracy was compared with existing methods. Reliability problems in both parallel and series systems were introduced to evaluate the performance of the proposed method for efficient reliability assessment. The results show that the proposed method generally outperforms or performs similarly to the best one among existing methods. In addition, it is also shown that proper kernel function can significantly improve the performance of GPR as the training data increases. Stated differently, the proposed method can learn the function of being fitted efficiently with less training samples than existing methods. In this context, the proposed method can make powerful and automatic predictive modeling based on GPR in engineering problems.

A novel cross-sensor calibration method to generate a consistent night-time lights time series dataset

ABSTRACT Night-time lights (NTLs) collected from the Defense Meteorological Satellite Program‘s Operational Linescan System (DMSP-OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) of the Suomi National Polar Partnership satellite have been widely used in multiple disciplines. However, the defects of DMSP and VIIRS data itself, and the inconsistency between them, hinder their applications in long-term finer studies. Despite some effective efforts, existing relevant researches are still limited by the shortcomings of data inaccessibility, data deficiency neglection, and spatial resolution degradation. To resolve these issues, a novel cross-sensor calibration method was developed in this article by considering three Chinese metropolises (Beijing, Shanghai, and Guangzhou) as the study area. First, the original DMSP NTL images for 2000–2013 were calibrated through stepwise calibration, background noise removal and vegetation adjustment. Second, stable VIIRS annual composites for 2012–2019 were produced after seasonal noise removal, yearly aggregation, background noise removal, vegetation adjustment, and outliers correction. Third, a power regression model was applied to align pixel values of the processed DMSP and the processed VIIRS data for the overlapped years, and consistent NTLs for 2000–2019 were further generated using the regression results. The evaluations based on statistical coefficients, spatial patterns, profile curves, dynamic changes, and correlations with socioeconomic statistics, indicated the robustness and effectiveness of the proposed approach in filling the gaps between DMSP and VIIRS data. The consistent, continuous, and stable NTL time series could serve as input data for further applications, such as urban dynamics capture, economic growth estimation, and population distribution mapping.

Application of landsat 8 sattelite imagery for estimated distribution of above ground carbon in Percut Sei Tuan forest landscape

The use of remote sensing technology has been widely used to estimate carbon stocks above ground level, including in mangrove forest landscapes. This study aims to obtain information on the spatial distribution of above-ground carbon using Landsat 8 satellite imagery in the Percut Sei Tuan mangrove forest landscape. Ordinary least square (OLS) regression method is used to build a model of estimating the spatial distribution of above-ground carbon based on vegetation index values of normalize different vegetation index (NDVI) and green normalize different vegetation index (GNDVI). The results showed the average carbon content above ground level in the Percut Sei Tuan forest landscape was 23.55 tons ha−1. The power regression model is the best estimator model for the distribution of carbon content above ground level with the equation y = 205.9 x4.2713 and R2 of 72.4%.

Risk Management Tools for Sustainable Agriculture: A Model for Calculating the Average Price for the Season in Revenue Insurance for Citrus Fruit

Risk management in agriculture is at the heart of major reforms in many OECD countries and European agricultural policies. Price risks, which are generally not insurable per se, have been covered by the Common Agricultural Policy (CAP), which has been shaped as a system of protection against market shocks and an instrument for income stabilization. However, there is an increasing propensity to combine the use of public and private risk management tools as well. In Spain, revenue insurance has not yet developed in the same way as other risk coverage insurance, although it is an upcoming target of agricultural insurance policies with the aim of ensuring income stability for agricultural producers. This paper presents the results of the methodology used to draw up a composition index or model of the average price for the season or representative market field price to be used for revenue insurance purposes in citrus fruit. High explanatory power regression models and the analytic hierarchy process (AHP) were used. The results show that the average price for the season obtained reliably represents the market field prices in the country’s various producer areas.

Estimation of Parameters of Two Non-linear Regression Models Using Assumed Values: Reciprocal Power Regression Models

Fitting nonlinear models is not a single-step procedure but it involved a process that requires careful examination of each individual step. Depending on the objective and the application domain, different priorities are set when fitting nonlinear models; these include obtaining acceptable parameter estimates and a good model fit while meeting standard assumptions of statistical models. We propose steps in fitting nonlinear models in this research work. Two reciprocal power regression models were considered with a non-linear data set. Then, the following steps are considered (i) fit the models to the data collected using iterative steps, (ii) to develop a linear model to estimate the parameter β1 and β2 when the initial value (or growth rate β3) lies between -1.0 ≤ β3 ≤1.0 ); using the transform models of the reciprocal power regression model (iii) to find the “best” model between the two models using R2, AIC and BIC. The results show Model B is better than Model A, using the model selection criteria.

Leaf blade area estimate of Digitaria pentzii under different cutting Heights

Abstract Pasture studies require information on leaf area, as it is one of the main parameters for evaluation of plant growth. Thus, the objective of this study was to estimate the leaf blade area of pangolão grass (Digitaria pentzii Stent.) using non-destructive methods by regression model analysis. The experimental design consisted of randomized blocks, with three cutting heights (10, 15, and 20 cm) and four replications. Three hundred leaf blades of pangolão grass were randomly collected, and their respective lengths (L) and widths (W) determined using a digital caliper. The leaf blade area of pangolão grass was estimated by the gravimetric method, using linear and power regression models to explain the leaf blade area as a function of the product of L and maximum W. The real leaf blade area presented an average value of 18.64 cm2, ranging from 4.29 to 45.95 cm2. The leaf blade area of pangolão grass, regardless of cutting height, was estimated with greater accuracy by the power model. The power model, Ŷ=LW1.007, can be used to estimate the leaf blade area of pangolão grass based on leaf blade L and W values.

Factors affecting life expectancy in Kazakhstan

Relevance. Life expectancy is a comprehensive indicator reflecting the quality of life in a country or region, which is why it is important to estimate the impact of various socio-economic factors on this indicator as accurately as possible. Our study makes a novel contribution to the existing research by conducting a correlation and regression analysis of factors affecting life expectancy in regions of Kazakhstan based on panel data. Research objective. This paper aims to present a modified methodology for estimation of factors affecting life expectancy in regions of Kazakhstan. Data and methods. Our research relies on panel data on regions and cities of Kazakhstan. The data are provided by the Ministry of National Economy and the Ministry of Health Care of the Republic of Kazakhstan. Methodologically, the research is based on regression and correlation analysis. The two main criteria were applied for data selection: availability of statistical data for a sufficiently long period and the potential impact of factors on life expectancy. We built a two-factor power regression model calculated with the help of software package Microsoft Excel. Results. In our research, regression models were used to formulate conclusions concerning the impact of certain socio-economic factors on life expectancy in regions of Kazakhstan. We also brought to light the factors whose relationship to life expectancy requires further investigation. Conclusions. It was found that the most significant factors affecting life expectancy in regions of Kazakhstan are economic ones. The proposed methodology can be used for short- and medium-term predictions of life expectancy.

Comparison between partial least square and support vector regression with a genetic algorithm wavelength selection method for the simultaneous determination of some oxygenate compounds in gasoline by FTIR spectroscopy

In the current research, FTIR spectroscopy (Mid, 600–4000 cm−1) coupled with a multivariate calibration method has been suggested as a powerful regression model for the simultaneous determination of oxygenate in gasoline. To reach that goal, partial least squares regression (PLS-R) combined with genetic algorithm wavelength selection method (GA) was compared with the GA- support vector regression (GA-SVR) method. In order to evaluate the models, root mean square error of prediction, and leave-one-out cross-validation root mean square error, as well as the correlation coefficient between the calculated (Rcal2) and predicted values (Rpred2), were applied. Based on the findings in this work, GA-SVR model is the superior predictive factor of the two, having a higherRpred2 (0.971, 0.950, 0.955, 0.960, 0.970, and 0.969) and a lower root mean square error of prediction values (RMSEP = 0.185, 0.245, 0.218, 0.229, 0.218, and 0.227) respectively for methyl t-butyl ether (MTBE), iso-butanol, n-butanol, propanol, ethanol, and methanol in comparison to PLS (Rpred2 = 0.951, 0.940, 0.938, 0.940, 0.952, and 0.949; RMSEP = 0.32, 0.283, 0.303, 0.299, 0.300, and 0.311). The lowest detection limit was 0.06% w/w for GA-SVR and 0.2% w/w for GA-PLS model. Also, in a concentration range from 0.06 to 3.5% w/w the values were in accordance to gas chromatography analysis of oxygenates compound. Hence, together with GA-SVR, FTIR can be an efficient, real-time approach towards a feasible quantitative analysis of oxygenate compounds in gasoline.

Statistical relationship between land surface altitude and soil salinity in the enclosed desert depressions of arid regions

Soil salinity is one of the most limiting factors in land evaluation and management for agricultural use as it impacts directly on crop cultivation and production. The laboratory soil salinity analysis (electrical conductivity, EC) is an essential procedure to determine the suitability of soils for different uses. The main goal of this research is to quantify the statistical relationship between surface altitude (based on digital elevation model, DEM) and soil salinity (based on EC) in a desert depression in the arid region. A total of 40 surface soil samples collected from different altitudes in Wadi El Farigh depression, western desert, Egypt. The co-kriging geostatistical model was applied to interpolate the laboratory-measured EC values as the primary variable and the elevation as a covariate. Based on the altitude variations, the dataset classified into two groups; soil samples within the altitude of ≤20 m and > 20 m. Both linear and logistic (power) regression models were employed to predict the EC values based on the elevation of the study area using all dataset and the two classified groups. The results show a strong correlation between EC values and surface elevation. The linear regression models produced R2s of 0.67, 0.86, and 0.74 using all dataset, ≤ 20 and > 20 m, respectively. Stronger relationships with respective R2s of 0.94, 0.86, and 0.84 achieved when power model was applied. No significant differences observed between the models’ results and validation datasets. Generally, the regression analysis shows a promising technique for predicting the soil salinity based on surface altitude in the enclosed depressions of the arid region.

Improved polygenic prediction by Bayesian multiple regression on summary statistics

Accurate prediction of an individual’s phenotype from their DNA sequence is one of the great promises of genomics and precision medicine. We extend a powerful individual-level data Bayesian multiple regression model (BayesR) to one that utilises summary statistics from genome-wide association studies (GWAS), SBayesR. In simulation and cross-validation using 12 real traits and 1.1 million variants on 350,000 individuals from the UK Biobank, SBayesR improves prediction accuracy relative to commonly used state-of-the-art summary statistics methods at a fraction of the computational resources. Furthermore, using summary statistics for variants from the largest GWAS meta-analysis (n ≈ 700, 000) on height and BMI, we show that on average across traits and two independent data sets that SBayesR improves prediction R2 by 5.2% relative to LDpred and by 26.5% relative to clumping and p value thresholding.

Estimation of lacunarity using gamma regression model

In this study we evaluate estimated lacunarity in three different databases using the power (standard) and gamma model. Results showed that estimates of lacunarity using gamma regression model was superior to those with the power regression model. The gamma regression model had a higher coefficient of model determination than the power regression model for all three used databases and, additionally, had smaller sums of residuals squared. The Gamma model was chosen the most appropriate model for lacunarity estimates.

Determining process of occasional flooding from channel hydrological characteristics of Chel Basin, North Bengal (India)

Occasional flooding has a distinct impact on fluvial dynamics of a channel. It brings new thresholds of adjustment for hydraulic conditions of a channel. The task of estimation of peak discharge during occasional floods is very complex, as the water recedes very quickly. The flood and rainfall design techniques (Gumbel’s frequency distribution) are the relevant solutions for peak discharge estimation at an un-gauged station of measurement. This paper aims to estimate the volume of peak discharge during the phase of occasional floods at ‘t’ recurrence intervals (5, 10, 20, 50 and 100 years) which are 148.87221, 158.58408, 167.9592, 179.9475, and 218.8758 cumecs. The separate yield of peak runoffs from small 3rd-order basins has been estimated by rational method on 2 years (162.70 cumecs) and 10 years (212.60 cumecs) probabilities of recurrence. While deriving probable peak flood discharge, various temporal hydrological relationships regarding rainfall–runoff and stage–discharge have been established by curve-fitting method in linear and power regression models. The temporal study (2015–2017) reveals some interesting hydrological results, including (a) 1284.21–1522.22 Ha m day–1 channel runoff causes discharge greater than 150 cumecs, (b) effect of basin lag influences the runoff curve and (c) the water stage above 2.5 m gauge height indicates occasional floods. The rainfall as system input indicates high spatio-temporal variation ranges, between 200 and 5500 mm, as maximum annual average. This approach fundamentally assembles the rainfall–runoff–discharge trio relation in estimating the design for peak occasional flood discharge for Chel Basin.

Reproductive biology of the southern pufferfish, Sphoeroides nephelus (Actinopterygii: Tetraodontiformes: Tetraodontidae), in the northern coast off the Yucatan Peninsula, Mexico

Background. Overexploitation of fishery resources has led to the capture of alternative species of a lower trophic level, considered previously unprofitable or unfit for human consumption. The southern pufferfish, Sphoeroides nephelus (Goode et Bean, 1882), is a bycatch species of the recreational fishery in the USA and Mexico. Unlike other species of the genus Sphoeroides, there is no background on their reproductive cycle. Therefore, this study aimed to describe several reproductive traits (sex ratio, gonadal development, annual reproductive cycle, and fecundity) of specimens from the northern coast of the Yucatan Peninsula, Mexico. This kind of information might serve as a point of reference for its potential use either in the pharmaceutical industry, aquarium trade, as well as in aquaculture. Materials and methods. The reproductive biology of the southern pufferfish was described based on 189 females and 223 males, caught off the northern coast off the Yucatan, Mexico, during an annual cycle. Several reproductive traits, such as the annual cycle of gonad development, sex ratio, and the batch fecundity were determined. Results. The sex ratio showed no significant difference from 1 ÷ 1 ratio. Male’s reproductive season lasted from August to May, although females were spawning from August to April. The size at 50% maturity was estimated at 14.93 cm TL for females. Batch fecundity ranged between 59 087 and 367 022 oocytes (mean 176 456) per spawning batch, and it was significantly fitted to both female size and weight based on power and linear regression models, respectively. The relative batch fecundity was estimated at 828 ± 182 oocytes per gram of eviscerated weight, and 11 989 ± 4774 oocytes per gram of gonad weight. Conclusion. It was determined that S. nephelus exhibited 1 ÷ 1 sex ratio, an asynchronous development of oocytes and indeterminate fecundity with batch spawning. The species has a protracted reproductive season that lasted almost all year round with peaks in February–March (before of the dry season) and a brief inactive period in June–July (before of the wet season). This latter information should be helpful to obtain a broodstock that would be the base to produce fingerlings to be raised, instead of collecting individuals from the wild.

Relationship between Verticillium dahliae inoculum and sunflower wilt in Argentina

Leaf mottle and wilt (LM) of sunflower caused by Verticillium dahliae is the most important disease in Argentina. LM is a monocyclic disease where microsclerotia in the soil are the primary inoculum source and have an important impact on symptom expression (incidence and severity) and yield. The inoculum (microsclerotia/g of soil) and disease relationship is affected by several factors, such as temperature and soil moisture. The objective of this work was to determine the regression model that best fits the relationship between V. dahliae microsclerotia in soil and sunflower LM, over a wide environmental range. Sunflower plants were exposed to different microsclerotia densities in pasteurized and non-pasteurized soils in pots under greenhouse and field conditions. The microsclerotia in soil and LM relationship regression models were also tested in the field with natural soil borne microsclerotia infestation, in different sunflower cultivars and locations. After the flowering stage (± 8 days), disease severity was affected by inoculum density at every evaluation time (P-value≤0.0009). A 45% increase in the area under the disease progress curve was shown by soil pasteurization. Power regression model showed the highest concordance and Pearson correlation coefficients, parameter significance and no difference with the lowest Akaike index criterion value among all tested models in all experiments. This study provides a model (LM = 11.6*inoculum density^0.57) which describes the inoculum density and LM relationship. The use of this model might help farmers to predict an LM risk before sowing and to choose the appropriate field and the resistance level of the sunflower hybrid to be used.

Regression Analysis to Estimate Morphology Parameters of Pepper Plant in a Controlled Greenhouse System

Regression analysis to predict growth indices of plant is essential for understanding the relationship between the total leaf area, production of fresh weight and dry matter, and expansion of the plant growth. An experiment was conducted to develop regression models for estimating leaf area, fresh weight, and dry weight from measurements of plant height at the vegetative phase of hot pepper (Capsicum annuum Linnaeus) grown in biodegradable pots in a greenhouse. Five models were evaluated and compared: linear regression model, two-order polynomial regression model (P. order 2), three-order polynomial regression model (P. order 3), four-order polynomial regression model (P. order 4), and power regression model. The models were compared using the coefficient of determination (R2), Pearson’s correlation coefficient (r), root mean square error (RMSE), relative standard error (RSE), and mean absolute percentage error (MAPE). Power regression involving plant height demonstrated the highest R-square among the other models with minimum error estimate for the expected leaf area (R2 > 0.96, r > 0.98, RMSE 0.98, r > 0.99, RMSE 0.97, r > 0.98, RMSE < 0.03, RSE < 0.02, and MAPE < 11.7) of the plant considering both the fit and degree of adjustment, and the interpretation of the model. This study creates scope for further experimentation on various species of crops by changing management practices under different environmental conditions to enhance knowledge and understanding of the growing patterns of plants.

Rainfall erosivity and erosivity density in Eastern Ghats Highland of east India

The rainfall erosivity (R-factor in USLE) is the long-term average of the sum of the product of rainfall kinetic energy and its maximum 30-min intensity. Therefore, at most 30-min time intervals pluviograph records are required to calculate R-factor. But, such high-resolution data are scarce in many parts of the world and require lengthy processing period. In this study, R-factor was correlated with daily, monthly and annual rainfall, and its spatial variability in Eastern Ghats Highland of east India was mapped. The result showed that power regression models predicted satisfactorily the daily, monthly and annual R-factor, of which annual R-factor model performed best (model efficiency 0.93). Mean monsoon season R-factor was 15.6 and 10.0 times higher than the pre- and post-monsoon season R-factor, and thus remained highly critical with respect to erosion. Annual R-factor values ranged from 3040 to 10,127 MJ mm ha−1 h−1 year−1, with standard deviation of 1981 MJ mm ha−1 h−1 year−1. Rainfall intensity was positively correlated with erosivity density, and numerical value of rainfall intensity was almost double of the erosivity density value. The combination of rainfall and erosivity density was used to identify flood, erosion and landslide-prone areas. The developed iso-erosivity, erosivity density and risk maps can be opted as a tool for policy makers to take suitable measures against natural hazards in Eastern Ghats Highland of east India and elsewhere with similar rainfall characteristics.