MMF Model Research Articles

“Estimating soil surface roughness by proximal sensing for soil erosion modeling implementation at field scale”

Soil Surface Roughness (SSR) is a physical feature of soil microtopography, which is strongly influenced by tillage practices and plays a key role in hydrological and soil erosion processes. Therefore, surface roughness indices are required when using models to estimate soil erosion rates, where tabular values or direct measurements can be used. Field measurements often imply out-of-date and time-consuming methods, such as the pin meter and the roller chain, providing inaccurate indices. A novel technique for SSR measurement has been adopted, employing an RGB-Depth camera to produce a small-scale Digital Elevation Model of the soil surface, in order to extrapolate roughness indices. Canopy cover coverage (CC) of the cover crop was also detected from the camera's images. The values obtained for SSR and CC indices were implemented in the MMF (Morgan-Morgan-Finney) model, to validate the reliability of the proposed methodology by comparing the models' results for sediment yields with long-term soil erosion measurements in sloping vineyards in NW Italy. The performance of the model in predicting soil losses was satisfactory to good for a vineyard plot with inter-rows managed with recurrent tillage, and it was improved using spatialized soil roughness input data with respect to a uniform value. Performance for plot with permanent ground cover was not so good, however it was also improved using spatialized data. The measured values were also useful to obtain C-factor for RUSLE application, to be used instead of tabular values.

Interpretation of uniaxial, confined compression test data for agricultural topsoils

Traditionally, soil strength is estimated from uniaxial, confined compression tests by procedures adopted from classical soil mechanics. The heterogeneity of agricultural topsoil calls for an alternative approach. Undisturbed soil cores were collected in the plough layer of 14 soils in arable agriculture. Soil texture ranged from coarse sandy to silty loam soils with a maximum of 20% clay. The samples were drained to either of six matric potentials in the range from − 30 to − 300 hPa. Uniaxial, confined compression was applied to ∼800 kPa with strain-controlled stress application (1 mm min−1). Measured strain was fitted to stress by the Morgan-Mercer-Flodin (MMF) model. The model fitted data remarkably well for all samples. Three fitting parameters of the model reflected physical characteristics of soil reaction to stress. The estimates of soil compressibility calculated from the model at 10 kPa (C10) correlated closely and linearly to the Cs index considered to reflect elastic deformation in classical studies of soil compression tests. Soil bulk density and content of soil organic matter decreased C10 as well as compressibility at 100 (C100) and 400 kPa (C400). A complex pattern in the effects of soil texture and soil moisture on compressibility was revealed. The pattern in strain-stress data is interpreted as a reflection of a gradual transition from elastic to plastic deformation of the mixture of structural units. The MMF model is suggested for interpretation of strain-stress data from uniaxial, confined compression tests. This implies use of stress in a linear scale.

Rancang Bangun dan Model Kinetika Pengeringan Jamur Tiram pada Pengering Surya dengan Tiga Kolektor

Oyster mushroom is a group of food plants that are rich in protein, vitamins and minerals. The weakness of this mushroom is its high moisture content so it rots quickly when stored in fresh conditions. Drying by using solar energy is one of the best options because of its sufficient quantity and low cost. This study proposes a solar dryer with three collectors of 500 mm x 500 mm x 1254 mm each with 4 rack. The drying experimental is using a sample of 1 kg. The best model of drying kinetics is the MMF model with an R value of 0.9950. The moisture extraction rate (MER) was 137 gr/hour under experimental conditions.

Soil erosion modelling of burned and mulched soils following a Mediterranean forest wildfire

AbstractSoil erosion modelling applied to burned forests in different global regions can be unreliable because of a lack of verification data. Here, we evaluated the following three erosion models: (1) Water Erosion Prediction Project (WEPP), (2) Morgan‐Morgan‐Finney (MMF) and (3) Universal Soil Loss Equation‐Modified (USLE‐M). Using field plots that were either untreated or mulched with straw, this study involved observations of soil loss at the event scale at a burned pine forest in Central Eastern Spain. The erosion predictions of the three models were analysed for goodness‐of‐fit. Optimization of the MMF model with a new procedure to estimate the C‐factor resulted in a satisfactory erosion prediction capacity in burned plots with or without the mulching treatment. The WEPP model underestimated erosion in the unburned areas and largely overestimated the soil loss in burned areas. The accuracy of soil loss estimation by the USLE‐M model was also poor. Calibration of the curve numbers and C‐factors did not improve the USLE‐M model estimation. Therefore, we conclude that an optimized MMF model was the most accurate way to estimate soil loss and recommend this approach for in Mediterranean burned forests with or without postfire mulching. This study gives land managers insight about the choice of the most suitable model for erosion predictions in burned forests.

Effect of surface coating with seeds mucilages and xanthan gum on oil uptake and physical properties of fried potato strips.

The oil uptake and high‐fat/oil content problem associated with fried food products can be decreased using polysaccharides (mucilages and gums) as edible coatings. In this study, the efficiency of seeds mucilages (Balangu, Basil, and Wild sage), and xanthan gum as edible coatings on the oil uptake, moisture retention, and physical properties (color indexes and surface shrinkage) of potato strips during oil frying have been examined. Coating treatment with seeds mucilages and xanthan gum significantly decreased the moisture loss and oil uptake of potato strips (p < .05). Lightness (L*) index values of uncoated‐control and coated potato strips decreased during frying. The lowest and highest color change intensity (∆E) and surface shrinkage (%) values were observed for the potato strips coated by Wild sage seeds gum and uncoated‐control sample, respectively. The MMF (Morgan‐Mercer‐Flodin) model was selected as the best equation for describing the color changes intensity kinetics of untreated and treated potato strips during frying. The appropriate coating suspension for reducing the oil content of fried potato strips and improving product appearance quality was 1.0% Wild sage seeds gum.

Effect of Basil Seed and Xanthan Gums Coating on Colour and Surface Change Kinetics of Peach Slices During Infrared Drying

Abstract The article presented conducts the research of infrared radiation power effect on the colour and surface changes of peach slices coated with basil seeds gum (BSG) and xanthan gum during drying. The colour indices include redness (a*), yellowness (b*), lightness (L*), and total colour difference (∆E), which were used for the purposes of colour change calculation. As the IR radiation power increased from 150 W to 375 W, the average values of L*, a* and b* of uncoated and coated peach slices decreased from 67.45 to 65.41; 7.95 to 5.89; and 49.21 to 38.52, respectively. The lowest ∆E and surface change values were observed in peach samples coated with BSG. The modelling results showed that the MMF model was the best model to describe the total colour difference of uncoated and coated peach slices (the average correlation coefficient was equal to 0.991 and the average standard error was equal to 1.791). The surface area change (%) of uncoated and coated peach slices increased with the progression of drying time, but the rate of changes was lower for the coated peach slices with BSG. The current research indicated that BSG coating has the potential to improve surface colour and appearance quality of dried peach slices.

Predicting the Digestive Tract Development and Growth Performance of Goat Kids Using Sigmoidal Models.

Simple SummaryGrowth is a biological phenomenon that usually happens with increased mass or volume over time. The transition from birth to postweaning has a fundamental impact on characteristics specific to the development and growth of the digestive tract. Consequently, the growth performance in goat kids is due to the combined effects of age, rumen development, and feeding system, which controls the growth patterns of each stage. Generally, the development of rumen is in stages. Therefore, modeling informative growth curves is of great importance for a better understanding of effective development patterns for optimizing feeding management systems for improved production efficiency. For the above reasons, this study was conducted to compare the sigmoid and polynomial models which were used to determine the age-related changes in body weight, body size indices, and digestive tract development; and to identify the most appropriate model. These empirical mathematical models of growth patterns are continuous functions of time and have biologically meaningful parameters that can be used to predict growth efficiently. In light of these models which lack sufficient information in literature, further research about different species of goats in different environments is still needed, thereby enabling us to distinguish more accurate results. The transition from monogastric to rumination stage is crucial in ruminants’ growth to avoid stressors—weaning and neonatal mortalities. Poor growth of the digestive tract could adversely affect the performance of the animal. Modeling informative growth curves is of great importance for a better understanding of the effective development pattern, in order to optimize feeding management system, and to achieve more production efficiency. However, little is known about the digestive tract growth curves. For this reason, one big goat farm of Laiwu black breed was chosen as a basis of this study. Forty-eight kids belonging to eight-time points (1, 7, 14, 28, 42, 56, 70, and 84 d; 6 kids for each) were selected and slaughtered. The body weight, body size indices, rumen pH, and stomach parts were determined and fitted to the polynomial and sigmoidal models. In terms of goodness of fit criteria, the Gompertz model was the best model for body weight, body oblique length, tube, and rumen weight. Moreover, the Logistic model was the best model for carcass weight, body height, and chest circumference. In addition, the Quadratic model showed the best fit for dressing percentage, omasum weight, abomasum weight, and rumen volume. Moreover, the cubic model best fitted the ruminal pH and reticulum percentage. The Weibull model was the best model for the reticulum weight and omasum percentage, while the MMF model was the best model describing the growth of chest depth, rumen percentage, and abomasum percentage. The model parameters, R squared, inflection points, area under curve varied among the different dependent variables. The Pearson correlation showed that the digestive tract development was more correlated with age than body weight, but the other variables were more correlated with body weight than age. The study demonstrated the use of empirical sigmoidal and polynomial models to predict growth rates of the digestive tract at relevant age efficiently.

Predictive Mathematical Modelling of the Total Number of COVID-19 Cases for Indonesia

In the current article, we showcase various growth models like Von Bertalanffy, Baranyi-Roberts, Morgan-Mercer-Flodin (MMF), modified Richards, modified Gompertz, modified Logistics and Huang in the fitting and analysis of the COVID-19 epidemic trend as of 15 July 2020 in Indonesia in the form of the total number of SARS-CoV-2 infections. The MMF model was proved as the suitable model with the highest adjusted R2 value and lowest RMSE value. The Accuracy and Bias Factors values were near to unity (1.0). The parameters obtained from the MMF model consist of maximum growth rate (µm) (log) of 0.025 (95% CI from 0.020 to 0.028), curve constant () that affects the inflection point of 0.770 (95% CI from 0.691 to 0.849), lower asymptote value () of 0.297 (95% CI from 0.229 to 0.365) and maximal total number of cases (ymax) of 4,634,469 (95% CI from 1,967,886 to 15,417,005). The MMF forecast that the total number of cases in Indonesia on the coming 15th of August and 15th of September 2020 will be 113,179 (95% CI of 103,477 to 123,790) and 154,235 (95% CI of 136,542 to 174,220), respectively. The predictive capability of the model applied in this paper is likely a reliable tool for epidemiologist to monitor and evaluate the severity of COVID-19 death cases in Indonesia in few months to come. Undoubtedly, the models will be reexamined after every few months in the event unwarranted phenomena lead to an exponential increase or wave of new infection.

Prediction of Cumulative Death Cases in Brazil Due to Covid-19 Using Mathematical Models

The novel corona virus (2019-nCoV) infection has spread rapidly to other provinces and neighbouring countries since the emergence of the first cases at Wuhan, China. Estimation of the death cases by mathematical modelling can help to determine the potential and severity of the outbreak and to provide critical information on the type and intensity of disease response. In this paper, we present different growth models such as Von Bertalanffy, Baranyi-Roberts, Morgan-Mercer-Flodin (MMF), modified Richards, modified Gompertz, modified Logistics and Huang in fitting and analyzing the epidemic trend of COVID-19 in the form of total number of death cases of SARS-CoV-2 in Brazil as of 15th of July 2020. The MMF model was found to be the best model with the highest adjusted R2 value with the lowest RMSE value. The Accuracy and Bias Factors values were close to unity (1.0). The parameters obtained from the MMF model include maximum growth of death rate (log) of 0.03 (95% CI from 0.02 to 0.028), curve constant (d) that affects the inflection point of 0.7057 (95% CI from 0.68 to 0.73) and maximal total number of death (ymax) of 17,619,760 (95% CI from 9,705,100 to 34,994,517). The MMF model predicted that the total number of death cases for Brazil on the coming 15th of August and 15th of September 2020 will be 132,787 (95% CI of 123,422 to 142,863) and 212,166 (95% CI of 192,578 to 233,746), respectively. The predictive ability of the model utilized in this study is a powerful tool for epidemiologist to monitor and assess the severity of COVID-19 in Brazil in months to come. However, as with any other model, these values need to be taken with caution due to the unpredictability of the COVID-19 situation locally and globally.

Prediction of Cumulative Death Cases in The United States Due to COVID-19 Using Mathematical Models

&#x0D; &#x0D; &#x0D; &#x0D; In this paper, we present different growth models such as Von Bertalanffy, Baranyi-Roberts, Morgan-Mercer-Flodin (MMF), modified Richards, modified Gompertz, modified Logistics and Huang in fitting and analyzing the epidemic trend of COVID-19 in the form of total number of death cases of SARS-COV-2 in The United States as of 20th of July 2020. The MMF model was found to be the best model with the highest adjusted R2 value with the lowest RMSE value. The accuracy and bias factors values were close to unity (1.0). The parameters obtained from the MMF model include maximum growth of death rate (log) of 0.048 (95% ci from 0.047 to 0.048), curve constant (d) that affects the inflection point of 2.34 (95% ci from 2.31 to 2.38) and maximal total number of death (ymax) of 151,356 (95% ci from 147,911 to 154,525). The MMF predicted that the total number of death cases for The United States on the coming 20th of August and 20th of September 2020 will be 148,183 (95% ci of 149,199 to 147,173) and 153,780 (95% ci of 152,640 to 154,928), respectively. The predictive ability of the model utilized in this study is a powerful tool for epidemiologist to monitor and assess the severity of COVID-19 in The United States in months to come. However, as with any other model, these values need to be taken with caution due to the unpredictability of the COVID-19 situation locally and globally.&#x0D; &#x0D; &#x0D; &#x0D;

Prediction of Cumulative Death Cases in Nigeria Due to COVID-19 Using Mathematical Models

In this paper, we present various growth models such as Von Bertalanffy, Baranyi-Roberts, Morgan-Mercer-Flodin (MMF), modified Richards, modified Gompertz, modified Logistics and Huang in fitting and evaluating the COVID-19 epidemic pattern as of 15 July 2020 in the form of the total number of SARS-CoV-2 deaths in Nigeria. The MMF model was found to be the best model having the highest adjusted R2 value and lowest RMSE value. The values for the Accuracy and Bias Factors were near unity (1.0). The parameters derived from the MMF model include maximum growth rate (log) of 0.02 (95% CI from 0.02 to 0.03), curve constant (d) that affects the infection point of 1.61 (95% CI from 1.42 to 1.79) and maximal total number of death cases (Ymax) of 1,717 (95% CI from 1,428 to 2,123). The model estimated that the total number of death cases for Nigeria on the coming 15th of August and 15th of September 2020 were 940 (95% CI of 847 to 1,043) and 1,101 (95% CI of 968 to 1,252), respectively. The predictive ability of the model employed in this study is a powerful tool for epidemiologist to monitor and assess the severity of COVID-19 in Nigeria in months to come. However, like any other model, these values need to be taken with caution because of the COVID-19 uncertainty situation locally and globally.

Effect of furfural concentration on ethanol production using Saccharomyces cerevisiae in an immobilized cells stirred‐tank bioreactor with glucose‐based medium and mathematical modeling

The aim of this study was to determine the toleration limit of furfural concentration (0.1–10 g/L) in the medium for ethanol fermentation using immobilized cells of Saccharomyces cerevisiae in the bioreactor with glucose-based media and to model the fermentation. Results showed that the highest ethanol yield and production were 47.62% and 36.40 g/L when 0.5 and 2 g/L of furfural were added in the medium, respectively. In contrast, the lowest yield and production were 5.40% and 0.57 in 10 g/L of furfural. Thus, the furfural has been found to adversely affect the ethanol yield even the yeast was immobilized. Nevertheless, the yeast can grow up to a furfural concentration of 6 g/L. Regarding modeling, the experimental ethanol production, sugar consumption, and biomass production data of the fermentation performed by adding 2 g/L of furfural were successfully predicted by MGM, MMF, and Weibull model. Practical applications In the pretreatment of renewable resources that can be used to produce ethanol, inhibitors are formed, which inhibit the microbial growth and the product formation. Detoxification methods are commonly used to remove the inhibitors. In this study, the immobilization technique was used to protect the yeast growth and thus product formation from inhibition. Yeast cells were covered with Ca-alginate by immobilization. Cells protected with alginate exposed less to inhibition. Protected cells remained more active in this way. Based on the results, this technique was successful since the ethanol was produced in the glucose-based medium including furfural. The results also showed that no detoxification was required and that the process saves time and energy.

Improvement of seasonal runoff and soil loss predictions by the MMF (Morgan-Morgan-Finney) model after wildfire and soil treatment in Mediterranean forest ecosystems

The negative hydrological effects of wildfire are very difficult to predict in Mediterranean forest ecosystems, due the intrinsic climate and soil characteristics of these areas. Among the hydrological models simulating surface runoff and soil erosion in these environmental contexts, the semi-empirical Morgan-Morgan-Finney (MMF) model can ensure the representation of the main physical processes, while offering ease of use and limiting the number of input parameters. However, literature reports very few modelling studies using MMF in burned areas of the Mediterranean environment with or without post-fire rehabilitation measures. To fill this gap, the capacity of the MMF model to predict the seasonal surface runoff and soil loss in a Mediterranean forest was verified and improved for unburned plots and areas affected by a wildfire, with and without post-fire straw mulch treatment. The application of MMF with default input parameters (set up according to the original guidelines of the model’s developers) led to poor performance. Conversely, after introducing some changes in input data for both the hydrological and erosive components (seasonal values of evapotranspiration, reduction of the soil hydrological depth, including soil water repellency effects in burned soils, and modelling erosive precipitation only), MMF was able to predict seasonal runoff volumes and soil loss with good reliability in all the experimented conditions.This modelling experiment has shown the capacity of the MMF model to simulate the seasonal hydrological and erosion response of the experimental unburned and burned soils of Mediterranean semi-arid forests. Although more research is needed to validate the model's prediction capacity in these conditions, the use of MMF as a management tool may be suggested to predict the hydrogeological risk in these delicate ecosystems threatened by wildfire, as well as to evaluate the potential efficiency of soil treatments after fire.

Sediment assessment for a watershed in arid region via neural networks

In the present study, the estimation of suspended sediment load is computed by four Artificial Neural Networks (ANNs) algorithms, Cascade Forward Back Propagation (CFBP), Feed Forward Back Propagation (FFBP), Radial Basis Function (RBF), and Recurrent Neural Network (RNN). Five cases of model input are calibrated to establish the relationship among precipitation, discharge and suspended sediment load. While discharge and rainfall up to four previous days as employed for input, model gives pre-eminent performance. Sensitivity of all models is appraised concerning Nash-Sutcliffe coefficient (ENS) and coefficient of determination (R2) for predicting sediment load. Among all ANNs, MMF (Morgan-Morgan-Finney) model when trained with stream flow as the input in RNN, gives best result with coefficient of determination, R2 as 0.9474, while the values for FFBP, CFBP and RBF are 0.9115, 0.8766 and 0.8511, respectively. Performance of all results show that an MMF model is superior to conventional SRC (Sediment Rating Curve) and MLR (Multiple Linear Regression) models in determining the complex relationship between discharge and suspended sediment load.

Evaluation of Soil Erosion Status of Flatland Micro-Watershed under Semi-Arid Conditions using MMF Model

Evaluation of Soil Erosion Status of Flatland Micro-Watershed under Semi-Arid Conditions using MMF Model

Mathematical modeling of lactic acid fermentation in bioreactor with carob extract

Abstract In this study, nonlinear sixteen mathematical models including Gompertz (G), generalized Gompertz (GG), modified Gompertz (MG), re-modified Gompertz (RMG), logistic (L), generalized logistic (GL), modified logistic (ML), re-modified logistic (RML), Richards (R), generalized Richards (GR), modified Richards (MR), re-modified Richards (RMR), Stannard (S), Baranyi (B), Weibull (W), and Morgan-Mercer-Flodin (MMF) were applied to fit cell growth, product formation, and sugar consumption of batch lactic acid (LA) fermentation in stirred tank bioreactor with carob extract. To determine the modeling success, root-mean-square-error (RMSE), mean-absolute-error (MAE), R2, slope, bias factor (BF), and accuracy factor (AF) were used. Results indicated that the best model for cell growth was MMF model (RMSE=0.24 g/L, MAE=0.16 g/L, R2=1.00, Slope=1.06, BF=1.04 and AF=1.16). For product formation, the best models selected were RMG (RMSE=1.33 g/L, MAE=1.00 g/L, R2=0.99, Slope=0.95, BF=0.94 and AF=1.11) and RMR (RMSE=1.33 g/L, MAE=1.01 g/L, R2=0.99, Slope=0.96, BF=0.94 and AF=1.12) models. As for sugar consumption, B model was the best model for estimation of experimental data (RMSE=0.88 g/L, MAE=0.52 g/L, R2=1.00, Slope=1.00, BF=1.00 and AF=1.01). Additionally, the most successful models that predict experimental kinetic data were GG, GL, S, and W models. Consequently, the best models selected could be used for more progress of LA production process in bioreactor with carob extract.

Modelling erosion on a daily basis, an adaptation of the MMF approach

Effect of soil erosion causing negative impact on ecosystem services and food security is well known. On the other hand there can be yearly variation of total precipitation received in an area, with the presence of extreme rains. To assess annual erosion rates various empirical models have been extensively used in all the climatic regions. While these models are simple to operate and do not require lot of input data, the effect of extreme rain is not taken into account. Although physically based models are available to simulate erosion processes including particle detachment, transportation and deposition of sediments during a storm they are not applicable for assessing annual soil loss rates. Moreover storm event data may not be available everywhere prohibiting their extensive use. In this paper we describe a method by adapting the revised MMF model to assess erosion on daily basis so that the effects of extreme rains are taken into account. We couple it to a simple surface soil moisture balance and include estimation of daily vegetation cover changes for calculating rain interception and for estimating effective rain. The runoff fraction is based on the available daily storage of the effective hydrological depth. Unlike the original MMF model which accumulates annual runoff, the daily model accumulates daily runoff from upstream contributing area in a predefined flow network according to steepest slope direction. Annual soil loss is calculated by adding daily erosion rates. We compare the obtained results with that obtained from applying the revised MMF model in two locations: (i) in sub-humid tropics in central Thailand which is affected by deforestation and land cover changes resulting in excessive soil losses, and (ii) in semi-arid environment in Morocco which is affected by severe gully formation. Since the model results are daily estimates it is possible to see the effects of exceptional rain. In Morocco, the effects of extreme rains are clearly shown which were absent in the results obtained by using the annual model. The results also show that erosion rates can be moderate when rainfall pattern is normal without extreme rains in a year although total rain may be similar. This is clearly shown in the erosion assessment in Thailand for the years 2005 (1390 mm) and 2006 (1366 mm, and with the presence of extreme rainy days).

A Novel MMF Distribution Model for 3-D Analysis of Linear Induction Motor With Asymmetric Cap-Secondary for Metro

This paper develops a novel magnetomotive forces (mmfs) distribution model along the transverse direction for the 3-D analysis of linear induction motor, and it investigates the influence of a laterally asymmetric cap-secondary on the thrust, vertical force, lateral force, and efficiency in a prototype single-sided linear induction motor for linear metro. First, the mmf distribution model and its working conditions are defined, and the model is transferred into a mathematical model. Second, the air-gap flux and secondary eddy current in a prototype linear motor with a cap-secondary are obtained by a 3-D space harmonic analytical method. Third, the thrust, vertical force, lateral force, and efficiency of the test motor are analyzed with various lateral deflection, and the power factor in secondary is also presented. Finally, these results are experimentally verified by measurements on the test rig of the linear motor, and the comprehensive effect on the operating performance can be accurately calculated by the mmf model.

Numerical Modeling of Flow over an Ogee Crested Spillway under Radial Gate: VOF and MMF Model

The present research work shows the simulation of the gated flow over an ogee crested spillway for one of water reservoir. The average velocities and Froude Number analysis at various gate openings gives a better insight of flow behavior. Also, the simulations were carried out by changing the gate bottom shape. The STAR CCM+ CFD tool is used to solve the fluid flow performance. The flow parameters near the bottom of the gate have been studied with two types of fluid flow models i.e. Volume of Fluid (VOF) and Multi-Mixture Fluid Models. The use of Volume of Fluid (VOF) multiphase model together with RNG k-e turbulence for the simulation, gives the excellent agreement between the experimental and numerical data. The spillway performance of the gated flow at various gate openings resembles with the actual flow behavior. The applicability of the CFD model to simulate the gated flow over ogee crested spillway is reviewed. The computational model study showed that CFD can be useful in hydraulics structures for designing of various reservoirs. This numerical model gives significant advantage in practice, in terms of parametric studies

Remote sensing and GIS based assessment of soil erosion and soil loss risk around hill top surface mines situated in Saranda Forest, Jharkhand

The quantitative analysis of soil erosion changes over 7 years due to mining operations in two neighboring hilltops in West-Singhbhum District, Jharkhand, are reported. CartoSat-1, ETM+ and LISS-IV satellites' data provided spatial inputs in Universal Soil Loss Equation (USLE) and Morgan, Morgan and Finney method (MMF) models, which were used to predict the average annual soil erosion during the period of 2001–2008 in a geographic information system (GIS), in six distinct classes. In the comparative analysis of the 7-year period, the MMF model revealed a lower coefficient of variation 0.71 (2001) and 0.84 (2008) in predicted average annual soil loss, which increased by 16% (81.3–94.2 t ha−1yr−1), whereas in the case of USLE, the coefficients of variation were 3.88 (2001) and 1.94 (2008), with an increase of 61% (48.56–78.38 t ha−1yr−1). The correlation coefficient of these models was 0.1 (2001) and 0.36 (2008), which shows that both models predicted significantly differently as a result of the different factors considered. Overall, the MMF model predicted a higher soil erosion rate but less variation than USLE. Both models showed soil erosion rates were drastically increased by anthropogenic activities in the area, hence careful consideration is needed. The same sensor and imaging data could not be maintained. Correction of errors may reduce erosion, but it will still remain significant for future planning.