| 1. |
- Abba, S. I., et al.
(författare)
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Effluents quality prediction by using nonlinear dynamic block-oriented models : A system identification approach
- 2021
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Ingår i: Desalination and Water Treatment. - : Desalination Publications. - 1944-3994 .- 1944-3986. ; 218, s. 52-62
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Tidskriftsartikel (refereegranskat)abstract
- The dynamic and complex municipal wastewater treatment plant (MWWTP) process should be handled efficiently to safeguard the excellent quality of effluents characteristics. Most of the available mathematical models do not efficiently capture the MWWTP process, in such cases, the data-driven models are reliable and indispensable for effective modeling of effluents characteristics. In the present research, two nonlinear system identification (NSI) models namely; Hammerstein-Wiener model (HW) and nonlinear autoregressive with exogenous (NARX) neural network model, and a classical autoregressive (AR) model were proposed to predict the characteristics of the effluent of total suspended solids (TSSeff) and pHeff from Nicosia MWWTP in Cyprus. In order to attain the optimal models, two different combinations of input variables were cast through auto-correla-tion function and partial auto-correlation analysis. The prediction accuracy was evaluated using three statistical indicators the determination coefficient (DC), root mean square error (RMSE) and correlation coefficient (CC). The results of the appraisal indicated that the HW model outperformed NARX and AR models in predicting the pHeff, while the NARX model performed better than the HW and AR models for TSSeff prediction. It was evident that the accuracy of the HW increased averagely up to 18% with regards to the NARX model for pHeff . Likewise, the TSSeff performance increased averagely up to 25% with regards to the HW model. Also, in the validation phase, the HW model yielded DC, RMSE, and CC of 0.7355, 0.1071, and 0.8578 for pHeff, while the NARX model yielded 0.9804, 0.0049 and 0.9902 for TSSeff, respectively. For comparison with the traditional AR, the results showed that both HW and NARX models outperformed in (TSSeff) and pHeff prediction at the study location. Hence, the outcomes determined that the NSI model (i.e., HW and NARX) are reliable and resilient modeling tools that could be adopted for pHeff and TSSeff prediction.
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| 2. |
- Abba, S.I., et al.
(författare)
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Integrating feature extraction approaches with hybrid emotional neural networks for water quality index modeling
- 2022
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Ingår i: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 114
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Tidskriftsartikel (refereegranskat)abstract
- The establishment of water quality prediction models is vital for aquatic ecosystems analysis. The traditional methods of water quality index (WQI) analysis are time-consuming and associated with a high degree of errors. These days, the application of artificial intelligence (AI) based models are trending for capturing nonlinear and complex processes. Therefore, the present study was conducted to predict the WQI in the Kinta River, Malaysia by employing the hybrid AI model i.e., GA-EANN (genetic algorithm-emotional artificial neural network). The extreme gradient boosting (XGB) and neuro-sensitivity analysis (NSA) approaches were utilized for feature extraction, and six different model combinations were derived to examine the relationship among the WQI with water quality (WQ) variables. The efficacy of the proposed hybrid GA-EANN model was evaluated against the backpropagation neural network (BPNN) and multilinear regression (MLR) models during calibration, and validation periods based on Nash–Sutcliffeefficiency (NSE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (CC) indicators. According to results of appraisal the hybrid GA-EANN model produced better outcomes (NSE = 0.9233/ 0.9018, MSE = 10.5195/ 9.7889 mg/L, RMSE = 3.2434/ 3.1287 mg/L, MAPE = 3.8032/ 3.0348 mg/L, CC = 0.9609/ 0.9496) in calibration/ validation phases than BPNN and MLR models. In addition, the results indicate the better performance and suitability of the hybrid GA-EANN model with five input parameters in predicting the WQI for the study site.
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| 3. |
- Aiyelokun, Oluwatobi, et al.
(författare)
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Credibility of design rainfall estimates for drainage infrastructures : extent of disregard in Nigeria and proposed framework for practice
- 2021
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Ingår i: Natural Hazards. - : Springer Science and Business Media LLC. - 0921-030X .- 1573-0840. ; 109:2, s. 1557-1588
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Tidskriftsartikel (refereegranskat)abstract
- Rainfall intensity or depth estimates are vital input for hydrologic and hydraulic models used in designing drainage infrastructures. Unfortunately, these estimates are susceptible to different sources of uncertainties including climate change, which could have high implications on the cost and design of hydraulic structures. This study adopts a systematic literature review to ascertain the disregard of credibility assessment of rainfall estimates in Nigeria. Thereafter, a simple framework for informing the practice of reliability check of rainfall estimates was proposed using freely available open-source tools and applied to the north central region of Nigeria. The study revealed through a synthesis matrix that in the last decade, both empirical and theoretical methods have been applied in predicting design rainfall intensities or depths for different frequencies across Nigeria, but none of the selected studies assessed the credibility of the design estimates. This study has established through the application of the proposed framework that drainage infrastructure designed in the study area using 100–1000-year return periods are more susceptible to error. And that the extent of the credibility of quantitative estimates of extreme rains leading to flooding is not equal for each variability indicator across a large spatial region. Hence, to optimize informed decision-making regarding flood risk reduction by risk assessor, variability and uncertainty of rainfall estimates should be assessed spatially to minimize erroneous deductions.
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| 4. |
- Bishnoi, Sudha, et al.
(författare)
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Classification of Cotton Genotypes with Mixed Continuous and Categorical Variables: Application of Machine Learning Models
- 2022
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Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 14:20
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Tidskriftsartikel (refereegranskat)abstract
- Mixed data is a combination of continuous and categorical variables and occurs frequently in fields such as agriculture, remote sensing, biology, medical science, marketing, etc., but only limited work has been done with this type of data. In this study, data on continuous and categorical characters of 452 genotypes of cotton (Gossypium hirsutum) were obtained from an experiment conducted by the Central Institute of Cotton Research (CICR), Sirsa, Haryana (India) during the Kharif season of the year 2018–2019. The machine learning (ML) classifiers/models, namely k-nearest neighbor (KNN), Classification and Regression Tree (CART), C4.5, Naïve Bayes, random forest (RF), bagging, and boosting were considered for cotton genotypes classification. The performance of these ML classifiers was compared to each other along with the linear discriminant analysis (LDA) and logistic regression. The holdout method was used for cross-validation with an 80:20 ratio of training and testing data. The results of the appraisal based on hold-out cross-validation showed that the RF and AdaBoost performed very well, having only two misclassifications with the same accuracy of 97.26% and the error rate of 2.74%. The LDA classifier performed the worst in terms of accuracy, with nine misclassifications. The other performance measures, namely sensitivity, specificity, precision, F1 score, and G-mean, were all together used to find out the best ML classifier among all those considered. Moreover, the RF and AdaBoost algorithms had the highest value of all the performance measures, with 96.97% sensitivity and 97.50% specificity. Thus, these models were found to be the best in classifying the low- and high-yielding cotton genotypes.
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| 5. |
- Malik, Anurag, et al.
(författare)
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Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test
- 2021
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 15:1, s. 1075-1094
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Tidskriftsartikel (refereegranskat)abstract
- Ensuring accurate estimation of evaporation is weighty for effective planning and judicious management of available water resources for agricultural practices. Thus, this work enhances the potential of support vector regression (SVR) optimized with a novel nature-inspired algorithm, namely, Slap Swarm Algorithm (SVR-SSA) against Whale Optimization Algorithm (SVR-WOA), Multi-Verse Optimizer (SVR-MVO), Spotted Hyena Optimizer (SVR-SHO), Particle Swarm Optimization (SVR-PSO), and Penman model (PM). Daily EP (pan-evaporation) was estimated in two different agro-climatic zones (ACZ) in northern India. The optimal combination of input parameters was extracted by applying the Gamma test (GT). The outcomes of the hybrid of SVR and PM models were equated with recorded daily EP observations based on goodness-of-fit measures along with graphical scrutiny. The results of the appraisal showed that the novel hybrid SVR-SSA-5 model performed superior (MAE = 0.697, 1.556, 0.858 mm/day; RMSE = 1.116, 2.114, 1.202 mm/day; IOS = 0.250, 0.350, 0.303; NSE = 0.0.861, 0.750, 0.834; PCC = 0.929, 0.868, 0.918; IOA = 0.960, 0.925, 0.956) than other models in testing phase at Hisar, Bathinda, and Ludhiana stations, respectively. In conclusion, the hybrid SVR-SSA model was identified as more suitable, robust, and reliable than the other models for daily EP estimation in two different ACZ.
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| 6. |
- Malik, Anurag, et al.
(författare)
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Modeling monthly pan evaporation process over the Indian central Himalayas : application of multiple learning artificial intelligence model
- 2020
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - UK : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 14:1, s. 323-338
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Tidskriftsartikel (refereegranskat)abstract
- The potential of several predictive models including multiple model-artificial neural network (MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM), multi-gene genetic programming (MGGP), and ‘M5Tree’ were assessed to simulate the pan evaporation in monthly scale (EPm) at two stations (e.g. Ranichauri and Pantnagar) in India. Monthly climatological information were used for simulating the pan evaporation. The utmost effective input-variables for the MM-ANN, MGGP, MARS, SVM, and M5Tree were determined using the Gamma test (GT). The predictive models were compared to each other using several statistical criteria (e.g. mean absolute percentage error (MAPE), Willmott's Index of agreement (WI), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and Legate and McCabe’s Index (LM)) and visual inspection. The results showed that the MM-ANN-1 and MGGP-1 models (NSE, WI, LM, RMSE, MAPE are 0.954, 0.988, 0.801, 0.536 mm/month, 9.988% at Pantnagar station, and 0.911, 0.975, 0.724, and 0.364 mm/month, 12.297% at Ranichauri station, respectively) with input variables equal to six were more successful than the other techniques during testing period to simulate the monthly pan evaporation at both Ranichauri and Pantnagar stations. Thus, the results of proposed MM-ANN-1 and MGGP-1 models will help to the local stakeholders in terms of water resources management.
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| 7. |
- Malik, Anurag, et al.
(författare)
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Pan Evaporation Estimation in Uttarakhand and Uttar Pradesh States, India : Validity of an Integrative Data Intelligence Model
- 2020
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Ingår i: Atmosphere. - Switzerland : MDPI. - 2073-4433. ; 11:6
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Tidskriftsartikel (refereegranskat)abstract
- Appropriate input selection for the estimation matrix is essential when modeling non-linear progression. In this study, the feasibility of the Gamma test (GT) was investigated to extract the optimal input combination as the primary modeling step for estimating monthly pan evaporation (EPm). A new artificial intelligent (AI) model called the co-active neuro-fuzzy inference system (CANFIS) was developed for monthly EPm estimation at Pantnagar station (located in Uttarakhand State) and Nagina station (located in Uttar Pradesh State), India. The proposed AI model was trained and tested using different percentages of data points in scenarios one to four. The estimates yielded by the CANFIS model were validated against several well-established predictive AI (multilayer perceptron neural network (MLPNN) and multiple linear regression (MLR)) and empirical (Penman model (PM)) models. Multiple statistical metrics (normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC), Willmott index (WI), and relative error (RE)) and graphical interpretation (time variation plot, scatter plot, relative error plot, and Taylor diagram) were performed for the modeling evaluation. The results of appraisal showed that the CANFIS-1 model with six input variables provided better NRMSE (0.1364, 0.0904, 0.0947, and 0.0898), NSE (0.9439, 0.9736, 0.9703, and 0.9799), PCC (0.9790, 0.9872, 0.9877, and 0.9922), and WI (0.9860, 0.9934, 0.9927, and 0.9949) values for Pantnagar station, and NRMSE (0.1543, 0.1719, 0.2067, and 0.1356), NSE (0.9150, 0.8962, 0.8382, and 0.9453), PCC (0.9643, 0.9649, 0.9473, and 0.9762), and WI (0.9794, 0.9761, 0.9632, and 0.9853) values for Nagina stations in all applied modeling scenarios for estimating the monthly EPm. This study also confirmed the supremacy of the proposed integrated GT-CANFIS model under four different scenarios in estimating monthly EPm. The results of the current application demonstrated a reliable modeling methodology for water resource management and sustainability.
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| 8. |
- Malik, Anurag, et al.
(författare)
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The Implementation of a Hybrid Model for Hilly Sub-Watershed Prioritization Using Morphometric Variables : Case Study in India
- 2019
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Ingår i: Water. - : MDPI. - 2073-4441. ; 11:6, s. 1-19
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Tidskriftsartikel (refereegranskat)abstract
- Among several components of watershed prioritization, morphometric parameters are considered to be essential elements for appropriate water resource planning and anagement. In the current study, nine hilly sub-watersheds are prioritized using novel hybrid model ased on morphometric variables analysis at Bino Watershed (BW) located in the upper Ramganga basin, India. The proposed model is based on the hybridization of principal component analysis (PCA) with weighted-sum approach (WSA), presenting a single-frame methodology (PCWSA) for sub-watershed prioritization. The prioritization process was conducted based on several morphometric parameters including linear, areal, and shape. The PCA was performed to identify the significant correlated factor-loading matrix whereas WSA was established to provide the weights for the morphometric parameters and fix their priority ranking (PR) to be categorized based on compound factor value. The findings showed that 37.81% of total area is under highly susceptible zone sub-watersheds (SW-6 and SW-7). This is verifying the necessity for appropriate soil and water conservation measures for the area. The proposed hybrid methodology demonstrated a reliable approach for water resource planning and management, agriculture, and irrigation activities in the study region.
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| 9. |
- Maroufpoor, Saman, et al.
(författare)
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A novel hybridized neuro-fuzzy model with an optimal input combination for dissolved oxygen estimation
- 2022
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Ingår i: Frontiers in Environmental Science. - : Frontiers Media S.A.. - 2296-665X. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Dissolved oxygen (DO) is one of the main prerequisites to protect amphibian biological systems and to support powerful administration choices. This research investigated the applicability of Shannon’s entropy theory and correlation in obtaining the combination of the optimum inputs, and then the abstracted input variables were used to develop three novel intelligent hybrid models, namely, NF-GWO (neuro-fuzzy with grey wolf optimizer), NF-SC (subtractive clustering), and NF-FCM (fuzzy c-mean), for estimation of DO concentration. Seven different input combinations of water quality variables, including water temperature (TE), specific conductivity (SC), turbidity (Tu), and pH, were used to develop the prediction models at two stations in California. The performance of proposed models for DO estimation was assessed using statistical metrics and visual interpretation. The results revealed the better performance of NF-GWO for all input combinations than other models where its performance was improved by 24.2–66.2% and 14.9–31.2% in terms of CC (correlation coefficient) and WI (Willmott index) compared to standalone NF for different input combinations. Additionally, the MAE (mean absolute error) and RMSE (root mean absolute error) of the NF model were reduced using the NF-GWO model by 9.9–46.0% and 8.9–47.5%, respectively. Therefore, NF-GWO with all water quality variables as input can be considered the optimal model for predicting DO concentration of the two stations. In contrast, NF-SC performed worst for most of the input combinations. The violin plot of NF-GWO-predicted DO was found most similar to the violin plot of observed data. The dissimilarity with the observed violin was found high for the NF-FCM model. Therefore, this study promotes the hybrid intelligence models to predict DO concentration accurately and resolve complex hydro-environmental problems.
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| 10. |
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PAU Smart Seeder: a novel way forward for rice residue management in North-west India
- 2024
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Ingår i: Scientific Reports. - : Nature Research. - 2045-2322. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- In winter, the paddy residues become wet during morning and late evening due to dew, which restricts the operation of sowing machines (Happy Seeder and Super Seeder) into paddy residues, as wet residues do not slide on furrow openers/tines. A PAU Smart Seeder (PSS) was developed and evaluated for a four-wheel tractor that can sow wheat with optimum crop establishment in combined harvested rice fields. The PSS were evaluated for its performance under varying straw load, forward speed, and rotor speed in terms of fuel consumption, field capacity, seed emergence, and grain yield. The crop establishment and wheat yield of PSS was also compared with the existing straw management machines Happy Seeder (HS) and Super Seeder (SS) under heavy paddy residue conditions. The effect of the straw load was more pronounced on dependent variables than the effect of the speed index. PSS performance was best at a forward speed of 2.6 km h−1, rotor speed of 127.5 rpm, and a straw load of 6 t ha−1. Average fuel consumption using PSS was lower than SS but higher than HS. Wheat emergence was higher by 15.6 and 25.7% on the PSS plots compared to HS and SS, respectively. Average wheat grain yield in PSS plots was significantly higher by 12.7 and 18.9% than SS and HS, respectively in one experiment, while the grain yield was similar for both PSS and HS in other experiments. PSS has a novel mechanism to manage paddy straw and simultaneously sow wheat into a heavy straw load (> 8 t ha−1) mixture of anchored and loose straw. In conclusion, PSS showed promise for in-situ management of rice straw as it eliminates most of the operational problems encountered by the existing seeders (HS and SS).
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| 11. |
- Rai, Priya, et al.
(författare)
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Evaluation of Machine Learning Versus Empirical Models for Monthly Reference Evapotranspiration Estimation in Uttar Pradesh and Uttarakhand States, India
- 2022
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Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 14:10
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Tidskriftsartikel (refereegranskat)abstract
- Reference evapotranspiration (ETo) plays an important role in agriculture applications such as irrigation scheduling, crop simulation, water budgeting, and reservoir operations. Therefore, the accurate estimation of ETo is essential for optimal utilization of available water resources on regional and global scales. The present study was conducted to estimate the monthly ETo at Nagina (Uttar Pradesh State) and Pantnagar (Uttarakhand State) stations by employing the three ML (machine learning) techniques including the SVM (support vector machine), M5P (M5P model tree), and RF (random forest) against the three empirical models (i.e., Valiantzas-1: V-1, Valiantzas-2: V-2, Valiantzas-3: V-3). Three different input combinations (i.e., C-1, C-2, C-3) were formulated by using 8-year (2009–2016) climatic data of wind speed (u), solar radiation (Rs), relative humidity (RH), and mean air temperature (T) recorded at both stations. The predictive efficacy of ML and the empirical models was evaluated based on five statistical indicators i.e., CC (correlation coefficient), WI (Willmott index), EC (efficiency coefficient), RMSE (root mean square error), and MAE (mean absolute error) presented through a heatmap along with graphical interpretation (Taylor diagram, time-series, and scatter plots). The results showed that the SVM-1 model corresponding to the C-1 input combination outperformed the other ML and empirical models at both stations. Moreover, the SVM-1 model had the lowest MAE (0.076, 0.047 mm/month) and RMSE (0.110, 0.063 mm/month), and highest EC (0.995, 0.999), CC (0.998, 0.999), and WI (0.999, 1.000) values during validation period at Nagina and Pantnagar stations, respectively, and closely followed by the M5P model. Consequently, the ML model (i.e., SVM) was found to be more robust, and reliable in monthly ETo estimation and can be used as a promising alternative to empirical models at both study locations.
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| 12. |
- Sammen, Saad Sh., et al.
(författare)
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Assessment of climate change impact on probable maximum floods in a tropical catchment
- 2022
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Ingår i: Journal of Theoretical and Applied Climatology. - : Springer. - 0177-798X .- 1434-4483. ; 148:1-2, s. 15-31
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Tidskriftsartikel (refereegranskat)abstract
- The increases in extreme rainfall could increase the probable maximum flood (PMF) and pose a severe threat to the critical hydraulic infrastructure such as dams and flood protection structures. This study is conducted to assess the impact of climate change on PMF in a tropical catchment. Climate and inflow data of the Tenmengor reservoir, located in the state of Perak in Malaysia, have been used to calibrate and validate the hydrological model. The projected rainfall from regional climate model is used to generate probable maximum precipitation (PMP) for future periods. A hydrological model was used to simulate PMF from PMP estimated for the historical and two future periods, early (2031 − 2045) and late (2060 − 2075). The results revealed good performance of the hydrological model with Nash–Sutcliffe efficiency, 0.74, and the relative standard error, 0.51, during validation. The estimated rainfall depths were 89.5 mm, 106.3 mm, and 143.3 mm, respectively, for 5, 10, and 50 years of the return period. The study indicated an increase in PMP by 162% to 507% and 259% to 487% during early and late periods for different return periods ranging from 5 to 1000 years. This would cause an increase in PMF by 48.9% and 122.6% during early and late periods. A large increase in PMF indicates the possibility of devastating floods in the future in his tropical catchment due to climate change.
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| 13. |
- Sammen, Saad Sh., et al.
(författare)
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Enhanced Artificial Neural Network with Harris Hawks Optimization for Predicting Scour Depth Downstream of Ski-Jump Spillway
- 2020
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Ingår i: Applied Sciences. - Switzerland : MDPI. - 2076-3417. ; 10:15
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Tidskriftsartikel (refereegranskat)abstract
- A spillway is a structure used to regulate the discharge flowing from hydraulic structures such as a dam. It also helps to dissipate the excess energy of water through the still basins. Therefore, it has a significant effect on the safety of the dam. One of the most serious problems that may be happening below the spillway is bed scouring, which leads to soil erosion and spillway failure. This will happen due to the high flow velocity on the spillway. In this study, an alternative to the conventional methods was employed to predict scour depth (SD) downstream of the ski-jump spillway. A novel optimization algorithm, namely, Harris hawks optimization (HHO), was proposed to enhance the performance of an artificial neural network (ANN) to predict the SD. The performance of the new hybrid ANN-HHO model was compared with two hybrid models, namely, the particle swarm optimization with ANN (ANN-PSO) model and the genetic algorithm with ANN (ANN-GA) model to illustrate the efficiency of ANN-HHO. Additionally, the results of the three hybrid models were compared with the traditional ANN and the empirical Wu model (WM) through performance metrics, viz., mean absolute error (MAE), root mean square error (RMSE), coefficient of correlation (CC), Willmott index (WI), mean absolute percentage error (MAPE), and through graphical interpretation (line, scatter, and box plots, and Taylor diagram). Results of the analysis revealed that the ANN-HHO model (MAE = 0.1760 m, RMSE = 0.2538 m) outperformed ANN-PSO (MAE = 0.2094 m, RMSE = 0.2891 m), ANN-GA (MAE = 0.2178 m, RMSE = 0.2981 m), ANN (MAE = 0.2494 m, RMSE = 0.3152 m) and WM (MAE = 0.1868 m, RMSE = 0.2701 m) models in the testing period. Besides, graphical inspection displays better accuracy of the ANN-HHO model than ANN-PSO, ANN-GA, ANN, and WM models for prediction of SD around the ski-jump spillway.
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| 14. |
- Singh, Mahesh Chand, et al.
(författare)
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GIS integrated RUSLE model-based soil loss estimation and watershed prioritization for land and water conservation aspects
- 2023
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Ingår i: Frontiers in Environmental Science. - : Frontiers Media S.A.. - 2296-665X. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- Land degradation has become one of the major threats throughout the globe, affecting about 2.6 billion people in more than 100 countries. The highest rate of land degradation is in Asia, followed by Africa and Europe. Climate change coupled with anthropogenic activities have accelerated the rate of land degradation in developing nations. In India, land degradation has affected about 105.48 million hectares. Thus, modeling and mapping soil loss, and assessing the vulnerability threat of the active erosional processes in a region are the major challenges from the land and water conservation aspects. The present study attempted rigorous modeling to estimate soil loss from the Banas Basin of Rajasthan state, India, using GIS-integrated Revised Universal Soil Loss Equation (RUSLE) equation. Priority ranking was computed for different watersheds in terms of the degree of soil loss from their catchments, so that appropriate conservation measures can be implemented. The total area of Banas basin (68,207.82 km2) was systematically separated into 25 watersheds ranging in area from 113.0 to 7626.8 km2. Rainfall dataset of Indian Meteorological Department for 30 years (1990–2020), FAO based Soil map for soil characterization, ALOS PALSAR digital elevation model for topographic assessment, and Sentinal-2 based land use and land cover map were integrated for modeling and mapping soil erosion/loss risk assessment. The total annual soil loss in the Banas basin was recorded as 21,766,048.8 tons. The areas under very low (0–1 t ha-1 year-1), low (1–5 t ha-1 year-1), medium (5–10 t ha-1 year-1), high (10–50 t ha-1 year-1) and extreme (>50 t ha-1 year-1) soil loss categories were recorded as 24.2, 66.8, 7.3, 0.9, and 0.7%, respectively, whereas the respective average annual soil loss values were obtained as 0.8, 3.0, 6.0, 23.1, and 52.0 t ha-1 year-1. The average annual soil loss among different watersheds was recorded in the range of 1.1–84.9 t ha-1 year-1, being highest (84.9 t ha-1 year-1) in WS18, followed by WS10 (38.4 t ha-1 year-1), SW25 (34.7 t ha-1 year-1) and WS23 (17.9 t ha-1 year-1), whereas it was lowest for WS8 (1.1 t ha-1 year-1). Thus, WS18 obtained the highest/top priority rank in terms of the average annual soil loss (84.9 t ha-1 year-1) to be considered as the first priority for land and water conservation planning and implementation. The quantitative results of this study would be useful for implementation of land and water conservation measures in the problematic areas of the Banas basin for controlling soil loss through water erosion.
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