Application of weights-of-evidence (WoE) and evidential belief function (EBF) models for the delineation of soil erosion vulnerable zones: a study on Pathro river basin, Jharkhand, India

Integration of remote sensing and machine learning algorithm for agricultural drought early warning over Genale Dawa river basin, Ethiopia

Drought remains a menace in the Horn of Africa; as a result, the Ethiopia's Genale Dawa River Basin is one of the most vulnerable to agricultural drought. Hence, this study integrates remote sensing and machine learning algorithm for early warning identification through assessment and prediction of index-based agricultural drought over the basin. To track the severity of the drought in the basin from 2003 to 2023, a range of high-resolution satellite imagery output indexes were used, including the Vegetation Condition Index (VCI), Thermal Condition Index (TCI), and Vegetation Health Index (VHI). Additionally, the Artificial Neural Network machine learning technique was used to predict agricultural drought VHI for the period of 2028 and 2033. Results depict that during the 2023 period, 25% of severe drought and 18% of extreme drought countered at the lower part of the basin at Dolo ado and Chereti regions. A high TCI value was found that around 23.24% under extreme drought and low precipitation countered in areas of Moyale, Dolo ado, Dolobay, Afder, and Bure lower than 3.57 mm per month. Similarly, increment of severe drought from 24.26% to 24.58% and 16.53% to 16.58% of extreme drought value of VHI might be experienced during the 2028 and 2033 period respectively in the area of Mada Wolabu, Dolo ado, Dodola, Gore, Gidir, and Rayitu. The findings of this study are significantly essential for the institutes located particularly in the basin as they will allow them to adapt drought-coping mechanisms and decision-making easily.

Influence of climate on desert locust (Schistocerca gregaria Forskål, 1775) Plague and migration prediction in tropics

The outbreak of the desert locust Schistocerca gregaria Forskål, 1775, which originated from the Horn of Africa in 2019-2020 created an episodic plague under bio-geographical settings in the arid and semi-arid areas of South and Southwest Asia. In India, it happened after twenty-seven years due to the persistence of a few favourable conditions caused by its plague, resulting in hundreds of crores in crop damage. Keeping this in mind, the study aims to assess the suitability and likelihood of the desert locust epidemic occurring in India, utilizing two widely recognized statistical models: Weight-of-Evidence (WoE) and Frequency Ratio (FR). This work evaluated nine critical climatic factors for the study considering western and central parts of India. The 'Projected Locust Suitability' (PLS) was calculated by analyzing the correlation of the considered variables and the occurrence of locust swarms and bands. The significance (importance) of each variable on PLS was determined using Principal Component Analysis (PCA) and Random Forest (RF) algorithms. The PLS maps clearly show that 42.7-52.8% of the areas fall under high and very high locust suitability zones. The result suggests that the Ajmer-Gwalior-Allahabad tract is highly prone to future locust occurrences, while the Aligarh-Bareilly-Lakhimpur tract is moderately susceptible. The effectiveness of both modelled PLS maps was determined with the help of the ROC curve. The AUC results indicate that both the WoE (0.92) and the RF (0.90) models worked remarkably well in precisely predicting PLS. The RF-based IncNodePurity analysis indicates that low to moderate temperatures in the presence of cloud cover significantly impact locust occurrence and migration. The present findings are projected to direct the development of sustainable locust management strategies utilizing proper land use policies in the tropical climate.

Prediction and monitoring of LULC shift using cellular automata-artificial neural network in Jumar watershed of Ranchi District, Jharkhand

Jumar watershed of Ranchi district is agrarian in nature. The unplanned and exponentially growing urban sprawl has become one of the probable threats in achieving sustainable development goals (SDG-15). The purpose of this research study is to monitor the urban sprawl in Jumar watershed within three decades i.e. from the year 1990 to 2021. Land use land cover (LULC) change has been monitored using satellite data from LANDSAT (4, 5 and 8). Various indices are calculated like normalised difference vegetation index (NDVI), normalised difference built-up index (NDBI), normalised difference water index (NDWI) and built-up index (BUI) to monitor LULC change in the area. For prediction of urban sprawl, cellular automata and artificial neural network (CA-ANN) with GIS application technique is used. The model is validated by using Kappa coefficient. The prediction results showed increase in built-up area by 8.23 sq. km in the next decade. The built-up and barren land together increase up to 42.85 sq. km by 2030 and 34.61 sq. km in 2021. The NDVI for 3-decade period showed significant decrease in the healthy vegetation and increase in sparse vegetation. The NDBI showed a slight increase in urban area but massive increase in uncultivated and barren land. NDWI showed a decrease in area of the surface water. The LULC studies showed a major shift from healthy vegetation to agriculture and then to barren land. To assess the impact of urbanisation on water quality, water samples are taken seasonally from J1to J11 sampling locations and are analysed as per APHA procedure. The sites are classified as urban, semi urban and rural area as per their location. The water quality index (WQI) varied between 42.14 to 61.42 during pre-monsoon, 62.20 to 68.7995 during monsoon and 43.48 to 60.12 during post-monsoon. The quality of water is found poor in all seasons at all sampling sites. The water is found highly turbid and alkaline throughout the year. Overall, it can be concluded that the water needs to be pre-treated for drinking purposes throughout the year.

Soil Erosion Assessment Using the RUSLE Model, Remote Sensing, and GIS in the Shatt Al-Arab Basin (Iraq-Iran)

In the Shatt Al-Arab basin, soil erosion is a major problem due to the steepness of the terrain and the significant difference in altitude between the upstream and downstream parts of the basin. Vast quantities of soil are moved annually, resulting in massive repercussions including soil degradation, structural damage, biodiversity loss, and productivity reduction in the catchment area, huge sediment load, and the pollution of streams and rivers. Consequently, the assessment of soil erosion risk and geographical distribution is essential for constructing a database for developing effective control strategies. Revised Universal Soil Loss Equation (RUSLE) was combined with Remote Sensing (RS) and Geographic Information System (GIS) in the current work to define the soil erosion hazard map in the Shatt Al-Arab basin. The RUSLE model included various characteristics for soil erosion zonation including rainfall erosivity, soil erodibility, slope length and steepness, land cover and management, and conservation support practices. Annual erosion rates in this study in tons per hectare were: extremely high (more than 50); very high (50 to 16.5); high (16.5 to 2.2); medium (2.2 to 1); and low (1 to 0) ton ha−1year−1 representing 16, 4, 13, 7, and 60 % of the basin’s area, respectively. The high soil loss rates are associated with heavy rainfall, loamy soil predominance, elevated terrains/plateau borders with a steep side slope, and intensive farming. Managers and policymakers may use the results of this study to implement adequate conservation programs to prevent soil erosion or recommend soil conservation acts if development projects are to proceed in places with a high soil erosion risk.

Machine learning models for wetland habitat vulnerability in mature Ganges delta

The present study attempts to measure wetland habitat vulnerability (WHV) in the Indian part of mature Ganges delta. Predictive algorithms belonging to bivariate statistics and machine learning (ML) algorithms were applied for fulfilling the data mining and generating the models. Results show that 60% of the wetland areas are covered by moderate to very high WHV, out of which > 300 km² belong to very high WHV followed by a high vulnerability in almost 150 km². This areal coverage increases by 10-15% from phase II to phase III. On the other hand, a relatively safe situation is confined to < 200 km². The receiver operating characteristic curve, root-mean-square error, and correlation coefficient are used to assess the accuracy of these models and categorization of habitat vulnerability. Ensemble modeling is done using the individual models having a greater accuracy level in order to increase accuracy. A field-based model of the same is prepared by gathering information directly from the field which also exhibits similar results with the algorithm-based models. Analysis of residuals in standard regression strongly supports the relevance of the selected parameters and multi-parametric models.

Susceptibility to Gully Erosion: Applying Random Forest (RF) and Frequency Ratio (FR) Approaches to a Small Catchment in Ethiopia

Soil erosion by gullies in Ethiopia is causing environmental and socioeconomic problems. A sound soil and water management plan requires accurately predicted gully erosion hotspot areas. Hence, this study develops a gully erosion susceptibility map (GESM) using frequency ratio (FR) and random forest (RF) algorithms. A total of 56 gullies were surveyed, and their extents were derived by digitizing Google Earth imagery. Literature review and a multicollinearity test resulted in 14 environmental variables for the final analysis. Model prediction potential was evaluated using the area under the curve (AUC) method. Results showed that the best prediction accuracy using the FR and RF models was obtained by using the top four most important gully predictor factors: drainage density, elevation, land use, and groundwater table. The notion that the groundwater table is one of the most important gully predictor factors in Ethiopia is a novel and significant quantifiable finding and is critical to the design of effective watershed management plans. Results from separate variable importance analyses showed land cover for Nitisols and drainage density for Vertisols as leading factors determining gully locations. Factors such as texture, stream power index, convergence index, slope length, and plan and profile curvatures were found to have little significance for gully formation in the studied catchment.

A Review on Assessing and Mapping Soil Erosion Hazard Using Geo-Informatics Technology for Farming System Management

Soil erosion is a severe threat to food production systems globally. Food production in farming systems decreases with increasing soil erosion hazards. This review article focuses on geo-informatics applications for identifying, assessing and predicting erosion hazards for sustainable farming system development. Several researchers have used a variety of quantitative and qualitative methods with erosion models, integrating geo-informatics techniques for spatial interpretations to address soil erosion and land degradation issues. The review identified different geo-informatics methods of erosion hazard assessment and highlighted some research gaps that can provide a basis to develop appropriate novel methodologies for future studies. It was found that rainfall variation and land-use changes significantly contribute to soil erosion hazards. There is a need for more research on the spatial and temporal pattern of water erosion with rainfall variation, innovative techniques and strategies for landscape evaluation to improve the environmental conditions in a sustainable manner. Examining water erosion and predicting erosion hazards for future climate scenarios could also be approached with emerging algorithms in geo-informatics and spatiotemporal analysis at higher spatial resolutions. Further, geo-informatics can be applied with real-time data for continuous monitoring and evaluation of erosion hazards to risk reduction and prevent the damages in farming systems.

Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic algorithms

Landslides are natural and sometimes quasi-natural hazards that are destructive to natural resources and cause loss of human life every year. Hence, preparing susceptibility maps for landslide monitoring is essential to minimizing their negative effects. The main aim of the current research was to develop landslide susceptibility maps for Icheon Township, South Korea, using hybrid Machin learning and metaheuristic algorithms, that is, the bee algorithm (Bee), the adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and the grey wolf optimizer (GWO), and to compare their predictive accuracy. Based on identified landslide locations, an inventory map was prepared and divided into training and validation data sets (70%/30%). the predicated model outcomes were validated with root mean square error (RMSE), and area under receiver operating characteristic curve (AUC), and pairwise comparison values for the ANFIS, ANFIS-Bee, ANFIS-GWO, SVR, SVR-Bee, and SVR-GWO models were obtained. The area under the curve was obtained with the training and validation data sets. Based on the training data sets, AUC of 80%, 83%, 83%, 69%, 81%, and 80% were obtained for the SVR, SVR-GWO, SVR-Bee, ANFIS, ANFIS-GWO, and ANFIS-Bee models, respectively. For the validation data sets, values of 79%, 82%, 82%, 68%, 79%, and 79%, respectively, were obtained. The SVR-GWO and SVR-Bee models were the most predictive models in terms of constructing the exceptionally focused landslide susceptibility map, with little spatial variation in the highly susceptible classes. Furthermore, the MSE, RMSE, and pairwise comparisons indicated that the SVR-GWO and SVR-Bee models were superior models for this study township. In addition, ANFIS individually was not superior to the ensembles of ANFIS-GWO and ANFIS-Bee for landslide assessment. These landslide susceptibility maps provide a platform for land use planning with an eye toward sustainable development of infrastructure and damage reduction for Icheon Township.

Evaluating the Performance of Individual and Novel Ensemble of Machine Learning and Statistical Models for Landslide Susceptibility Assessment at Rudraprayag District of Garhwal Himalaya

Landslides are known as the world’s most dangerous threat in mountainous regions and pose a critical obstacle for both economic and infrastructural progress. It is, therefore, quite relevant to discuss the pattern of spatial incidence of this phenomenon. The current research manifests a set of individual and ensemble of machine learning and probabilistic approaches like an artificial neural network (ANN), support vector machine (SVM), random forest (RF), logistic regression (LR), and their ensembles such as ANN-RF, ANN-SVM, SVM-RF, SVM-LR, LR-RF, LR-ANN, ANN-LR-RF, ANN-RF-SVM, ANN-SVM-LR, RF-SVM-LR, and ANN-RF-SVM-LR for mapping landslide susceptibility in Rudraprayag district of Garhwal Himalaya, India. A landslide inventory map along with sixteen landslide conditioning factors (LCFs) was used. Randomly partitioned sets of 70%:30% were used to ascertain the goodness of fit and predictive ability of the models. The contribution of LCFs was analyzed using the RF model. The altitude and drainage density were found to be the responsible factors in causing the landslide in the study area according to the RF model. The robustness of models was assessed through three threshold dependent measures, i.e., receiver operating characteristic (ROC), precision and accuracy, and two threshold independent measures, i.e., mean-absolute-error (MAE) and root-mean-square-error (RMSE). Finally, using the compound factor (CF) method, the models were prioritized based on the results of the validation methods to choose best model. Results show that ANN-RF-LR indicated a realistic finding, concentrating only on 17.74% of the study area as highly susceptible to landslide. The ANN-RF-LR ensemble demonstrated the highest goodness of fit and predictive capacity with respective values of 87.83% (area under the success rate curve) and 93.98% (area under prediction rate curve), and the highest robustness correspondingly. These attempts will play a significant role in ensemble modeling, in building reliable and comprehensive models. The proposed ANN-RF-LR ensemble model may be used in the other geographic areas having similar geo-environmental conditions. It may also be used in other types of geo-hazard modeling.

Spatial Prediction of Landslide Susceptibility Based on GIS and Discriminant Functions

The areas where landslides occur frequently pose severe threats to the local population, which necessitates conducting regional landslide susceptibility mapping (LSM). In this study, four models including weight-of-evidence (WoE) and three WoE-based models, which were linear discriminant analysis (LDA), Fisher’s linear discriminant analysis (FLDA), and quadratic discriminant analysis (QDA), were used to obtain the LSM in the Nanchuan region of Chongqing, China. Firstly, a dataset was prepared from sixteen landslide causative factors, including eight topographic factors, three distance-related factors, and five environmental factors. A landslide inventory map including 298 landslide locations was also constructed and randomly divided with a ratio of 70:30 as training and validation data. Subsequently, the WoE method was used to estimate the relationship between landslides and the landslide causative factors, which assign a weight value to each class of causative factors. Finally, four models were applied using the training dataset, and the predictive performance of each model was compared using the validation datasets. The results showed that FLDA had a higher performance than the other three models according to the success rate curve (SRC) and prediction rate curve (PRC), illustrating that it could be considered a promising approach for landslide susceptibility mapping in the study area.

Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India

Gully erosion is a form of natural disaster and one of the land loss mechanisms causing severe problems worldwide. This study aims to delineate the areas with the most severe gully erosion susceptibility (GES) using the machine learning techniques Random Forest (RF), Gradient Boosted Regression Tree (GBRT), Naïve Bayes Tree (NBT), and Tree Ensemble (TE). The gully inventory map (GIM) consists of 120 gullies. Of the 120 gullies, 84 gullies (70%) were used for training and 36 gullies (30%) were used to validate the models. Fourteen gully conditioning factors (GCFs) were used for GES modeling and the relationships between the GCFs and gully erosion was assessed using the weight-of-evidence (WofE) model. The GES maps were prepared using RF, GBRT, NBT, and TE and were validated using area under the receiver operating characteristic(AUROC) curve, the seed cell area index (SCAI) and five statistical measures including precision (PPV), false discovery rate (FDR), accuracy, mean absolute error (MAE), and root mean squared error (RMSE). Nearly 7% of the basin has high to very high susceptibility for gully erosion. Validation results proved the excellent ability of these models to predict the GES. Of the analyzed models, the RF (AUROC = 0.96, PPV = 1.00, FDR = 0.00, accuracy = 0.87, MAE = 0.11, RMSE = 0.19 for validation dataset) is accurate enough for modeling and better suited for GES modeling than the other models. Therefore, the RF model can be used to model the GES areas not only in this river basin but also in other areas with the same geo-environmental conditions.

Spatial Prediction of Landslides Using Hybrid Integration of Artificial Intelligence Algorithms with Frequency Ratio and Index of Entropy in Nanzheng County, China

The main object of this study is to introduce hybrid integration approaches that consist of state-of-the-art artificial intelligence algorithms (SysFor) and two bivariate models, namely the frequency ratio (FR) and index of entropy (IoE), to carry out landslide spatial prediction research. Hybrid integration approaches of these two bivariate models and logistic regression (LR) were used as benchmark models. Nanzheng County was considered as the study area. First, a landslide distribution map was produced using news reports, interpreting satellite images and a regional survey. A total of 202 landslides were identified and marked. According to the previous studies and local geological environment conditions, 16 landslide conditioning factors were chosen for landslide spatial prediction research: elevation, profile curvature, plan curvature, slope angle, slope aspect, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), distance to roads, distance to rivers, distance to faults, lithology, rainfall, soil, normalized different vegetation index (NDVI), and land use. Then, the 202 landslides were randomly segmented into two parts with a ratio of 70:30. Seventy percent of the landslides (141) were used as the training dataset and the remaining landslides (61) were used as the validating dataset. Next, the evaluation models were built using the training dataset and compared by the receiver operating characteristics (ROC) curve. The results showed that all models performed well; the FR_SysFor model exhibited the best prediction ability (0.831), followed by the IoE_SysFor model (0.819), IoE_LR model (0.702), FR_LR model (0.696), IoE model (0.691), and FR model (0.681). Overall, these six models are practical tools for landslide spatial prediction research and the results can provide a reference for landslide prevention and control in the study area.

GIS-Based Evaluation of Landslide Susceptibility Models Using Certainty Factors and Functional Trees-Based Ensemble Techniques

The main purpose of this paper is to use ensembles techniques of functional tree-based bagging, rotation forest, and dagging (functional trees (FT), bagging-functional trees (BFT), rotation forest-functional trees (RFFT), dagging-functional trees (DFT)) for landslide susceptibility modeling in Zichang County, China. Firstly, 263 landslides were identified, and the landslide inventory map was established, and the landslide locations were randomly divided into 70% (training data) and 30% (validation data). Then, 14 landslide conditioning factors were selected. Furthermore, the correlation analysis between conditioning factors and landslides was applied using the certainty factor method. Hereafter, four models were applied for landslide susceptibility modeling and zoning. Finally, the receiver operating characteristic (ROC) curve and statistical parameters were used to evaluate and compare the overall performance of the four models. The results showed that the area under the curve (AUC) for the four models was larger than 0.74. Among them, the BFT model is better than the other three models. In addition, this study also illustrated that the integrated model is not necessarily more effective than a single model. The ensemble data mining technology used in this study can be used as an effective tool for future land planning and monitoring.

Valuation methodology of laminar erosion potential using fuzzy inference systems in a Brazilian savanna

This study presents an approach on the evaluation of potential laminar erosion in the Ribeirão Sucuri Grande watershed. It is located in the northeast of the state of Goiás, Brazil, a conservation area under strong anthropogenic pressure. A Mamdani fuzzy inference system was designed using linguistic variables, pertinence functions, and a set of rules associated to a traditional laminar erosion prediction model through the environmental conditioners slope, erodibility, and degree of soil protection. The laminar erosion prediction model associated with fuzzy logic is a qualitative evaluation of erosive potential capable of being spatialized with a greater level of detail, increasing the traditional classification by two levels. The processing of environmental and soil conditioning factors using the fuzzy logic resulted in values between 2.5 and 9.1, which places the basin at a low to very high laminar erosion potential. The results indicate areas that demand a greater attention regarding soil management; 56.89% of the area has a medium to high laminar erosion and high to very high erosion (6.99%).

Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms

Gully erosion is one of the most effective drivers of sediment removal and runoff from highland areas to valley floors and stable channels, where continued off-site effects of water erosion occur. Gully initiation and development is a natural process that greatly impacts natural resources, agricultural activities, and environmental quality as it promotes land and water degradation, ecosystem disruption, and intensification of hazards. In this research, an attempt is made to produce gully erosion susceptibility maps for the management of hazard-prone areas in the Pathro River Basin of India using four well-known machine learning models, namely, multivariate additive regression splines (MARS), flexible discriminant analysis (FDA), random forest (RF), and support vector machine (SVM). To support this effort, observations from 174 gully erosion sites were made using field surveys. Of the 174 observations, 70% were randomly split into a training data set to build susceptibility models and the remaining 30% were used to validate the newly built models. Twelve gully erosion conditioning factors were assessed to find the areas most susceptible to gully erosion. The predisposing factors were slope gradient, altitude, plan curvature, slope aspect, land use, slope length (LS), topographical wetness index (TWI), drainage density, soil type, distance from the river, distance from the lineament, and distance from the road. Finally, the results from the four applied models were validated with the help of ROC (Receiver Operating Characteristics) curves. The AUC value for the RF model was calculated to be 96.2%, whereas for those for the FDA, MARS, and SVM models were 84.2%, 91.4%, and 88.3%, respectively. The AUC results indicated that the random forest model had the highest prediction accuracy, followed by the MARS, SVM, and FDA models. However, it could be concluded that all the machine learning models performed well according to their prediction accuracy. The produced GESMs can be very useful for land managers and policy makers as they can be used to initiate remedial measures and erosion hazard mitigation in prioritized areas.

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

The main purpose of this study is to apply three bivariate statistical models, namely weight of evidence (WoE), evidence belief function (EBF) and index of entropy (IoE), and their ensembles with logistic regression (LR) for landslide susceptibility mapping in Muchuan County, China. First, a landslide inventory map contained 279 landslides was obtained through the field investigation and interpretation of aerial photographs. Next, the landslides were randomly divided into two parts for training and validation with the ratio of 70/30. In addition, according to the regional geological environment characteristics, twelve landslide conditioning factors were selected, including altitude, plan curvature, profile curvature, slope angle, distance to roads, distance to rivers, topographic wetness index (TWI), normalized different vegetation index (NDVI), land use, soil, and lithology. Subsequently, the landslide susceptibility mapping was carried out by the above models. Eventually, the accuracy of this research was validated by the area under the receiver operating characteristic (ROC) curve and the results indicated that the landslide susceptibility map produced by EBF-LR model has the highest accuracy (0.826), followed by IoE-LR model (0.825), WoE-LR model (0.792), EBF model (0.791), IoE model (0.778), and WoE model (0.753). The results of this study can provide references of landslide prevention and land use planning for local government.