Effect of the Gravel Filters and Plastic Cover on Improving Water Infiltration Process and Increasing Moisture Storage of Rainwater Harvesting Systems in Sloping Lands

Rainwater harvesting is a suitable method to plant seedlings in sloping lands in arid and semi-arid regions. In this research, a combination of plastic coating and gravel filter has been used to penetrate water into the soil. In this method, water is stored in the soil during the rainy months and the plant uses these waters in the warm months of the year. For this purpose, five treatments (with three replications) including control treatment, system vegetation removal with filter, system vegetation removal without filter, semi-insulated system with filter and semi-insulated system without filter are considered. Two sensors are installed in each of the treatments at a depth of 20 cm and 60 cm of soil, which will record moisture in seven-day periods. Apricot seedlings have been planted in all treatments. The information obtained was analyzed through a completely randomized block design. The results showed that the semi-insulated treatment with gravel filter stored significant moisture in the soil rather than other treatments and stored more moisture in the warm months of the year. The results showed that semi-insulated treatment with gravel filter is a suitable solution to increase soil moisture in the warm months of the year (June, July, August and September).

Land Cover Classification Using Remote Sensing in Amadiyah Province

This study was conducted on the vegetative and non-vegetative land cover spread in the Amadiya District of Dohuk Governorate, northern Iraq, located between longitudes (43 ° 25'24.309 "- 43 ° 11'6.839") to the east and latitudes (37 ° 12'36.359 "- 37 7'25.484") north. They rely on a spatial indication of accuracy (10 m) and are reduced to (5 m) from Sentinel -2. Using unsupervised classifications, to form a general perception of the items in the studied area. As the number of varieties and the number of spectral bands used were determined, then the Supervised Classification to classify the spatial indication at the site to determine the plant and non-plant ground targets. These two classifications resulted, using the (Arc GIS) program, we obtained 12 types when classifying the space declaration for the Amadiyah district. We noticed that the area occupied by the terrestrial targets of the site are (water, medium-density forests (sloping lands), medium-density forests (flatlands), low-density forests (sloping lands), low-density forests (flatlands), limestone rocky areas, dense forests. (Sloping lands), limestone and paved roads, barren lands, residential areas, pastures, dense forests (flatlands) and their areas respectively are (283.9 - 408.6 - 556.2 - 829.2 - 983.6 - 1022.8 - 1066.4 - 1138.8 - 1148.5 - 1172.2 - 1218.4. - 1272.4) km2. The classification accuracy of the spatial indication was estimated based on the error matrix and the Kappa test. From there we found that the accuracy was (84.6%) for the error matrix and (83.34%) for the Kappa test, and this indicates that the classification accuracy is very good It is acceptable and can be relied upon and recommended for classification.

Exploring soil particle size fraction and spatial redistribution of 137Cs in sloping landscapes with different lynchet height of terracing hedgerows in the remote mountain region, southwestern China

Soil erosion is a global environmental problem related to anthropogenic activities, as well as being influenced by natural factors. The sloping cultivated lands, which have serious soil erosion, constitute a major proportion of landscape in the remote mountain region. The traditional soil conservation strategy, referred as certain height of lynchets on the edge of the terracing hedgerows of the sloping lands, plays an effective part in soil and water conservation. A typical sloping landscape with lynchet of terracing hedgerows was chosen in this study. The results showed that fine-grained sediment was deposited in front of the lynchet of terracing hedgerows, especially particle size grouped at < 0.002mm and 0.002-0.02mm. The profiles 137Cs concentration of the lynchet from the upper to the lower sloping landscape showed first increased then decreased trends as the soil depth increased. 137Cs inventory generally increased along the whole sloping landscapes. And the results suggested that the mean 137Cs inventory and erosion rate could be represented by the average value of the middle slope position. The highest value of annual erosion modulus reached to 4917.06 tkm− 2a− 1 on the upper site of the sloping lands. While the annual erosion modulus were synchronous reduced from the upper to the lower of the slope landscape, the erosion rate had the similar trends. Meanwhile, K values of soil erodibility changed from 0.0338 thm2h (hm− 2MJ− 1mm− 1) to 0.0375 thm2h (hm− 2MJ− 1mm− 1) along the slope length. There existed the logarithmic relationship between K value and 137Cs inventory in the correspondent slope position. Therefore, it is useful to study spatial patterns of soil erosion in different slope positions with the different height of lynchet of terracing hedgerows of the whole sloping landscape. Also it is important for implementing soil conservation strategy in the remote mountain region, China.

Evaluation of Different Machine Learning Models for Predicting Soil Erosion in Tropical Sloping Lands of Northeast Vietnam

Soil erosion induced by rainfall under prevailing conditions is a prominent problem to farmers in tropical sloping lands of Northeast Vietnam. This study evaluates possibility of predicting erosion status by machine learning models, including fuzzy k-nearest neighbor (FKNN), artificial neural network (ANN), support vector machine (SVM), least squares support vector machine (LSSVM), and relevance vector machine (RVM). Model evaluation employed a historical dataset consisting of ten explanatory variables and soil erosion featured four different land use managements on hillslopes in Northwest Vietnam. All 236 data samples representing soil erosion/nonerosion events were randomly prepared (80% for training and 20% for testing) to assess the robustness of the five models. This subsampling process was repeatedly carried out by 30 rounds to eliminate the issue of randomness in data selection. Classification accuracy rate (CAR) and area under receiver operating characteristic (AUC) were used to evaluate performance of the five models. Significant difference between different algorithms was verified by the Wilcoxon test. Results of the study showed that RVM model achieves the best outcomes in both training (CAR = 92.22% and AUC = 0.98) and testing phases (CAR = 91.94% and AUC = 0.97). Four other learning algorithms also demonstrated good performance as indicated by their CAR values surpassing 80% and AUC values greater than 0.9. Hence, these results strongly confirm the efficacy of applying machine learning models for soil erosion prediction.