Kvantificiranje stope erozije vodenog tla korištenjem pristupa RUSLE, GIS i RS za sliv rijeke Al-Qshish, Latakija, Sirija

Soil erosion is one of the most prominent geomorphological hazards threatening environmental sustainability in the coastal region of western Syria. The current war conditions in Syria has led to a lack of field data and measurements related to assessing soil erosion. Mapping the spatial distribution of potential soil erosion is a basic step in implementing soil preservation procedures mainly in the river catchments. The present paper aims to conduct a comprehensive assessment of soil erosion severity using revised universal soil loss equation (RUSLE) and remote sensing (RS) data in geographic information system (GIS) environment across the whole Al-Qshish river basin. Quantitatively, the annual rate of soil erosion in the study basin was 81.1 t ha−1 year−1 with a spatial average reaching 55.2 t ha−1 year−1. Spatially, the soil erosion risk map was produced with classification into five susceptible-zones: very low (41 %), low (40.5%), moderate (8.9%), high (5.4%) and very high (4.2%). The current study presented a reliable assessment of soil loss rates and classification of erosion-susceptible areas within the study basin. These outputs can be relied upon to create measures for maintaining areas with high and very high soil erosion susceptibility under the current war conditions.Erozija tla jedna je od najistaknutijih geomorfoloških opasnosti koja prijeti održivosti okoliša u obalnoj regiji zapadne Sirije. Trenutni ratni uvjeti u Siriji doveli su do nedostatka terenskih podataka i mjerenja vezanih za procjenu erozije tla. Kartiranje prostorne distribucije potencijalne erozije tla osnovni je korak u provedbi postupaka očuvanja tla uglavnom u riječnim slivovima. Ovaj rad ima za cilj provesti sveobuhvatnu procjenu ozbiljnosti erozije tla korištenjem revidirane univerzalne jednadžbe gubitka tla (RUSLE) i podataka daljinske detekcije (RS) u okolišu geografskog informacijskog sustava (GIS) u cijelom slivu rijeke Al-Qshish. Kvantitativno gledano, godišnja stopa erozije tla u istraživanom bazenu iznosila je 81,1 t ha−1 godina−1 s prostornim prosjekom od 55,2 t ha−1 godina−1. Prostorno, izrađena je karta rizika od erozije tla s razvrstavanjem u pet osjetljivih zona: vrlo niska (41 %), niska (40,5 %), umjerena (8,9 %), visoka (5,4 %) i vrlo visoka (4,2 %). Sadašnja studija dala je pouzdanu procjenu stopa gubitka tla i klasifikaciju područja osjetljivih na eroziju unutar istraživanog bazena. Na te se rezultate može osloniti za stvaranje mjera za održavanje područja s visokom i vrlo visokom osjetljivošću tla na eroziju u trenutnim ratnim uvjetima

Spatial Analysis of Flood Hazard Zoning Map Using Novel Hybrid Machine Learning Technique in Assam, India

Twenty-two flood-causative factors were nominated based on morphometric, hydrological, soil permeability, terrain distribution, and anthropogenic inferences and further analyzed through the novel hybrid machine learning approach of random forest, support vector machine, gradient boosting, naïve Bayes, and decision tree machine learning (ML) models. A total of 400 flood and nonflood locations acted as target variables of the flood hazard zoning map. All operative factors in this study were tested using variance inflation factor (VIF) values (<5.0) and Boruta feature ranking (<10 ranks) for FHZ maps. The hybrid model along with RF and GBM had sound flood hazard zoning maps for the study area. The area under the receiver operating characteristics (AUROC) curve and statistical model matrices such as accuracy, precision, recall, F1 score, and gain and lift curve were applied to assess model performance. The 70%:30% sample ratio for training and validation of the standalone models concerning the AUROC value showed sound results for all the ML models, such as RF (97%), SVM (91%), GBM (97%), NB (96%), DT (88%), and hybrid (97%). The gain and lift curve also showed the suitability of the hybrid model along with the RF, GBM, and NB models for developing FHZ maps

Spatial Analysis of Flood Hazard Zoning Map Using Novel Hybrid Machine Learning Technique in Assam, India

Twenty-two flood-causative factors were nominated based on morphometric, hydrological, soil permeability, terrain distribution, and anthropogenic inferences and further analyzed through the novel hybrid machine learning approach of random forest, support vector machine, gradient boosting, naïve Bayes, and decision tree machine learning (ML) models. A total of 400 flood and nonflood locations acted as target variables of the flood hazard zoning map. All operative factors in this study were tested using variance inflation factor (VIF) values (<5.0) and Boruta feature ranking (<10 ranks) for FHZ maps. The hybrid model along with RF and GBM had sound flood hazard zoning maps for the study area. The area under the receiver operating characteristics (AUROC) curve and statistical model matrices such as accuracy, precision, recall, F1 score, and gain and lift curve were applied to assess model performance. The 70%:30% sample ratio for training and validation of the standalone models concerning the AUROC value showed sound results for all the ML models, such as RF (97%), SVM (91%), GBM (97%), NB (96%), DT (88%), and hybrid (97%). The gain and lift curve also showed the suitability of the hybrid model along with the RF, GBM, and NB models for developing FHZ maps