{"product_id":"geomorphic-risk-reduction-using-geospatial-methods-and-tools-9789819977062","title":"Geomorphic Risk Reduction Using Geospatial Methods and Tools","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eLandslide Susceptibility Assessment Based on Machine Learning Techniques.- Measuring landslide susceptibility in Jakholi region of Garhwal Himalaya applying novel ensembles of statistical and machine learning algorithms.- Landslide Susceptibility Mapping using GIS-based Frequency Ratio, Shannon Entropy, Information Value and Weight-of-Evidence approaches in part of Kullu district, Himachal Pradesh, India.- An advanced hybrid machine learning technique for assessing the susceptibility to landslides in the Meenachil river basin of Kerala, India.- Novel ensemble of M5P and Deep learning neural network for predicting landslide susceptibility: A cross-validation approach.- Artificial neural network ensemble with General linear model for modeling the Landslide Susceptibility in Mirik region of West Bengal, India.- Modeling gully erosion susceptibility using advanced machine learning method in Pathro River Basin, India.- Quantitative Assessment of Interferometric Synthetic Aperture 2 Radar(INSAR) for Landslide Monitoring and Mitigation.- Assessment of Landslide Vulnerability using Statistical and Machine Learning Methods in Bageshwar District of Uttarakhand, India.- Assessing the shifting of the River Ganga along Malda District of West Bengal, India.- An ensemble of J48 Decision Tree with AdaBoost, and Bagging for flood susceptibility mapping in the Sundarban of West Bengal, India.- Assessment of mouza level flood resilience in lower part of Mayurakshi River basin, Eastern India.- Spatial flashflood modeling in Beas River Basin of Himachal Pradesh, India using GIS-based machine learning algorithms.- Geospatial study of river shifting and erosion deposition phenomenon along a selected stretch of River Damodar, West Bengal, India.- An Evaluation of Hydrological Modeling Using CN Method in Ungauged Barsa River Basin of Pasakha, Bhutan.- The Adoption of Random Forest (RF) and Support Vector Machine (SVM) with Cat Swarm Optimization (CSO) to Predict the Soil Liquefaction.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":52091906195799,"sku":"9789819977062","price":116.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789819977062.jpg?v=1762276223","url":"https:\/\/bookcurl.com\/products\/geomorphic-risk-reduction-using-geospatial-methods-and-tools-9789819977062","provider":"Book Curl","version":"1.0","type":"link"}