A review of recent advances and future prospects in calculation of reference evapotranspiration in Bangladesh using soft computing models.

Evapotranspiration (ETo) is a complex and non-linear hydrological process with a significant impact on efficient water resource planning and long-term management. The Penman-Monteith (PM) equation method, developed by the Food and Agriculture Organization of the United Nations (FAO), represents an advancement over earlier approaches for estimating ETo. Eto though reliable, faces limitations due to the requirement for climatological data not always available at specific locations.

Past and future impacts of land-use changes on ecosystem services in Austria.

Environmental and socio-economic developments induce land-use changes with potentially negative impacts on human well-being. To counteract undesired developments, a profound understanding of the complex relationships between drivers, land use, and ecosystem services is needed. Yet, national studies examining extended time periods are still rare.

Flood hazard potential evaluation using decision tree state-of-the-art models.

Floods occur frequently in Romania and throughout the world and are one of the most devastating natural disasters that impact people's lives. Therefore, in order to reduce the potential damages, an accurate identification of surfaces susceptible to flood phenomena is mandatory. In this regard, the quantitative calculation of flood susceptibility has become a very popular practice in the scientific research.

Impacts of soil erosion and climate change on the built heritage of the Pambamarca Fortress Complex in northern Ecuador.

The Pambamarca fortress complex in northern Ecuador is a cultural and built heritage with 18 prehispanic fortresses known as Pucaras. They are mostly located on the ridge of the Pambamarca volcano, which is severely affected by erosion. In this research, we implemented a multiscale methodology to identify sheet, rill and gully erosion in the context of climate change for the prehistoric sites.

Flood vulnerability of a few areas in the foothills of the Western Ghats: a comparison of AHP and F-AHP models.

Flooding is one of the most destructive natural catastrophes that can strike anywhere in the world. With the recent, but frequent catastrophic flood events that occurred in the narrow stretch of land in southern India, sandwiched between the Western Ghats and the Arabian Sea, this study was initiated. The goal of this research is to identify flood-vulnerable zones in this area by making the local self governing bodies as the mapping unit.

Designing grazing susceptibility to land degradation index (GSLDI) in hilly areas.

Evaluation of grazing impacts on land degradation processes is a difficult task due to the heterogeneity and complex interacting factors involved. In this paper, we designed a new methodology based on a predictive index of grazing susceptibility to land degradation index (GSLDI) built on artificial intelligence to assess land degradation susceptibility in areas affected by small ruminants (SRs) of sheep and goats grazing. The data for model training, validation, and testing consisted of sampling points (erosion and no-erosion) taken from aerial imagery.

The coupled socio-ecohydrological evolution of river systems: Towards an integrative perspective of river systems in the 21st century.

River systems have undergone a massive transformation since the Anthropocene. The natural properties of river systems have been drastically altered and reshaped, limiting the use of management frameworks, their scientific knowledge base and their ability to provide adequate solutions for current problems and those of the future, such as climate change, biodiversity crisis and increased demands for water resources. To address these challenges, a socioecologically driven research agenda for river systems that complements current approaches is needed and proposed.

Flood susceptibility mapping by integrating frequency ratio and index of entropy with multilayer perceptron and classification and regression tree.

Episodes of frequent flooding continue to increase, often causing serious damage and tools to identify areas affected by such disasters have become indispensable in today's society. Using the latest techniques can make very accurate flood predictions. In this study, we introduce four effective methods to evaluate the flood susceptibility of Poyang County, in China, by integrating two independent models of frequency ratio and index of entropy with multilayer perceptron and classification and regression tree models.

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors.

There is an evident increase in the importance that remote sensing sensors play in the monitoring and evaluation of natural hazards susceptibility and risk. The present study aims to assess the flash-flood potential values, in a small catchment from Romania, using information provided remote sensing sensors and Geographic Informational Systems (GIS) databases which were involved as input data into a number of four ensemble models. In a first phase, with the help of high-resolution satellite images from the Google Earth application, 481 points affected by torrential processes were acquired, another 481 points being randomly positioned in areas without torrential processes.

Identification of areas prone to flash-flood phenomena using multiple-criteria decision-making, bivariate statistics, machine learning and their ensembles.

Taking into account the exponential growth of the number of flash-floods events worldwide, the detection of areas prone to these natural hazards is one of the main activities taken in order to mitigate the negative effects of these risk phenomena. In the present paper, new modeling approaches, Alternating Decision Tree (ADT) integrated with IOE (ADT-IOE) and ADT integrated with AHP (ADT-AHP), were proposed for flash-flood susceptibility mapping across the Suha river catchment (Romania). Besides, two stand-alone methods, Index of Entropy (IOE) and Analytical Hierarchy Process (AHP), were also investigated.

Comparative assessment of the flash-flood potential within small mountain catchments using bivariate statistics and their novel hybrid integration with machine learning models.

The present study is carried out in the context of the continuous increase, worldwide, of the number of flash-floods phenomena. Also, there is an evident increase of the size of the damages caused by these hazards. Bâsca Chiojdului River Basin is one of the most affected areas in Romania by flash-flood phenomena.

A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area.

This research proposes and evaluates a new approach for flash flood susceptibility mapping based on Deep Learning Neural Network (DLNN)) algorithm, with a case study at a high-frequency tropical storm area in the northwest mountainous region of Vietnam. Accordingly, a DLNN structure with 192 neurons in 3 hidden layers was proposed to construct an inference model that predicts different levels of susceptibility to flash flood. The Rectified Linear Unit (ReLU) and the sigmoid were selected as the activate function and the transfer function, respectively, whereas the Adaptive moment estimation (Adam) was used to update and optimize the weights of the DLNN.

Spatial prediction of flood potential using new ensembles of bivariate statistics and artificial intelligence: A case study at the Putna river catchment of Romania.

Flash-flood is considered to be one of the most destructive natural hazards in the world, which is difficult to accurately model and predict. The objective of the present research is to propose new ensembles of bivariate statistics and artificial intelligences and to introduce a comprehensive methodology for predicting flood susceptibility. The Putna river catchment of Romania is selected as a case study.

Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm.

Flooding is one of the most significant environmental challenges and can easily cause fatal incidents and economic losses. Flood reduction is costly and time-consuming task; so it is necessary to accurately detect flood susceptible areas. This work presents an effective flood susceptibility mapping framework by involving an adaptive neuro-fuzzy inference system (ANFIS) with two metaheuristic methods of biogeography based optimization (BBO) and imperialistic competitive algorithm (ICA).

Flash-Flood Potential assessment in the upper and middle sector of Prahova river catchment (Romania). A comparative approach between four hybrid models.

An accurate assessment of Flash-Flood Potential for certain areas is mandatory for the improvement of flash-flood forecast and warnings. The main aim of the present study is represented by the calculation of Flash-Flood Potential Index within the upper and the middle sector of Prahova river catchment (Romania) by using 4 hybrid models: Logistic Regression-Frequency Ratio (LR-FR) model, Logistic Regression-Weights of Evidence (LR-WoE) model, Support Vector Machine-Frequency Ratio (SVM-FR) model and Support Vector Machine-Weights of Evidence (SVM-WoE). The identification of areas affected by torrential phenomena represents the first step performed in the present research.

Vanishing river ice cover in the lower part of the Danube basin - signs of a changing climate.

Many of the world's largest rivers in the extra tropics are covered with ice during the cold season, and in the Northern Hemisphere approximately 60% of the rivers experience significant seasonal effects of river ice. Here we present an observational data set of the ice cover regime for the lower part of the Danube River which spans over the period 1837-2016, and its the longest one on record over this area. The results in this study emphasize the strong impact of climate change on the occurrence of ice regime especially in the second part of the 20 century.

Using a multi-criteria analysis to identify rivers with hydromorphological restoration priority: Braided rivers in the south-eastern Subcarpathians (Romania).

In order to systematically plan river restoration actions at a regional scale, this paper develops a multi-criteria analysis that classifies rivers, based on their priority for hydromorphological restoration. This priority is defined by severe human pressures within the erodible corridor of the river, drastic alteration of the stream channel, and low intensity of river pattern functioning. Based on relevant indicators for three groups of features (human pressures, channel changes, and river functionality), a Hydromorphological Restoration Priority Index (HRPI) was designed.

Chemical modeling of groundwater in the Banat Plain, southwestern Romania, with elevated As content and co-occurring species by combining diagrams and unsupervised multivariate statistical approaches.

The study proposes a combined model based on diagrams (Gibbs, Piper, Stuyfzand Hydrogeochemical Classification System) and unsupervised statistical approaches (Cluster Analysis, Principal Component Analysis, Fuzzy Principal Component Analysis, Fuzzy Hierarchical Cross-Clustering) to describe natural enrichment of inorganic arsenic and co-occurring species in groundwater in the Banat Plain, southwestern Romania. Speciation of inorganic As (arsenite, arsenate), ion concentrations (Na, K, Ca, Mg, HCO, Cl, F, SO, PO, NO), pH, redox potential, conductivity and total dissolved substances were performed. Classical diagrams provided the hydrochemical characterization, while statistical approaches were helpful to establish (i) the mechanism of naturally occurring of As and F species and the anthropogenic one for NO, SO, PO and K and (ii) classification of groundwater based on content of arsenic species.