Integrating machine learning and geospatial data analysis for comprehensive flood hazard assessment.

Flooding is a major natural hazard worldwide, causing catastrophic damage to communities and infrastructure. Due to climate change exacerbating extreme weather events robust flood hazard modeling is crucial to support disaster resilience and adaptation. This study uses multi-sourced geospatial datasets to develop an advanced machine learning framework for flood hazard assessment in the Arambag region of West Bengal, India.

Assessing river water quality for ecological risk in the context of a decaying river in India.

The decay of rivers and river water pollution are common problems worldwide. However, many works have been performed on decaying rivers in India, and the status of the water quality is still unknown in Jalangi River. To this end, the present study intends to examine the water quality of the Jalangi River to assess ecological status in both the spatial and seasonal dimensions.

Integrating fuzzy-AHP and GIS for solid waste disposal site selection in Kenitra province, NW Morocco.

Selecting an optimal solid waste disposal site is one of the decisive waste management issues because unsuitable sites cause serious environmental and public health problems. In Kenitra province, northwest Morocco, sustainable disposal sites have become a major challenge due to rapid urbanization and population growth. In addition, the existing disposal sites are traditional and inappropriate.

Climate change and future challenges to the sustainable management of the Iraqi marshlands.

The application of restoration plans for the Iraqi marshlands is encountering significant challenges due to water scarcity and the impacts of climate change. This paper assesses the impact of water scarcity on the possibility of continuing the application of restoration and sustainable management plans for the main marshlands in Iraq. This assessment was conducted based on the available data and expected situation of available water resources under climate change conditions until the year 2035.

Improving PM prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm.

Fine particulate matter (PM) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In such a context, accurate prediction of PM concentration is critical for raising public awareness, allowing sensitive populations to plan ahead, and providing governments with information for public health alerts. This study applies a novel hybridization of extreme learning machine (ELM) with a snake optimization algorithm called the ELM-SO model to forecast PM concentrations.

Classification of precipitation types in Poland using machine learning and threshold temperature methods.

The phase in which precipitation falls-rainfall, snowfall, or sleet-has a considerable impact on hydrology and surface runoff. However, many weather stations only provide information on the total amount of precipitation, at other stations series are short or incomplete. To address this issue, data from 40 meteorological stations in Poland spanning the years 1966-2020 were utilized in this study to classify precipitation.

Land cover and crop types mapping using different spatial resolution imagery in a Mediterranean irrigated area.

Crop type identification is critical for agricultural sustainability policy development and environmental assessments. Therefore, it is important to obtain their spatial distribution via different approaches. Medium-, high- and very high-resolution optical satellite sensors are efficient tools for acquiring this information, particularly for challenging studies such as those conducted in heterogeneous agricultural fields.

Predicting potential reforestation areas by Quercus ilex (L.) species using machine learning algorithms: case of upper Ziz, southeastern Morocco.

The selection of appropriate areas for reforestation remains a complex task because of influence by several factors, which requires the use of new techniques. Based on the accurate outcomes obtained through machine learning in prior investigations, the current study evaluates the capacities of six machine learning techniques (MLT) for delineating optimal areas for reforestation purposes specifically targeting Quercus ilex, an important local species to protect soil and water in upper Ziz, southeast Morocco. In the initial phase, the remaining stands of Q.

Groundwater vulnerability to pollution assessment: an application of geospatial techniques and integrated IRN-DEMATEL-ANP decision model.

This study evaluated the susceptibility to groundwater pollution using a modified DRASTIC model. A novel hybrid multi-criteria decision-making (MCDM) model integrating Interval Rough Numbers (IRN), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Analytical Network Process (ANP) was used to investigate the interrelationships between critical hydrogeologic factors (and determine their relative weights) via a novel vulnerability index based on the DRASTIC model. The flexibility of GIS in handling spatial data was employed to delineate thematic map layers of the hydrogeologic factors and to improve the DRASTIC model.

Zoning the suitability of the western Mekong Delta for paddy rice cultivation and aquaculture under current and future environmental conditions.

Ca Mau and Kien Giang, the two provinces of the Mekong Delta bordering the Gulf of Thailand, are facing major environmental challenges affecting the agriculture and aquaculture sectors upon which many livelihoods in this region depend on. This study maps the suitability of these two provinces for paddy rice cultivation and shrimp farming according to soil characteristics and current and future environmental conditions for variables found to significantly influence the yield of those two sectors, i.e.

Region of freshwater influence (ROFI) and its impact on sediment transport in the lower Mekong Delta coastal zone of Vietnam.

The delta of the Mekong River is one of the largest in the world, with the Mekong River carrying a large amount of sediments in its Region of Freshwater Influence (ROFI). This study investigates the flow structure and movement of both suspended and bedload sediments in the ROFI of the Lower Mekong Delta (LMD) in order to identify areas prone to sediment accretion and erosion. This is accomplished by applying the three-dimensional Coastal and Regional Ocean COmmunity (CROCO) model and then calculating the sediment budget of different stretches of the coastline.

A modified approach to quantify aquifer vulnerability to pollution towards sustainable groundwater management in Irrigated Indus Basin.

The quality of groundwater in the study watershed has worsened because of industrial effluents and residential wastes from the urbanized cities; therefore, there is an important need to explore the aquifer vulnerability to pollution for sustainable groundwater management in the Irrigated Indus Basin (IIB). This study proposed a novel methodology to quantify groundwater vulnerability using two fully independent methodologies: the first by reintroducing an improved recharge factor (R) map and the second by incorporating three different weight and rating schemes into a traditional DRASTIC framework to improve the performance of the DRASTIC approach. In the current study, we composed a recharge map from Soil and Water Assessment Tool (SWAT) output (namely SWAT recharge map) with a drainage density map to retrieve an improved composite recharge map (SWAT-CRM).

Integrating water quality index, GIS and multivariate statistical techniques towards a better understanding of drinking water quality.

Groundwater is considered as an imperative component of the accessible water assets across the world. Due to urbanization, industrialization and intensive farming practices, the groundwater resources have been exposed to large-scale depletion and quality degradation. The prime objective of this study was to evaluate the groundwater quality for drinking purposes in Mewat district of Haryana, India.

Prediction of groundwater nitrate concentration in a semiarid region using hybrid Bayesian artificial intelligence approaches.

Nitrate is a major pollutant in groundwater whose main source is municipal wastewater and agricultural activities. In the present study, Bayesian approaches such as Bayesian generalized linear model (BGLM), Bayesian regularized neural network (BRNN), Bayesian additive regression tree (BART), and Bayesian ridge regression (BRR) were used to model groundwater nitrate contamination in a semiarid region Marvdasht watershed, Fars province, Iran. Eleven groundwater (GW) nitrate conditioning factors have been taken as input parameters for predictive modeling.

Developing a new approach for design support of subsurface constructed wetland using machine learning algorithms.

Knowing the effluent quality of treatment systems in advance to enable the design of treatment systems that comply with environmental standards is a realistic strategy. This study aims to develop machine learning - based predictive models for designing the subsurface constructed wetlands (SCW). Data from the SCW literature during the period of 2009-2020 included 618 sets and 10 features.

Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin.

Estimating monthly runoff variation, especially in ungauged basins, is inevitable for water resource planning and management. The present study aimed to evaluate the regionalization methods for determining regional parameters of the rainfall-runoff model (i.e.

Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh.

Wetland risk assessment is a global concern especially in developing countries like Bangladesh. The present study explored the spatiotemporal dynamics of wetlands, prediction of wetland risk assessment. The wetland risk assessment was predicted based on ten selected parameters, such as fragmentation probability, distance to road, and settlement.

Development of an integrated peri-urban wetland degradation assessment approach for the Chatra Wetland in eastern India.

The loss of peri-urban wetlands is a major side effect of urbanization in India in recent days. Timely and proper assessment of wetland area change is essential for the conservation of wetlands. This study follows the integrated way of the peri-urban wetland degradation assessment in the case of medium and small-size urban agglomerations with a special focus on Chatra Wetland.

A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation.

Precise monitoring of cyanobacteria concentration in water resources is a daunting task. The development of reliable tools to monitor this contamination is an important research topic in water resources management. Indirect methods such as chlorophyll-a determination, cell counting, and toxin measurement of the cyanobacteria are tedious, cumbersome, and often lead to inaccurate results.

Development of fuzzy analytic hierarchy process based water quality model of Upper Ganga river basin, India.

The ecological sustainability of rivers is in question due to severe pollution and lack of stringent regulations. Long term (1990-2016) water quality data of five stations namely Haridwar, Bareilly, Kanpur, Prayagraj and Varanasi of Upper Ganga river, India was considered for analysis using fuzzy analytical process (FAHP) based water quality index (WQI) to assess surface water quality. The value of water physical, biological and chemical parameters of temporal resolution (monthly, seasonal and yearly) indicate that value of electrical conductivity (EC), total dissolved solids (TDS), biological oxygen demand (BOD), chemical oxygen demand (COD), total alkalinity (Mg CaCO), total hardness (Mg CaCO), calcium (Ca), magnesium (Mg), sodium (Na), chlorine (Cl) and bicarbonate (HCO) were observed very high compared to recommended value of Bureau of Indian Standards (BIS) and World Health Organization (WHO) at Kanpur, Prayagraj and Varanasi stations.

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.

The optimal alternative for quantifying reference evapotranspiration in climatic sub-regions of Bangladesh.

Reference evapotranspiration (ET) is a basic element for hydrological designing and agricultural water resources management. The FAO56 recommended Penman-Monteith (FAO56-PM) formula recognized worldwide as the robust and standard model for calculating ET. However, the use of the FAO56-PM model is restricted in some data-scarce regions like Bangladesh.